[ { "path": "Advanced - CTEs.sql", "content": "-- Using Common Table Expressions (CTE)\n-- A CTE allows you to define a subquery block that can be referenced within the main query. \n-- It is particularly useful for recursive queries or queries that require referencing a higher level\n-- this is something we will look at in the next lesson/\n\n-- Let's take a look at the basics of writing a CTE:\n\n\n-- First, CTEs start using a \"With\" Keyword. Now we get to name this CTE anything we want\n-- Then we say as and within the parenthesis we build our subquery/table we want\nWITH CTE_Example AS \n(\nSELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary), AVG(salary)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nGROUP BY gender\n)\n-- directly after using it we can query the CTE\nSELECT *\nFROM CTE_Example;\n\n\n-- Now if I come down here, it won't work because it's not using the same syntax\nSELECT *\nFROM CTE_Example;\n\n\n\n-- Now we can use the columns within this CTE to do calculations on this data that\n-- we couldn't have done without it.\n\nWITH CTE_Example AS \n(\nSELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nGROUP BY gender\n)\n-- notice here I have to use back ticks to specify the table names - without them it doesn't work\nSELECT gender, ROUND(AVG(`SUM(salary)`/`COUNT(salary)`),2)\nFROM CTE_Example\nGROUP BY gender;\n\n\n\n-- we also have the ability to create multiple CTEs with just one With Expression\n\nWITH CTE_Example AS \n(\nSELECT employee_id, gender, birth_date\nFROM employee_demographics dem\nWHERE birth_date > '1985-01-01'\n), -- just have to separate by using a comma\nCTE_Example2 AS \n(\nSELECT employee_id, salary\nFROM parks_and_recreation.employee_salary\nWHERE salary >= 50000\n)\n-- Now if we change this a bit, we can join these two CTEs together\nSELECT *\nFROM CTE_Example cte1\nLEFT JOIN CTE_Example2 cte2\n\tON cte1. employee_id = cte2. employee_id;\n\n\n-- the last thing I wanted to show you is that we can actually make our life easier by renaming the columns in the CTE\n-- let's take our very first CTE we made. We had to use tick marks because of the column names\n\n-- we can rename them like this\nWITH CTE_Example (gender, sum_salary, min_salary, max_salary, count_salary) AS \n(\nSELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nGROUP BY gender\n)\n-- notice here I have to use back ticks to specify the table names - without them it doesn't work\nSELECT gender, ROUND(AVG(sum_salary/count_salary),2)\nFROM CTE_Example\nGROUP BY gender;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Advanced - Stored Procedures.sql", "content": "-- So let's look at how we can create a stored procedure\n\n-- First let's just write a super simple query\nSELECT *\nFROM employee_salary\nWHERE salary >= 60000;\n\n-- Now let's put this into a stored procedure.\nCREATE PROCEDURE large_salaries()\nSELECT *\nFROM employee_salary\nWHERE salary >= 60000;\n\n-- Now if we run this it will work and create the stored procedure\n-- we can click refresh and see that it is there\n\n-- notice it did not give us an output, that's because we \n\n-- If we want to call it and use it we can call it by saying:\nCALL large_salaries();\n\n-- as you can see it ran the query inside the stored procedure we created\n\n\n-- Now how we have written is not actually best practice.alter\n-- Usually when writing a stored procedure you don't have a simple query like that. It's usually more complex\n\n-- if we tried to add another query to this stored procedure it wouldn't work. It's a separate query:\nCREATE PROCEDURE large_salaries2()\nSELECT *\nFROM employee_salary\nWHERE salary >= 60000;\nSELECT *\nFROM employee_salary\nWHERE salary >= 50000;\n\n\n-- Best practice is to use a delimiter and a Begin and End to really control what's in the stored procedure\n-- let's see how we can do this.\n-- the delimiter is what separates the queries by default, we can change this to something like two $$\n-- in my career this is what I've seen a lot of people who work in SQL use so I've picked it up as well\n\n-- When we change this delimiter it now reads in everything as one whole unit or query instead of stopping\n-- after the first semi colon\nDELIMITER $$\nCREATE PROCEDURE large_salaries2()\nBEGIN\n\tSELECT *\n\tFROM employee_salary\n\tWHERE salary >= 60000;\n\tSELECT *\n\tFROM employee_salary\n\tWHERE salary >= 50000;\nEND $$\n\n-- now we change the delimiter back after we use it to make it default again\nDELIMITER ;\n\n-- let's refresh to see the SP\n-- now we can run this stored procedure\nCALL large_salaries2();\n\n-- as you can see we have 2 outputs which are the 2 queries we had in our stored procedure\n\n\n\n-- we can also create a stored procedure by right clicking on Stored Procedures and creating one:\n\n-- it's going to drop the procedure if it already exists.\nUSE `parks_and_recreation`;\nDROP procedure IF EXISTS `large_salaries3`;\n-- it automatically adds the dilimiter for us\nDELIMITER $$\nCREATE PROCEDURE large_salaries3()\nBEGIN\n\tSELECT *\n\tFROM employee_salary\n\tWHERE salary >= 60000;\n\tSELECT *\n\tFROM employee_salary\n\tWHERE salary >= 50000;\nEND $$\n\nDELIMITER ;\n\n-- and changes it back at the end\n\n-- this can be a genuinely good option to help you write your Stored Procedures faster, although either way\n-- works\n\n-- if we click finish you can see it is created the same and if we run it\n\nCALL large_order_totals3();\n\n-- we get our results\n\n\n\n-- -------------------------------------------------------------------------\n\n-- we can also add parameters\nUSE `parks_and_recreation`;\nDROP procedure IF EXISTS `large_salaries3`;\n-- it automatically adds the dilimiter for us\nDELIMITER $$\nCREATE PROCEDURE large_salaries3(employee_id_param INT)\nBEGIN\n\tSELECT *\n\tFROM employee_salary\n\tWHERE salary >= 60000\n AND employee_id_param = employee_id;\nEND $$\n\nDELIMITER ;\n\n\n\nCALL large_salaries3(1);\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Advanced - Temp Tables.sql", "content": "-- Using Temporary Tables\n-- Temporary tables are tables that are only visible to the session that created them. \n-- They can be used to store intermediate results for complex queries or to manipulate data before inserting it into a permanent table.\n\n-- There's 2 ways to create temp tables:\n-- 1. This is the less commonly used way - which is to build it exactly like a real table and insert data into it\n\nCREATE TEMPORARY TABLE temp_table\n(first_name varchar(50),\nlast_name varchar(50),\nfavorite_movie varchar(100)\n);\n\n-- if we execute this it gets created and we can actualyl query it.\n\nSELECT *\nFROM temp_table;\n-- notice that if we refresh out tables it isn't there. It isn't an actual table. It's just a table in memory.\n\n-- now obviously it's balnk so we would need to insert data into it like this:\n\nINSERT INTO temp_table\nVALUES ('Alex','Freberg','Lord of the Rings: The Twin Towers');\n\n-- now when we run it and execute it again we have our data\nSELECT *\nFROM temp_table;\n\n-- the second way is much faster and my preferred method\n-- 2. Build it by inserting data into it - easier and faster\n\nCREATE TEMPORARY TABLE salary_over_50k\nSELECT *\nFROM employee_salary\nWHERE salary > 50000;\n\n-- if we run this query we get our output\nSELECT *\nFROM temp_table_2;\n\n-- this is the primary way I've used temp tables especially if I'm just querying data and have some complex data I want to put into boxes or these temp tables to use later\n-- it helps me kind of categorize and separate it out\n\n-- In the next lesson we will look at the Temp Tables vs CTEs\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Advanced - Triggers and Events.sql", "content": "-- Triggers\n\n-- a Trigger is a block of code that executes automatically executes when an event takes place in a table.\n\n-- for example we have these 2 tables, invoice and payments - when a client makes a payment we want it to update the invoice field \"total paid\"\n-- to reflect that the client has indeed paid their invoice\n\n\nSELECT * FROM employee_salary;\n\nSELECT * FROM employee_demographics;\n\n-- so really when we get a new row or data is inserted into the payments table we want a trigger to update the correct invoice \n-- with the amount that was paid\n-- so let's write this out\nUSE parks_and_recreation;\nDELIMITER $$\n\nCREATE TRIGGER employee_insert2\n -- we can also do BEFORE, but for this lesson we have to do after\n\tAFTER INSERT ON employee_salary\n -- now this means this trigger gets activated for each row that is inserted. Some sql databses like MSSQL have batch triggers or table level triggers that\n -- only trigger once, but MySQL doesn't have this functionality unfortunately\n FOR EACH ROW\n \n -- now we can write our block of code that we want to run when this is triggered\nBEGIN\n-- we want to update our client invoices table\n-- and set the total paid = total_paid (if they had already made some payments) + NEW.amount_paid\n-- NEW says only from the new rows that were inserted. There is also OLD which is rows that were deleted or updated, but for us we want NEW\n INSERT INTO employee_demographics (employee_id, first_name, last_name) VALUES (NEW.employee_id,NEW.first_name,NEW.last_name);\nEND $$\n\nDELIMITER ; \n\n-- Now let's run it and create it\n\n\n-- Now that it's created let's test it out.\n\n-- Let's insert a payment into the payments table and see if it updates in the Invoice table.\n\n-- so let's put the values that we want to insert - let's pay off this invoice 3 in full\nINSERT INTO employee_salary (employee_id, first_name, last_name, occupation, salary, dept_id)\nVALUES(13, 'Jean-Ralphio', 'Saperstein', 'Entertainment 720 CEO', 1000000, NULL);\n-- now it was updated in the payments table and the trigger was triggered and update the corresponding values in the invoice table\n\nDELETE FROM employee_salary\nWHERE employee_id = 13;\n\n\n\n-- -------------------------------------------------------------------------\n\n-- now let's look at Events\n\n-- Now I usually call these \"Jobs\" because I called them that for years in MSSQL, but in MySQL they're called Events\n\n-- Events are task or block of code that gets executed according to a schedule. These are fantastic for so many reasons. Importing data on a schedule. \n-- Scheduling reports to be exported to files and so many other things\n-- you can schedule all of this to happen every day, every monday, every first of the month at 10am. Really whenever you want\n\n-- This really helps with automation in MySQL\n\n-- let's say Parks and Rec has a policy that anyone over the age of 60 is immediately retired with lifetime pay\n-- All we have to do is delete them from the demographics table\n\nSELECT * \nFROM parks_and_recreation.employee_demographics;\n\nSHOW EVENTS;\n\n-- we can drop or alter these events like this:\nDROP EVENT IF EXISTS delete_retirees;\nDELIMITER $$\nCREATE EVENT delete_retirees\nON SCHEDULE EVERY 30 SECOND\nDO BEGIN\n\tDELETE\n\tFROM parks_and_recreation.employee_demographics\n WHERE age >= 60;\nEND $$\n\n\n-- if we run it again you can see Jerry is now fired -- or I mean retired\nSELECT * \nFROM parks_and_recreation.employee_demographics;\n\n\n\n\n\n\n\n" }, { "path": "Beginner - Group By + Order By.sql", "content": "-- Group By\n-- When you use the GROUP BY clause in a MySQL query, it groups together rows that have the same values in the specified column or columns.\n-- GROUP BY is going to allow us to group rows that have the same data and run aggregate functions on them\n\nSELECT *\nFROM employee_demographics;\n\n-- when you use group by you have to have the same columns you're grouping on in the group by statement\nSELECT gender\nFROM employee_demographics\nGROUP BY gender\n;\n\n\nSELECT first_name\nFROM employee_demographics\nGROUP BY gender\n;\n\n\n\n\n\nSELECT occupation\nFROM employee_salary\nGROUP BY occupation\n;\n\n-- notice there is only one office manager row\n\n-- when we group by 2 columns we now have a row for both occupation and salary because salary is different\nSELECT occupation, salary\nFROM employee_salary\nGROUP BY occupation, salary\n;\n\n-- now the most useful reason we use group by is so we can perform out aggregate functions on them\nSELECT gender, AVG(age)\nFROM employee_demographics\nGROUP BY gender\n;\n\nSELECT gender, MIN(age), MAX(age), COUNT(age),AVG(age)\nFROM employee_demographics\nGROUP BY gender\n;\n\n\n\n#10 - The ORDER BY clause:\n-------------------------\n#The ORDER BY keyword is used to sort the result-set in ascending or descending order.\n\n#The ORDER BY keyword sorts the records in ascending order by default. To sort the records in descending order, use the DESC keyword.\n\n\n#So let's try it out with our customer table\n#First let's start simple with just ordering by one column\nSELECT *\nFROM customers\nORDER BY first_name;\n\n#You can see that first name is ordered from a - z or Ascending.\n\n#We can change that by specifying DESC after it\nSELECT *\nFROM employee_demographics;\n\n-- if we use order by it goes a to z by default (ascending order)\nSELECT *\nFROM employee_demographics\nORDER BY first_name;\n\n-- we can manually change the order by saying desc\nSELECT *\nFROM employee_demographics\nORDER BY first_name DESC;\n\n#Now we can also do multiple columns like this:\n\nSELECT *\nFROM employee_demographics\nORDER BY gender, age;\n\nSELECT *\nFROM employee_demographics\nORDER BY gender DESC, age DESC;\n\n\n\n#now we don't actually have to spell out the column names. We can actually just use their column position\n\n#State is in position 8 and money is in 9, we can use those as well.\nSELECT *\nFROM employee_demographics\nORDER BY 5 DESC, 4 DESC;\n\n#Now best practice is to use the column names as it's more overt and if columns are added or replaced or something in this table it will still use the right columns to order on.\n\n#So that's all there is to order by - fairly straight forward, but something I use for most queries I use in SQL\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Beginner - Having vs Where.sql", "content": "-- Having vs Where\n\n-- Both were created to filter rows of data, but they filter 2 separate things\n-- Where is going to filters rows based off columns of data\n-- Having is going to filter rows based off aggregated columns when grouped\n\nSELECT gender, AVG(age)\nFROM employee_demographics\nGROUP BY gender\n;\n\n\n-- let's try to filter on the avg age using where\n\nSELECT gender, AVG(age)\nFROM employee_demographics\nWHERE AVG(age) > 40\nGROUP BY gender\n;\n-- this doesn't work because of order of operations. On the backend Where comes before the group by. So you can't filter on data that hasn't been grouped yet\n-- this is why Having was created\n\nSELECT gender, AVG(age)\nFROM employee_demographics\nGROUP BY gender\nHAVING AVG(age) > 40\n;\n\nSELECT gender, AVG(age) as AVG_age\nFROM employee_demographics\nGROUP BY gender\nHAVING AVG_age > 40\n;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Beginner - Limit and Aliasing.sql", "content": "-- LIMIT and ALIASING\n\n-- Limit is just going to specify how many rows you want in the output\n\n\nSELECT *\nFROM employee_demographics\nLIMIT 3;\n\n-- if we change something like the order or use a group by it would change the output\n\nSELECT *\nFROM employee_demographics\nORDER BY first_name\nLIMIT 3;\n\n-- now there is an additional paramater in limit which we can access using a comma that specifies the starting place\n\nSELECT *\nFROM employee_demographics\nORDER BY first_name;\n\nSELECT *\nFROM employee_demographics\nORDER BY first_name\nLIMIT 3,2;\n\n-- this now says start at position 3 and take 2 rows after that\n-- this is not used a lot in my opinion\n\n-- you could us it if you wanted to select the third oldest person by doing this:\nSELECT *\nFROM employee_demographics\nORDER BY age desc;\n-- we can see it's Donna - let's try to select her\nSELECT *\nFROM employee_demographics\nORDER BY age desc\nLIMIT 2,1;\n\n\n-- ALIASING\n\n-- aliasing is just a way to change the name of the column (for the most part)\n-- it can also be used in joins, but we will look at that in the intermediate series\n\n\nSELECT gender, AVG(age)\nFROM employee_demographics\nGROUP BY gender\n;\n-- we can use the keyword AS to specify we are using an Alias\nSELECT gender, AVG(age) AS Avg_age\nFROM employee_demographics\nGROUP BY gender\n;\n\n-- although we don't actually need it, but it's more explicit which I usually like\nSELECT gender, AVG(age) Avg_age\nFROM employee_demographics\nGROUP BY gender\n;\n\n\n\n\n\n\n\n\n\n" }, { "path": "Beginner - Parks_and_Rec_Create_db.sql", "content": "DROP DATABASE IF EXISTS `Parks_and_Recreation`;\nCREATE DATABASE `Parks_and_Recreation`;\nUSE `Parks_and_Recreation`;\n\n\n\n\n\n\nCREATE TABLE employee_demographics (\n employee_id INT NOT NULL,\n first_name VARCHAR(50),\n last_name VARCHAR(50),\n age INT,\n gender VARCHAR(10),\n birth_date DATE,\n PRIMARY KEY (employee_id)\n);\n\nCREATE TABLE employee_salary (\n employee_id INT NOT NULL,\n first_name VARCHAR(50) NOT NULL,\n last_name VARCHAR(50) NOT NULL,\n occupation VARCHAR(50),\n salary INT,\n dept_id INT\n);\n\n\nINSERT INTO employee_demographics (employee_id, first_name, last_name, age, gender, birth_date)\nVALUES\n(1,'Leslie', 'Knope', 44, 'Female','1979-09-25'),\n(3,'Tom', 'Haverford', 36, 'Male', '1987-03-04'),\n(4, 'April', 'Ludgate', 29, 'Female', '1994-03-27'),\n(5, 'Jerry', 'Gergich', 61, 'Male', '1962-08-28'),\n(6, 'Donna', 'Meagle', 46, 'Female', '1977-07-30'),\n(7, 'Ann', 'Perkins', 35, 'Female', '1988-12-01'),\n(8, 'Chris', 'Traeger', 43, 'Male', '1980-11-11'),\n(9, 'Ben', 'Wyatt', 38, 'Male', '1985-07-26'),\n(10, 'Andy', 'Dwyer', 34, 'Male', '1989-03-25'),\n(11, 'Mark', 'Brendanawicz', 40, 'Male', '1983-06-14'),\n(12, 'Craig', 'Middlebrooks', 37, 'Male', '1986-07-27');\n\n\nINSERT INTO employee_salary (employee_id, first_name, last_name, occupation, salary, dept_id)\nVALUES\n(1, 'Leslie', 'Knope', 'Deputy Director of Parks and Recreation', 75000,1),\n(2, 'Ron', 'Swanson', 'Director of Parks and Recreation', 70000,1),\n(3, 'Tom', 'Haverford', 'Entrepreneur', 50000,1),\n(4, 'April', 'Ludgate', 'Assistant to the Director of Parks and Recreation', 25000,1),\n(5, 'Jerry', 'Gergich', 'Office Manager', 50000,1),\n(6, 'Donna', 'Meagle', 'Office Manager', 60000,1),\n(7, 'Ann', 'Perkins', 'Nurse', 55000,4),\n(8, 'Chris', 'Traeger', 'City Manager', 90000,3),\n(9, 'Ben', 'Wyatt', 'State Auditor', 70000,6),\n(10, 'Andy', 'Dwyer', 'Shoe Shiner and Musician', 20000, NULL),\n(11, 'Mark', 'Brendanawicz', 'City Planner', 57000, 3),\n(12, 'Craig', 'Middlebrooks', 'Parks Director', 65000,1);\n\n\n\nCREATE TABLE parks_departments (\n department_id INT NOT NULL AUTO_INCREMENT,\n department_name varchar(50) NOT NULL,\n PRIMARY KEY (department_id)\n);\n\nINSERT INTO parks_departments (department_name)\nVALUES\n('Parks and Recreation'),\n('Animal Control'),\n('Public Works'),\n('Healthcare'),\n('Library'),\n('Finance');\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Beginner - Select Statement.sql", "content": "-- SELECT STATEMENET\n\n-- the SELECT statement is used to work with columns and specify what columns you want to work see in your output. There are a few other things as well that\n-- we will discuss throughout this video\n\n#We can also select a specefic number of column based on our requirement. \n\n#Now remember we can just select everything by saying:\nSELECT * \nFROM parks_and_recreation.employee_demographics;\n\n\n#Let's try selecting a specific column\nSELECT first_name\nFROM employee_demographics;\n\n#As you can see from the output, we only have the one column here now and don't see the others\n\n#Now let's add some more columns, we just need to separate the columns with columns\nSELECT first_name, last_name\nFROM employee_demographics;\n\n#Now the order doesn't normall matter when selecting your columns.\n#There are some use cases we will look at in later modules where the order of the column\n#Names in the select statement will matter, but for this you can put them in any order\n\nSELECT last_name, first_name, gender, age\nFROM employee_demographics;\n\n#You'll also often see SQL queries formatted like this.\nSELECT last_name, \nfirst_name, \ngender, \nage\nFROM employee_demographics;\n\n#The query still runs the exact same, but it is easier to read and pick out the columns\n#being selected and what you're doing with them.\n\n#For example let's take a look at using a calculation in the select statement\n\n#You can see here we have the total_money_spent - we can perform calculations on this\nSELECT first_name,\n last_name,\n total_money_spent,\n total_money_spent + 100\nFROM customers;\n\n#See how it's pretty easy to read and to see which columns we are using.\n\n#Math in SQL does follow PEMDAS which stands for Parenthesis, Exponent, Multiplication,\n#Division, Addition, subtraction - it's the order of operation for math\n\n#For example - What will the output be?:\nSELECT first_name, \nlast_name,\nsalary,\nsalary + 100\nFROM employee_salary;\n#This is going to do 10* 100 which is 1000 and then adds the original 540\n\n#Now what will the output be when we do this?\nSELECT first_name, \nlast_name,\nsalary,\n(salary + 100) * 10\nFROM employee_salary;\n\n\n# Pemdas\n\n#One thing I wanted to show you about the select statement in this lesson is the DISTINCT Statement - this will return only unique values in\n#The output - and you won't have any duplicates\n\nSELECT department_id\nFROM employee_salary;\n\nSELECT DISTINCT department_id\nFROM employee_salary;\n\n#Now a lot happens in the select statement. We have an entire module dedicated to just the \n#select statement so this is kind of just an introduction to the select statement." }, { "path": "Beginner - Where Statement.sql", "content": "#WHERE Clause:\n#-------------\n#The WHERE clause is used to filter records (rows of data)\n\n#It's going to extract only those records that fulfill a specified condition.\n\n# So basically if we say \"Where name is = 'Alex' - only rows were the name = 'Alex' will return\n# So this is only effecting the rows, not the columns\n\n\n#Let's take a look at how this looks\nSELECT *\nFROM employee_salary\nWHERE salary > 50000;\n\nSELECT *\nFROM employee_salary\nWHERE salary >= 50000;\n\nSELECT *\nFROM employee_demographics\nWHERE gender = 'Female';\n\n\n#We can also return rows that do have not \"Scranton\"\nSELECT *\nFROM employee_demographics\nWHERE gender != 'Female';\n\n\n#We can use WHERE clause with date value also\nSELECT *\nFROM employee_demographics\nWHERE birth_date > '1985-01-01';\n\n-- Here '1990-01-01' is the default data formate in MySQL.\n-- There are other date formats as well that we will talk about in a later lesson.\n\n\n# LIKE STATEMENT\n\n-- two special characters a % and a _\n\n-- % means anything\nSELECT *\nFROM employee_demographics\nWHERE first_name LIKE 'a%';\n\n-- _ means a specific value\nSELECT *\nFROM employee_demographics\nWHERE first_name LIKE 'a__';\n\n\nSELECT *\nFROM employee_demographics\nWHERE first_name LIKE 'a___%';\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Intermediate - Case Statements.sql", "content": "-- Case Statements\n\n-- A Case Statement allows you to add logic to your Select Statement, sort of like an if else statement in other programming languages or even things like Excel\n\n\n\nSELECT * \nFROM employee_demographics;\n\n\nSELECT first_name, \nlast_name, \nCASE\n\tWHEN age <= 30 THEN 'Young'\nEND\nFROM employee_demographics;\n\n\n--\n\nSELECT first_name, \nlast_name, \nCASE\n\tWHEN age <= 30 THEN 'Young'\n WHEN age BETWEEN 31 AND 50 THEN 'Old'\n WHEN age >= 50 THEN \"On Death's Door\"\nEND\nFROM employee_demographics;\n\n-- Poor Jerry\n\n-- Now we don't just have to do simple labels like we did, we can also perform calculations\n\n-- Let's look at giving bonuses to employees\n\nSELECT * \nFROM employee_salary;\n\n-- Pawnee Council sent out a memo of their bonus and pay increase structure so we need to follow it\n-- Basically if they make less than 45k then they get a 5% raise - very generous\n-- if they make more than 45k they get a 7% raise\n-- they get a bonus of 10% if they work for the Finance Department\n\nSELECT first_name, last_name, salary,\nCASE\n\tWHEN salary > 45000 THEN salary + (salary * 0.05)\n WHEN salary < 45000 THEN salary + (salary * 0.07)\nEND AS new_salary\nFROM employee_salary;\n\n-- Unfortunately Pawnee Council was extremely specific in their wording and Jerry was not included in the pay increases. Maybe Next Year.\n\n-- Now we need to also account for Bonuses, let's make a new column\nSELECT first_name, last_name, salary,\nCASE\n\tWHEN salary > 45000 THEN salary + (salary * 0.05)\n WHEN salary < 45000 THEN salary + (salary * 0.07)\nEND AS new_salary,\nCASE\n\tWHEN dept_id = 6 THEN salary * .10\nEND AS Bonus\nFROM employee_salary;\n\n-- as you can see Ben is the only one who get's a bonus\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Intermediate - Joins.sql", "content": "-- Joins\n\n-- joins allow you to combine 2 tables together (or more) if they have a common column.\n-- doesn't mean they need the same column name, but the data in it are the same and can be used to join the tables together\n-- there are several joins we will look at today, inner joins, outer joins, and self joins\n\n\n-- here are the first 2 tables - let's see what columns and data in the rows we have in common that we can join on\nSELECT *\nFROM employee_demographics;\n\nSELECT *\nFROM employee_salary;\n\n-- let's start with an inner join -- inner joins return rows that are the same in both columns\n\n-- since we have the same columns we need to specify which table they're coming from\nSELECT *\nFROM employee_demographics\nJOIN employee_salary\n\tON employee_demographics.employee_id = employee_salary.employee_id;\n\n-- notice Ron Swanson isn't in the results? This is because he doesn't have an employee id in the demographics table. He refused to give his birth date or age or gender\n\n-- use aliasing!\nSELECT *\nFROM employee_demographics dem\nINNER JOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id;\n\n\n-- OUTER JOINS\n\n-- for outer joins we have a left and a right join\n-- a left join will take everything from the left table even if there is no match in the join, but will only return matches from the right table\n-- the exact opposite is true for a right join\n\nSELECT *\nFROM employee_salary sal\nLEFT JOIN employee_demographics dem\n\tON dem.employee_id = sal.employee_id;\n\n-- so you'll notice we have everything from the left table or the salary table. Even though there is no match to ron swanson. \n-- Since there is not match on the right table it's just all Nulls\n\n-- if we just switch this to a right join it basically just looks like an inner join\n-- that's because we are taking everything from the demographics table and only matches from the left or salary table. Since they have all the matches\n-- it looks kind of like an inner join\nSELECT *\nFROM employee_salary sal\nRIGHT JOIN employee_demographics dem\n\tON dem.employee_id = sal.employee_id;\n\n\n\n-- Self Join\n\n-- a self join is where you tie a table to itself\n\nSELECT *\nFROM employee_salary;\n\n-- what we could do is a secret santa so the person with the higher ID is the person's secret santa\n\n\nSELECT *\nFROM employee_salary emp1\nJOIN employee_salary emp2\n\tON emp1.employee_id = emp2.employee_id\n ;\n\n-- now let's change it to give them their secret santa\nSELECT *\nFROM employee_salary emp1\nJOIN employee_salary emp2\n\tON emp1.employee_id + 1 = emp2.employee_id\n ;\n\n\n\nSELECT emp1.employee_id as emp_santa, emp1.first_name as santa_first_name, emp1.last_name as santa_last_name, emp2.employee_id, emp2.first_name, emp2.last_name\nFROM employee_salary emp1\nJOIN employee_salary emp2\n\tON emp1.employee_id + 1 = emp2.employee_id\n ;\n\n-- So leslie is Ron's secret santa and so on -- Mark Brandanowitz didn't get a secret santa, but he doesn't deserve one because he broke Ann's heart so it's all good\n\n\n\n\n\n\n-- Joining multiple tables\n\n-- now we have on other table we can join - let's take a look at it\nSELECT * \nFROM parks_and_recreation.parks_departments;\n\n\nSELECT *\nFROM employee_demographics dem\nINNER JOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nJOIN parks_departments dept\n\tON dept.department_id = sal.dept_id;\n\n-- now notice when we did that, since it's an inner join it got rid of andy because he wasn't a part of any department\n\n-- if we do a left join we would still include him because we are taking everything from the left table which is the salary table in this instance\nSELECT *\nFROM employee_demographics dem\nINNER JOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nLEFT JOIN parks_departments dept\n\tON dept.department_id = sal.dept_id;\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Intermediate - String Functions.sql", "content": "#Now let's look at string functions. These help us change and look at strings differently.\n\nSELECT * \nFROM bakery.customers;\n\n\n#Length will give us the length of each value\nSELECT LENGTH('sky');\n\n#Now we can see the length of each name\nSELECT first_name, LENGTH(first_name) \nFROM employee_demographics;\n\n#Upper will change all the string characters to upper case\nSELECT UPPER('sky');\n\nSELECT first_name, UPPER(first_name) \nFROM employee_demographics;\n\n#lower will change all the string characters to lower case\nSELECT LOWER('sky');\n\nSELECT first_name, LOWER(first_name) \nFROM employee_demographics;\n\n#Now if you have values that have white space on the front or end, we can get rid of that white space using TRIM\nSELECT TRIM('sky' );\n\n#Now if we have white space in the middle it doesn't work\nSELECT LTRIM(' I love SQL');\n\n#There's also L trim for trimming just the left side\nSELECT LTRIM(' I love SQL');\n\n\n#There's also R trim for trimming just the Right side\nSELECT RTRIM('I love SQL ');\n\n\n#Now we have Left. Left is going to allow us to take a certain amount of strings from the left hand side.\nSELECT LEFT('Alexander', 4);\n\nSELECT first_name, LEFT(first_name,4) \nFROM employee_demographics;\n\n#Right is basically the opposite - taking it starting from the right side\nSELECT RIGHT('Alexander', 6);\n\nSELECT first_name, RIGHT(first_name,4) \nFROM employee_demographics;\n\n#Now let's look at substring, this one I personally love and use a lot.\n#Substring allows you to specify a starting point and how many characters you want so you can take characters from anywhere in the string. \nSELECT SUBSTRING('Alexander', 2, 3);\n\n#We could use this on phones to get the area code at the beginning.\nSELECT birth_date, SUBSTRING(birth_date,1,4) as birth_year\nFROM employee_demographics;\n\n#We can also use replace\nSELECT REPLACE(first_name,'a','z')\nFROM employee_demographics;\n\n#Next we have locate - we have 2 arguments we can use here: we can specify what we are searching for and where to search\n#It will return the position of that character in the string.\nSELECT LOCATE('x', 'Alexander');\n\n#Now Alexander has 2 e's - what will happen if we try to locate it\nSELECT LOCATE('e', 'Alexander');\n#It will return the location of just the first position.\n\n#Let's try it on our first name\nSELECT first_name, LOCATE('a',first_name) \nFROM employee_demographics;\n\n#You can also locate longer strings\nSELECT first_name, LOCATE('Mic',first_name) \nFROM employee_demographics;\n\n#Now let's look at concatenate - it will combine the strings together\nSELECT CONCAT('Alex', 'Freberg');\n\n#Here we can combine the first and the last name columns together\nSELECT CONCAT(first_name, ' ', last_name) AS full_name\nFROM employee_demographics;\n\n" }, { "path": "Intermediate - Subqueries.sql", "content": "# Subqueries\n\n#So subqueries are queries within queries. Let's see how this looks.\n\nSELECT *\nFROM employee_demographics;\n\n\n#Now let's say we wanted to look at employees who actually work in the Parks and Rec Department, we could join tables together or we could use a subquery\n#We can do that like this:\n\nSELECT *\nFROM employee_demographics\nWHERE employee_id IN \n\t\t\t(SELECT employee_id\n\t\t\t\tFROM employee_salary\n WHERE dept_id = 1);\n \n#So we are using that subquery in the where statement and if we just highlight the subwuery and run it it's basically a list we are selecting from in the outer query\n\nSELECT *\nFROM employee_demographics\nWHERE employee_id IN \n\t\t\t(SELECT employee_id, salary\n\t\t\t\tFROM employee_salary\n WHERE dept_id = 1);\n\n# now if we try to have more than 1 column in the subquery we get an error saying the operand should contain 1 column only \n\n#We can also use subqueries in the select and the from statements - let's see how we can do this\n\n-- Let's say we want to look at the salaries and compare them to the average salary\n\nSELECT first_name, salary, AVG(salary)\nFROM employee_salary;\n-- if we run this it's not going to work, we are using columns with an aggregate function so we need to use group by\n-- if we do that though we don't exactly get what we want\nSELECT first_name, salary, AVG(salary)\nFROM employee_salary\nGROUP BY first_name, salary;\n\n-- it's giving us the average PER GROUP which we don't want\n-- here's a good use for a subquery\n\nSELECT first_name, \nsalary, \n(SELECT AVG(salary) \n\tFROM employee_salary)\nFROM employee_salary;\n\n\n-- We can also use it in the FROM Statement\n-- when we use it here it's almost like we are creating a small table we are querying off of\nSELECT *\nFROM (SELECT gender, MIN(age), MAX(age), COUNT(age),AVG(age)\nFROM employee_demographics\nGROUP BY gender) \n;\n-- now this doesn't work because we get an error saying we have to name it\n\nSELECT gender, AVG(Min_age)\nFROM (SELECT gender, MIN(age) Min_age, MAX(age) Max_age, COUNT(age) Count_age ,AVG(age) Avg_age\nFROM employee_demographics\nGROUP BY gender) AS Agg_Table\nGROUP BY gender\n;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Intermediate - Unions.sql", "content": "#UNIONS\n\n\n#A union is how you can combine rows together- not columns like we have been doing with joins where one column is put next to another\n#Joins allow you to combine the rows of data\n\n#Now you should keep it the same kind of data otherwise if you start mixing tips with first_names it would be really confusing, but you can do it.\n#Let's try it out and use Union to bring together some random data, then we will look at an actual use case\n\nSELECT first_name, last_name\nFROM employee_demographics\nUNION\nSELECT occupation, salary\nFROM employee_salary;\n\n#So you can see we basically combined the data together, but not side by side in different columns, but one on top of the other in the same columns\n#This obviously is not good since you're mixing data, but it can be done if you want.\n\nSELECT first_name, last_name\nFROM employee_demographics\nUNION\nSELECT first_name, last_name\nFROM employee_salary;\n\n-- notice it gets rid of duplicates? Union is actually shorthand for Union Distinct\n\nSELECT first_name, last_name\nFROM employee_demographics\nUNION DISTINCT\nSELECT first_name, last_name\nFROM employee_salary;\n\n-- we can use UNION ALL to show all values\n\nSELECT first_name, last_name\nFROM employee_demographics\nUNION ALL\nSELECT first_name, last_name\nFROM employee_salary;\n\n\n\n#Now Let's actually try to use UNION\n# The Parks department is trying to cut their budget and wants to identify older employees they can push out or high paid employees who they can reduce pay or push out\n-- let's create some queries to help with this\n\nSELECT first_name, last_name, 'Old'\nFROM employee_demographics\nWHERE age > 50;\n\n\n\nSELECT first_name, last_name, 'Old Lady' as Label\nFROM employee_demographics\nWHERE age > 40 AND gender = 'Female'\nUNION\nSELECT first_name, last_name, 'Old Man'\nFROM employee_demographics\nWHERE age > 40 AND gender = 'Male'\nUNION\nSELECT first_name, last_name, 'Highly Paid Employee'\nFROM employee_salary\nWHERE salary >= 70000\nORDER BY first_name\n;\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Intermediate - Window Functions.sql", "content": "-- Window Functions\n\n-- windows functions are really powerful and are somewhat like a group by - except they don't roll everything up into 1 row when grouping. \n-- windows functions allow us to look at a partition or a group, but they each keep their own unique rows in the output\n-- we will also look at things like Row Numbers, rank, and dense rank\n\nSELECT * \nFROM employee_demographics;\n\n-- first let's look at group by\nSELECT gender, ROUND(AVG(salary),1)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\nGROUP BY gender\n;\n\n-- now let's try doing something similar with a window function\n\nSELECT dem.employee_id, dem.first_name, gender, salary,\nAVG(salary) OVER()\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n-- now we can add any columns and it works. We could get this exact same output with a subquery in the select statement, \n-- but window functions have a lot more functionality, let's take a look\n\n\n-- if we use partition it's kind of like the group by except it doesn't roll up - it just partitions or breaks based on a column when doing the calculation\n\nSELECT dem.employee_id, dem.first_name, gender, salary,\nAVG(salary) OVER(PARTITION BY gender)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n\n-- now if we wanted to see what the salaries were for genders we could do that by using sum, but also we could use order by to get a rolling total\n\nSELECT dem.employee_id, dem.first_name, gender, salary,\nSUM(salary) OVER(PARTITION BY gender ORDER BY employee_id)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n\n-- Let's look at row_number rank and dense rank now\n\n\nSELECT dem.employee_id, dem.first_name, gender, salary,\nROW_NUMBER() OVER(PARTITION BY gender)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n-- let's try ordering by salary so we can see the order of highest paid employees by gender\nSELECT dem.employee_id, dem.first_name, gender, salary,\nROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc)\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n-- let's compare this to rank\nSELECT dem.employee_id, dem.first_name, gender, salary,\nROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc) row_num,\nRank() OVER(PARTITION BY gender ORDER BY salary desc) rank_1 \nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n-- notice rank repeats on tom ad jerry at 5, but then skips 6 to go to 7 -- this goes based off positional rank\n\n\n-- let's compare this to dense rank\nSELECT dem.employee_id, dem.first_name, gender, salary,\nROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc) row_num,\nRank() OVER(PARTITION BY gender ORDER BY salary desc) rank_1,\ndense_rank() OVER(PARTITION BY gender ORDER BY salary desc) dense_rank_2 -- this is numerically ordered instead of positional like rank\nFROM employee_demographics dem\nJOIN employee_salary sal\n\tON dem.employee_id = sal.employee_id\n;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Portfolio Project - Data Cleaning.sql", "content": "-- SQL Project - Data Cleaning\n\n-- https://www.kaggle.com/datasets/swaptr/layoffs-2022\n\n\n\n\n\n\nSELECT * \nFROM world_layoffs.layoffs;\n\n\n\n-- first thing we want to do is create a staging table. This is the one we will work in and clean the data. We want a table with the raw data in case something happens\nCREATE TABLE world_layoffs.layoffs_staging \nLIKE world_layoffs.layoffs;\n\nINSERT layoffs_staging \nSELECT * FROM world_layoffs.layoffs;\n\n\n-- now when we are data cleaning we usually follow a few steps\n-- 1. check for duplicates and remove any\n-- 2. standardize data and fix errors\n-- 3. Look at null values and see what \n-- 4. remove any columns and rows that are not necessary - few ways\n\n\n\n-- 1. Remove Duplicates\n\n# First let's check for duplicates\n\n\n\nSELECT *\nFROM world_layoffs.layoffs_staging\n;\n\nSELECT company, industry, total_laid_off,`date`,\n\t\tROW_NUMBER() OVER (\n\t\t\tPARTITION BY company, industry, total_laid_off,`date`) AS row_num\n\tFROM \n\t\tworld_layoffs.layoffs_staging;\n\n\n\nSELECT *\nFROM (\n\tSELECT company, industry, total_laid_off,`date`,\n\t\tROW_NUMBER() OVER (\n\t\t\tPARTITION BY company, industry, total_laid_off,`date`\n\t\t\t) AS row_num\n\tFROM \n\t\tworld_layoffs.layoffs_staging\n) duplicates\nWHERE \n\trow_num > 1;\n \n-- let's just look at oda to confirm\nSELECT *\nFROM world_layoffs.layoffs_staging\nWHERE company = 'Oda'\n;\n-- it looks like these are all legitimate entries and shouldn't be deleted. We need to really look at every single row to be accurate\n\n-- these are our real duplicates \nSELECT *\nFROM (\n\tSELECT company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions,\n\t\tROW_NUMBER() OVER (\n\t\t\tPARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions\n\t\t\t) AS row_num\n\tFROM \n\t\tworld_layoffs.layoffs_staging\n) duplicates\nWHERE \n\trow_num > 1;\n\n-- these are the ones we want to delete where the row number is > 1 or 2or greater essentially\n\n-- now you may want to write it like this:\nWITH DELETE_CTE AS \n(\nSELECT *\nFROM (\n\tSELECT company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions,\n\t\tROW_NUMBER() OVER (\n\t\t\tPARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions\n\t\t\t) AS row_num\n\tFROM \n\t\tworld_layoffs.layoffs_staging\n) duplicates\nWHERE \n\trow_num > 1\n)\nDELETE\nFROM DELETE_CTE\n;\n\n\nWITH DELETE_CTE AS (\n\tSELECT company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions, \n ROW_NUMBER() OVER (PARTITION BY company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions) AS row_num\n\tFROM world_layoffs.layoffs_staging\n)\nDELETE FROM world_layoffs.layoffs_staging\nWHERE (company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions, row_num) IN (\n\tSELECT company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions, row_num\n\tFROM DELETE_CTE\n) AND row_num > 1;\n\n-- one solution, which I think is a good one. Is to create a new column and add those row numbers in. Then delete where row numbers are over 2, then delete that column\n-- so let's do it!!\n\nALTER TABLE world_layoffs.layoffs_staging ADD row_num INT;\n\n\nSELECT *\nFROM world_layoffs.layoffs_staging\n;\n\nCREATE TABLE `world_layoffs`.`layoffs_staging2` (\n`company` text,\n`location`text,\n`industry`text,\n`total_laid_off` INT,\n`percentage_laid_off` text,\n`date` text,\n`stage`text,\n`country` text,\n`funds_raised_millions` int,\nrow_num INT\n);\n\nINSERT INTO `world_layoffs`.`layoffs_staging2`\n(`company`,\n`location`,\n`industry`,\n`total_laid_off`,\n`percentage_laid_off`,\n`date`,\n`stage`,\n`country`,\n`funds_raised_millions`,\n`row_num`)\nSELECT `company`,\n`location`,\n`industry`,\n`total_laid_off`,\n`percentage_laid_off`,\n`date`,\n`stage`,\n`country`,\n`funds_raised_millions`,\n\t\tROW_NUMBER() OVER (\n\t\t\tPARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions\n\t\t\t) AS row_num\n\tFROM \n\t\tworld_layoffs.layoffs_staging;\n\n-- now that we have this we can delete rows were row_num is greater than 2\n\nDELETE FROM world_layoffs.layoffs_staging2\nWHERE row_num >= 2;\n\n\n\n\n\n\n\n-- 2. Standardize Data\n\nSELECT * \nFROM world_layoffs.layoffs_staging2;\n\n-- if we look at industry it looks like we have some null and empty rows, let's take a look at these\nSELECT DISTINCT industry\nFROM world_layoffs.layoffs_staging2\nORDER BY industry;\n\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE industry IS NULL \nOR industry = ''\nORDER BY industry;\n\n-- let's take a look at these\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE company LIKE 'Bally%';\n-- nothing wrong here\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE company LIKE 'airbnb%';\n\n-- it looks like airbnb is a travel, but this one just isn't populated.\n-- I'm sure it's the same for the others. What we can do is\n-- write a query that if there is another row with the same company name, it will update it to the non-null industry values\n-- makes it easy so if there were thousands we wouldn't have to manually check them all\n\n-- we should set the blanks to nulls since those are typically easier to work with\nUPDATE world_layoffs.layoffs_staging2\nSET industry = NULL\nWHERE industry = '';\n\n-- now if we check those are all null\n\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE industry IS NULL \nOR industry = ''\nORDER BY industry;\n\n-- now we need to populate those nulls if possible\n\nUPDATE layoffs_staging2 t1\nJOIN layoffs_staging2 t2\nON t1.company = t2.company\nSET t1.industry = t2.industry\nWHERE t1.industry IS NULL\nAND t2.industry IS NOT NULL;\n\n-- and if we check it looks like Bally's was the only one without a populated row to populate this null values\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE industry IS NULL \nOR industry = ''\nORDER BY industry;\n\n-- ---------------------------------------------------\n\n-- I also noticed the Crypto has multiple different variations. We need to standardize that - let's say all to Crypto\nSELECT DISTINCT industry\nFROM world_layoffs.layoffs_staging2\nORDER BY industry;\n\nUPDATE layoffs_staging2\nSET industry = 'Crypto'\nWHERE industry IN ('Crypto Currency', 'CryptoCurrency');\n\n-- now that's taken care of:\nSELECT DISTINCT industry\nFROM world_layoffs.layoffs_staging2\nORDER BY industry;\n\n-- --------------------------------------------------\n-- we also need to look at \n\nSELECT *\nFROM world_layoffs.layoffs_staging2;\n\n-- everything looks good except apparently we have some \"United States\" and some \"United States.\" with a period at the end. Let's standardize this.\nSELECT DISTINCT country\nFROM world_layoffs.layoffs_staging2\nORDER BY country;\n\nUPDATE layoffs_staging2\nSET country = TRIM(TRAILING '.' FROM country);\n\n-- now if we run this again it is fixed\nSELECT DISTINCT country\nFROM world_layoffs.layoffs_staging2\nORDER BY country;\n\n\n-- Let's also fix the date columns:\nSELECT *\nFROM world_layoffs.layoffs_staging2;\n\n-- we can use str to date to update this field\nUPDATE layoffs_staging2\nSET `date` = STR_TO_DATE(`date`, '%m/%d/%Y');\n\n-- now we can convert the data type properly\nALTER TABLE layoffs_staging2\nMODIFY COLUMN `date` DATE;\n\n\nSELECT *\nFROM world_layoffs.layoffs_staging2;\n\n\n\n\n\n-- 3. Look at Null Values\n\n-- the null values in total_laid_off, percentage_laid_off, and funds_raised_millions all look normal. I don't think I want to change that\n-- I like having them null because it makes it easier for calculations during the EDA phase\n\n-- so there isn't anything I want to change with the null values\n\n\n\n\n-- 4. remove any columns and rows we need to\n\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE total_laid_off IS NULL;\n\n\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE total_laid_off IS NULL\nAND percentage_laid_off IS NULL;\n\n-- Delete Useless data we can't really use\nDELETE FROM world_layoffs.layoffs_staging2\nWHERE total_laid_off IS NULL\nAND percentage_laid_off IS NULL;\n\nSELECT * \nFROM world_layoffs.layoffs_staging2;\n\nALTER TABLE layoffs_staging2\nDROP COLUMN row_num;\n\n\nSELECT * \nFROM world_layoffs.layoffs_staging2;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "Portfolio Project - EDA.sql", "content": "-- EDA\n\n-- Here we are jsut going to explore the data and find trends or patterns or anything interesting like outliers\n\n-- normally when you start the EDA process you have some idea of what you're looking for\n\n-- with this info we are just going to look around and see what we find!\n\nSELECT * \nFROM world_layoffs.layoffs_staging2;\n\n-- EASIER QUERIES\n\nSELECT MAX(total_laid_off)\nFROM world_layoffs.layoffs_staging2;\n\n\n\n\n\n\n-- Looking at Percentage to see how big these layoffs were\nSELECT MAX(percentage_laid_off), MIN(percentage_laid_off)\nFROM world_layoffs.layoffs_staging2\nWHERE percentage_laid_off IS NOT NULL;\n\n-- Which companies had 1 which is basically 100 percent of they company laid off\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE percentage_laid_off = 1;\n-- these are mostly startups it looks like who all went out of business during this time\n\n-- if we order by funcs_raised_millions we can see how big some of these companies were\nSELECT *\nFROM world_layoffs.layoffs_staging2\nWHERE percentage_laid_off = 1\nORDER BY funds_raised_millions DESC;\n-- BritishVolt looks like an EV company, Quibi! I recognize that company - wow raised like 2 billion dollars and went under - ouch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n-- SOMEWHAT TOUGHER AND MOSTLY USING GROUP BY--------------------------------------------------------------------------------------------------\n\n-- Companies with the biggest single Layoff\n\nSELECT company, total_laid_off\nFROM world_layoffs.layoffs_staging\nORDER BY 2 DESC\nLIMIT 5;\n-- now that's just on a single day\n\n-- Companies with the most Total Layoffs\nSELECT company, SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY company\nORDER BY 2 DESC\nLIMIT 10;\n\n\n\n-- by location\nSELECT location, SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY location\nORDER BY 2 DESC\nLIMIT 10;\n\n-- this it total in the past 3 years or in the dataset\n\nSELECT country, SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY country\nORDER BY 2 DESC;\n\nSELECT YEAR(date), SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY YEAR(date)\nORDER BY 1 ASC;\n\n\nSELECT industry, SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY industry\nORDER BY 2 DESC;\n\n\nSELECT stage, SUM(total_laid_off)\nFROM world_layoffs.layoffs_staging2\nGROUP BY stage\nORDER BY 2 DESC;\n\n\n\n\n\n\n-- TOUGHER QUERIES------------------------------------------------------------------------------------------------------------------------------------\n\n-- Earlier we looked at Companies with the most Layoffs. Now let's look at that per year. It's a little more difficult.\n-- I want to look at \n\nWITH Company_Year AS \n(\n SELECT company, YEAR(date) AS years, SUM(total_laid_off) AS total_laid_off\n FROM layoffs_staging2\n GROUP BY company, YEAR(date)\n)\n, Company_Year_Rank AS (\n SELECT company, years, total_laid_off, DENSE_RANK() OVER (PARTITION BY years ORDER BY total_laid_off DESC) AS ranking\n FROM Company_Year\n)\nSELECT company, years, total_laid_off, ranking\nFROM Company_Year_Rank\nWHERE ranking <= 3\nAND years IS NOT NULL\nORDER BY years ASC, total_laid_off DESC;\n\n\n\n\n-- Rolling Total of Layoffs Per Month\nSELECT SUBSTRING(date,1,7) as dates, SUM(total_laid_off) AS total_laid_off\nFROM layoffs_staging2\nGROUP BY dates\nORDER BY dates ASC;\n\n-- now use it in a CTE so we can query off of it\nWITH DATE_CTE AS \n(\nSELECT SUBSTRING(date,1,7) as dates, SUM(total_laid_off) AS total_laid_off\nFROM layoffs_staging2\nGROUP BY dates\nORDER BY dates ASC\n)\nSELECT dates, SUM(total_laid_off) OVER (ORDER BY dates ASC) as rolling_total_layoffs\nFROM DATE_CTE\nORDER BY dates ASC;\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "README.md", "content": "# MySQL-YouTube-Series" }, { "path": "layoffs.csv", "content": "company,location,industry,total_laid_off,percentage_laid_off,date,stage,country,funds_raised_millions\r\nAtlassian,Sydney,Other,500,0.05,3/6/2023,Post-IPO,Australia,210\r\nSiriusXM,New York City,Media,475,0.08,3/6/2023,Post-IPO,United States,525\r\nAlerzo,Ibadan,Retail,400,NULL,3/6/2023,Series B,Nigeria,16\r\nUpGrad,Mumbai,Education,120,NULL,3/6/2023,Unknown,India,631\r\nLoft,Sao Paulo,Real Estate,340,0.15,3/3/2023,Unknown,Brazil,788\r\nEmbark Trucks,SF Bay Area,Transportation,230,0.7,3/3/2023,Post-IPO,United States,317\r\nLendi,Sydney,Real Estate,100,NULL,3/3/2023,Unknown,Australia,59\r\nUserTesting,SF Bay Area,Marketing,63,NULL,3/3/2023,Acquired,United States,152\r\nAirbnb,SF Bay Area,,30,NULL,3/3/2023,Post-IPO,United States,6400\r\nAccolade,Seattle,Healthcare,NULL,NULL,3/3/2023,Post-IPO,United States,458\r\nIndigo,Boston,Other,NULL,NULL,3/3/2023,Series F,United States.,1200\r\nZscaler,SF Bay Area,Security,177,0.03,3/2/2023,Post-IPO,United States,148\r\nMasterClass,SF Bay Area,Education,79,NULL,3/2/2023,Series E,United States,461\r\nAmbev Tech,Blumenau,Food,50,NULL,3/2/2023,Acquired,Brazil,NULL\r\nFittr,Pune,Fitness,30,0.11,3/2/2023,Series A,India,13\r\nCNET,SF Bay Area,Media,12,0.1,3/2/2023,Acquired,United States,20\r\nFlipkart,Bengaluru,Retail,NULL,NULL,3/2/2023,Acquired,India,12900\r\nKandela,Los Angeles,Consumer,NULL,1,3/2/2023,Acquired,United States,NULL\r\nTruckstop.com,Boise,Logistics,NULL,NULL,3/2/2023,Acquired,United States,NULL\r\nThoughtworks,Chicago,Other,500,0.04,3/1/2023,Post-IPO,United States,748\r\niFood,Sao Paulo,Food,355,0.06,3/1/2023,Subsidiary,Brazil,2100\r\nColor Health,SF Bay Area,Healthcare,300,NULL,3/1/2023,Series E,United States,482\r\nWaymo,SF Bay Area,Transportation,209,0.08,3/1/2023,Subsidiary,United States,5500\r\nPayFit,Paris,HR,200,0.2,3/1/2023,Series E,France,495\r\nYellow.ai,SF Bay Area,Support,200,NULL,3/1/2023,Series C,United States,102\r\nSonder,SF Bay Area,Travel,100,0.14,3/1/2023,Post-IPO,United States,839\r\nProtego Trust Bank,Seattle,Crypto,NULL,0.5,3/1/2023,Series A,United States,70\r\nElectronic Arts,Baton Rouge,Consumer,200,NULL,2/28/2023,Post-IPO,United States,2\r\nEventbrite,SF Bay Area,Consumer,80,0.08,2/28/2023,Post-IPO,United States,557\r\nDUX Education,Bengaluru,Education,NULL,1,2/28/2023,Unknown,India,NULL\r\nMeridianLink,Los Angeles,Finance,NULL,0.09,2/28/2023,Post-IPO,United States,485\r\nSono Motors,Munich,Transportation,300,NULL,2/27/2023,Post-IPO,Germany,126\r\nCerebral,SF Bay Area,Healthcare,285,0.15,2/27/2023,Series C,United States,462\r\nAmount,Chicago,Finance,130,0.25,2/27/2023,Unknown,United States,283\r\nPalantir,Denver,Data,75,0.02,2/27/2023,Post-IPO,United States.,3000\r\nOutreach,Seattle,Sales,70,0.07,2/27/2023,Series G,United States,489\r\nStytch,SF Bay Area,Security,19,0.25,2/27/2023,Series B,United States,126\r\nBitSight,Tel Aviv,Security,40,NULL,2/26/2023,Unknown,Israel,401\r\nTwitter,SF Bay Area,Consumer,200,0.1,2/25/2023,Post-IPO,United States,12900\r\nEricsson,Stockholm,Other,8500,0.08,2/24/2023,Post-IPO,Sweden,663\r\nSAP Labs,Bengaluru,Other,300,NULL,2/24/2023,Subsidiary,India,1300\r\nVelodyne Lidar,SF Bay Area,Transportation,220,NULL,2/24/2023,Acquired,United States,575\r\nMedallia,SF Bay Area,Support,59,NULL,2/24/2023,Acquired,United States,325\r\nEat Just,SF Bay Area,Food,40,NULL,2/24/2023,Unknown,United States,465\r\nLucira Health,SF Bay Area,Healthcare,26,NULL,2/24/2023,Post-IPO,United States,132\r\nStax,Orlando,Finance,24,NULL,2/24/2023,Series D,United States,263\r\nPoshmark,SF Bay Area,Retail,NULL,0.02,2/24/2023,Acquired,United States,153\r\nMerative,Ann Arbor,Healthcare,200,0.1,2/23/2023,Acquired,United States,NULL\r\nOneFootball,Berlin,Marketing,150,0.32,2/23/2023,Series D,Germany,442\r\nThe Iconic,Sydney,Retail,69,0.06,2/23/2023,Unknown,Australia,NULL\r\nEVgo,Los Angeles,Transportation,40,NULL,2/23/2023,Post-IPO,United States,400\r\nStrongDM,SF Bay Area,Infrastructure,40,NULL,2/23/2023,Series B,United States,76\r\nDapper Labs,Vancouver,Crypto,NULL,0.2,2/23/2023,Series D,United States,607\r\nMessari,New York City,Crypto,NULL,0.15,2/23/2023,Series B,United States,61\r\nVibrent Health,Washington D.C.,Healthcare,NULL,0.13,2/23/2023,Unknown,United States,NULL\r\nSynamedia,London,Media,200,0.12,2/22/2023,Unknown,United Kingdom,NULL\r\nTaskUs,San Antonio,Support,186,NULL,2/22/2023,Post-IPO,United States,279\r\nArch Oncology,St. Louis,Healthcare,NULL,NULL,2/22/2023,Series C,United States,155\r\nImmutable,Sydney,Crypto,NULL,0.11,2/22/2023,Series C,Australia,279\r\nJounce Therapeutics,Boston,Healthcare,NULL,0.57,2/22/2023,Acquired,United States,194\r\nLocomation,Pittsburgh,Transportation,NULL,1,2/22/2023,Seed,United States,57\r\nPolygon,Bengaluru,Crypto,100,0.2,2/21/2023,Unknown,India,451\r\nCrunchyroll,Tokyo,Media,85,NULL,2/21/2023,Unknown,Japan,26\r\nEthos Life,SF Bay Area,Finance,50,NULL,2/21/2023,Series D,United States,406\r\nBolt,Lagos,Transportation,17,NULL,2/21/2023,Series F,Nigeria,NULL\r\nCriteo,Paris,Marketing,NULL,NULL,2/21/2023,Post-IPO,France,61\r\nGreen Labs,Seoul,Food,NULL,NULL,2/21/2023,Series C,South Korea,214\r\nPeerStreet,Los Angeles,Real Estate,NULL,NULL,2/21/2023,Series C,United States,121\r\nZalando,Berlin,Retail,NULL,NULL,2/21/2023,Post-IPO,Germany,467\r\nMyGate,Bengaluru,Other,200,0.3,2/20/2023,Series B,India,79\r\nFireblocks,New York City,Crypto,30,0.05,2/20/2023,Series E,United States,1000\r\nKinde,Sydney,Other,8,0.28,2/20/2023,Seed,Australia,10\r\nFipola,Chennai,Food,NULL,1,2/20/2023,Series A,United States,9\r\nHP,Tel Aviv,Hardware,100,NULL,2/19/2023,Post-IPO,Israel,4200\r\nTencent,Shenzen,Consumer,300,NULL,2/17/2023,Post-IPO,China,12600\r\nEvernote,SF Bay Area,Consumer,129,NULL,2/17/2023,Acquired,United States,290\r\nChipper Cash,SF Bay Area,Finance,100,0.33,2/17/2023,Series C,United States,302\r\nDigimarc,Portland,Other,NULL,NULL,2/17/2023,Post-IPO,United States,105\r\nReserve,SF Bay Area,Crypto,NULL,NULL,2/17/2023,Unknown,United States,NULL\r\nDocuSign,SF Bay Area,Sales,680,0.1,2/16/2023,Post-IPO,United States,536\r\nPico Interactive,SF Bay Area,Other,400,0.2,2/16/2023,Acquired,United States,62\r\nThe RealReal,SF Bay Area,Retail,230,0.07,2/16/2023,Post-IPO,United States,356\r\nSmartsheet,Seattle,Other,85,0.03,2/16/2023,Post-IPO,United States,152\r\nConvoy,Atlanta,Logistics,NULL,NULL,2/16/2023,Series E,United States,1100\r\nWix,Tel Aviv,Marketing,370,0.06,2/15/2023,Post-IPO,Israel,58\r\nServiceTitan,Los Angeles,Sales,221,0.08,2/15/2023,Series G,United States,1100\r\nNeon,Sao Paulo,Finance,210,0.09,2/15/2023,Series D,Brazil,720\r\nJellysmack,Paris,Media,208,NULL,2/15/2023,Series C,France,22\r\nDigitalOcean,New York City,Infrastructure,200,0.11,2/15/2023,Post-IPO,United States,491\r\nSprinklr,New York City,Support,100,0.04,2/15/2023,Post-IPO,United States,429\r\nBetterment,New York City,Finance,28,NULL,2/15/2023,Series F,United States,435\r\nDivvy Homes,SF Bay Area,Real Estate,NULL,NULL,2/15/2023,Series B,United States,180\r\nMilkrun,Sydney,Food,NULL,0.2,2/15/2023,Series A,Australia,86\r\nMomentive,SF Bay Area,Marketing,NULL,0.14,2/15/2023,Post-IPO,United States,1100\r\nObserve.AI,SF Bay Area,Support,NULL,NULL,2/15/2023,Series C,United States,214\r\nReligion of Sports,Los Angeles,Media,NULL,NULL,2/15/2023,Series B,United States,63\r\nTackle,Boise,Other,NULL,0.15,2/15/2023,Series C,United States,148\r\nVicarious Surgical,Boston,Healthcare,NULL,0.14,2/15/2023,Post-IPO,United States,185\r\nCommerceHub,Albany,Retail,371,0.31,2/14/2023,Acquired,United States,NULL\r\nHackerEarth,SF Bay Area,HR,NULL,0.08,2/14/2023,Series B,United States,11\r\nPhableCare,Bengaluru,Healthcare,NULL,0.7,2/14/2023,Series B,India,40\r\nUdemy,SF Bay Area,Education,NULL,0.1,2/14/2023,Post-IPO,United States,311\r\nTwilio,SF Bay Area,Other,1500,0.17,2/13/2023,Post-IPO,United States.,614\r\nElectric,New York City,Other,141,0.25,2/13/2023,Series D,United States,212\r\nEMX Digital,New York City,Marketing,100,1,2/13/2023,Unknown,United States,NULL\r\nPetLove,Sao Paulo,Retail,94,NULL,2/13/2023,Series C,Brazil,225\r\niRobot,Boston,Consumer,85,0.07,2/13/2023,Acquired,United States,30\r\nCollective Health,SF Bay Area,Healthcare,54,NULL,2/13/2023,Series F,United States,719\r\nMagic Eden,SF Bay Area,Crypto,22,NULL,2/13/2023,Series B,United States,170\r\nCasavo,Milan,Real Estate,NULL,0.3,2/13/2023,Unknown,Italy,708\r\nFoodpanda,Singapore,Food,NULL,NULL,2/13/2023,Acquired,Singapore,749\r\nGetir,London,Food,NULL,NULL,2/13/2023,Series E,United Kingdom,1800\r\nLinkedIn,SF Bay Area,HR,NULL,NULL,2/13/2023,Acquired,United States,154\r\nMoladin,Jakarta,Transportation,360,0.11,2/12/2023,Series B,Indonesia,138\r\nTripleLift,New York City,Marketing,100,0.2,2/10/2023,Acquired,United States,16\r\nTikTok India,Mumbai,Consumer,40,NULL,2/10/2023,Acquired,India,9400\r\nOpen Co,Sao Paulo,Finance,NULL,NULL,2/10/2023,Series D,Brazil,140\r\nRigetti Computing,SF Bay Area,Hardware,NULL,0.28,2/10/2023,Post-IPO,United States,298\r\nYahoo,SF Bay Area,Consumer,1600,0.2,2/9/2023,Acquired,United States,6\r\nMisfits Market,Philadelphia,Food,649,0.33,2/9/2023,Series C,United States,526\r\nDeliveroo,London,Food,350,0.09,2/9/2023,Post-IPO,United Kingdom,1700\r\nOlive AI,Columbus,Healthcare,215,0.35,2/9/2023,Series H,United States,856\r\nOportun,SF Bay Area,Finance,155,0.1,2/9/2023,Post-IPO,United States,566\r\nGitLab,SF Bay Area,Product,130,0.07,2/9/2023,Post-IPO,United States,413\r\nBark,New York City,Retail,126,0.12,2/9/2023,Post-IPO,United States,NULL\r\nNomad Health,New York City,Healthcare,119,0.17,2/9/2023,Unknown,United States,218\r\nVeriff,Tallinn,Security,66,0.12,2/9/2023,Series C,Estonia,192\r\nREE Automotive,Tel Aviv,Transportation,31,0.11,2/9/2023,Post-IPO,Israel,317\r\nGitHub,SF Bay Area,Product,NULL,0.1,2/9/2023,Acquired,United States,350\r\nQuillt,St. Louis,Media,NULL,NULL,2/9/2023,Unknown,United States,NULL\r\nWeTrade,Bengaluru,Crypto,NULL,1,2/9/2023,Unknown,India,NULL\r\nGoDaddy,Phoenix,Marketing,530,0.08,2/8/2023,Post-IPO,United States,800\r\nAffirm,SF Bay Area,Finance,500,0.19,2/8/2023,Post-IPO,United States,1500\r\nGusto,SF Bay Area,HR,126,0.05,2/8/2023,Series E,United States,746\r\nGong,SF Bay Area,Sales,80,0.07,2/8/2023,Series E,United States,583\r\nBeam Benefits,Columbus,Healthcare,31,0.08,2/8/2023,Series E,United States,168\r\nEquitybee,SF Bay Area,Finance,24,0.25,2/8/2023,Series B,United States,85\r\nBaraja,Sydney,Transportation,NULL,0.75,2/8/2023,Unknown,Australia,63\r\nKoho,Toronto,Finance,NULL,0.14,2/8/2023,Series D,Canada,278\r\nMedly,New York City,Healthcare,NULL,1,2/8/2023,Series C,United States,100\r\nNearmap,Sydney,Construction,NULL,0.2,2/8/2023,Acquired,Australia,15\r\nZoom,SF Bay Area,Other,1300,0.15,2/7/2023,Post-IPO,United States,276\r\neBay,SF Bay Area,Retail,500,0.04,2/7/2023,Post-IPO,United States,1200\r\nSecureWorks,Atlanta,Security,212,0.09,2/7/2023,Post-IPO,United States,83\r\nSalesloft,Atlanta,Sales,100,0.1,2/7/2023,Acquired,United States,245\r\nOpenpay,Melbourne,Finance,83,1,2/7/2023,Post-IPO,Australia,299\r\nLearnUpon,Dublin,Education,27,0.09,2/7/2023,Private Equity,Ireland,56\r\nSana Benefits,Austin,HR,NULL,0.19,2/7/2023,Series B,United States,106\r\nDell,Austin,Hardware,6650,0.05,2/6/2023,Post-IPO,United States,NULL\r\nLoggi,Sao Paulo,Logistics,300,0.07,2/6/2023,Series F,Brazil,507\r\nCatch.com.au,Melbourne,Retail,100,NULL,2/6/2023,Acquired,Australia,80\r\nVinFast US,Los Angeles,Transportation,80,NULL,2/6/2023,Subsidiary,United States,NULL\r\nDrift,Boston,Marketing,59,NULL,2/6/2023,Acquired,United States,107\r\nPocket Aces,Mumbai,Media,50,0.25,2/6/2023,Unknown,India,19\r\nClari,SF Bay Area,Sales,20,NULL,2/6/2023,Series F,United States,496\r\nC6 Bank,Sao Paulo,Finance,NULL,NULL,2/6/2023,Unknown,Brazil,2300\r\nDaraz,Singapore,Retail,NULL,0.11,2/6/2023,Unknown,Singapore,NULL\r\nTenureX,Tel Aviv,Finance,NULL,NULL,2/6/2023,Seed,Israel,6\r\nKyruus,Boston,Healthcare,70,NULL,2/5/2023,Unknown,United States,183\r\nLightico,Tel Aviv,Finance,20,0.25,2/5/2023,Series B,Israel,42\r\nFarEye,New Delhi,Logistics,90,NULL,2/3/2023,Series E,India,150\r\nProtocol Labs,SF Bay Area,Crypto,89,0.2,2/3/2023,Unknown,United States,10\r\nFinder,Sydney,Retail,NULL,0.15,2/3/2023,Unknown,Australia,30\r\nByju's,Bengaluru,Education,1500,NULL,2/2/2023,Private Equity,India,5500\r\nOkta,SF Bay Area,Security,300,0.05,2/2/2023,Post-IPO,United States,1200\r\nAutodesk,SF Bay Area,Other,250,0.02,2/2/2023,Post-IPO,United States,NULL\r\nMindstrong,SF Bay Area,Healthcare,127,NULL,2/2/2023,Series C,United States,160\r\nNCC Group,Manchester,Security,125,0.07,2/2/2023,Post-IPO,United Kingdom,NULL\r\nMiro,SF Bay Area,Other,119,0.07,2/2/2023,Series C,United States,476\r\nGetir,New York City,Food,100,NULL,2/2/2023,Series E,United States,1800\r\nHighspot,Seattle,Sales,100,0.1,2/2/2023,Series F,United States,644\r\nBittrex,Seattle,Crypto,80,NULL,2/2/2023,Unknown,United States,NULL\r\nSnowplow,London,Data,40,NULL,2/2/2023,Series B,United States,55\r\nArticulate,New York City,Education,38,NULL,2/2/2023,Series A,United States,1500\r\nDesktop Metal,Boston,Other,NULL,NULL,2/2/2023,Post-IPO,United States,811\r\nGetaround,SF Bay Area,Transportation,NULL,0.1,2/2/2023,Post-IPO,United States,948\r\nNCSoft,Seoul,Consumer,NULL,0.2,2/2/2023,Post-IPO,South Korea,240\r\nTalkdesk,SF Bay Area,Support,NULL,NULL,2/2/2023,Series D,United States,497\r\nSplunk,SF Bay Area,Data,325,0.04,2/1/2023,Post-IPO,United States,2400\r\nPinterest,SF Bay Area,Consumer,150,NULL,2/1/2023,Post-IPO,United States,1500\r\nDraftKings,Boston,Consumer,140,0.04,2/1/2023,Post-IPO,United States,719\r\nCyren,Washington D.C.,Security,121,NULL,2/1/2023,Post-IPO,United States,161\r\nWorkato,SF Bay Area,Other,90,0.1,2/1/2023,Series E,United States,415\r\nVerticalScope,Toronto,Media,60,0.22,2/1/2023,Post-IPO,Canada,NULL\r\nWheel,Austin,Healthcare,56,0.28,2/1/2023,Series C,United States,215\r\nChainalysis,New York City,Crypto,44,0.05,2/1/2023,Series F,United States,536\r\nAppgate,Miami,Security,34,0.08,2/1/2023,Post-IPO,United States,NULL\r\nExterro,Portland,Legal,24,0.03,2/1/2023,Private Equity,United States,100\r\nTheSkimm,New York City,Media,17,0.1,2/1/2023,Series C,United States,28\r\nAda,Toronto,Support,NULL,NULL,2/1/2023,Series C,Canada,190\r\nBustle Digital Group,New York City,Media,NULL,0.08,2/1/2023,Series E,United States,80\r\nFrequency Therapeutics,Boston,Healthcare,NULL,0.5,2/1/2023,Post-IPO,United States,76\r\nMariaDB,Helsinki,Data,NULL,0.08,2/1/2023,Post-IPO,Finland,245\r\nMatch Group,New York City,Consumer,NULL,0.08,2/1/2023,Post-IPO,United States,NULL\r\nOmnipresent,London,HR,NULL,NULL,2/1/2023,Series B,United States,137\r\nPicnic,Seattle,Food,NULL,NULL,2/1/2023,Unknown,United States,52\r\nRivian,Detroit,Transportation,NULL,0.06,2/1/2023,Post-IPO,United States,10700\r\nPayPal,SF Bay Area,Finance,2000,0.07,1/31/2023,Post-IPO,United States,216\r\nNetApp,SF Bay Area,Data,960,0.08,1/31/2023,Post-IPO,United States,NULL\r\nWorkday,SF Bay Area,HR,525,0.03,1/31/2023,Post-IPO,United States,230\r\nHubSpot,Boston,Marketing,500,0.07,1/31/2023,Post-IPO,United States,100\r\nUpstart,SF Bay Area,Finance,365,0.2,1/31/2023,Post-IPO,United States,144\r\nSoftware AG,Frankfurt,Data,200,0.04,1/31/2023,Post-IPO,Germany,344\r\nWish,SF Bay Area,Retail,150,0.17,1/31/2023,Post-IPO,United States,1600\r\nWefox,Berlin,Finance,100,NULL,1/31/2023,Series D,Germany,1300\r\nTilting Point,New York City,Consumer,60,0.14,1/31/2023,Unknown,United States,235\r\nGokada,Lagos,Transportation,54,NULL,1/31/2023,Unknown,Nigeria,12\r\nAU10TIX,Tel Aviv,Security,19,0.09,1/31/2023,Unknown,Israel,80\r\nNational Instruments,Austin,Hardware,NULL,0.04,1/31/2023,Post-IPO,United States,NULL\r\nOpenText,Waterloo,Data,NULL,0.08,1/31/2023,Post-IPO,Canada,1100\r\nPhilips,Amsterdam,Healthcare,6000,0.13,1/30/2023,Post-IPO,Netherlands,NULL\r\nOLX Group,Amsterdam,Marketing,1500,0.15,1/30/2023,Acquired,Netherlands,NULL\r\nArrival,London,Transportation,800,0.5,1/30/2023,Post-IPO,United Kingdom,629\r\nGroupon,Chicago,Retail,500,NULL,1/30/2023,Post-IPO,United States,1400\r\nIntel,SF Bay Area,Hardware,343,NULL,1/30/2023,Post-IPO,United States,12\r\nGlovo,Barcelona,Food,250,0.06,1/30/2023,Acquired,Spain,1200\r\nDelivery Hero,Berlin,Food,156,0.04,1/30/2023,Post-IPO,Germany,9900\r\nImpossible Foods copy,SF Bay Area,Food,140,0.2,1/30/2023,Series H,United States,1900\r\nChrono24,Karlsruhe,Retail,65,0.13,1/30/2023,Series C,Germany,205\r\nBM Technologies,Philadelphia,Finance,NULL,0.25,1/30/2023,Post-IPO,United States,NULL\r\nOlist,Curitiba,Retail,NULL,NULL,1/30/2023,Series E,Brazil,322\r\nOyster,Charlotte,HR,NULL,NULL,1/30/2023,Series C,United States,224\r\nPrime Trust,Las Vegas,Crypto,NULL,0.33,1/30/2023,Series B,United States,176\r\nQuantum SI,New Haven,Healthcare,NULL,0.12,1/30/2023,Post-IPO,United States,425\r\nSoFi,SF Bay Area,Finance,NULL,NULL,1/30/2023,Post-IPO,United States,3000\r\nHoxhunt,Helsinki,Security,NULL,NULL,1/29/2023,Series B,Finland,43\r\nMe Poupe,Sao Paulo,Finance,60,0.5,1/28/2023,Unknown,Brazil,NULL\r\nCoinTracker,SF Bay Area,Crypto,19,NULL,1/28/2023,Series A,United States,101\r\nSSense,Montreal,Retail,138,0.07,1/27/2023,Series A,Canada,NULL\r\nDealShare,Bengaluru,Retail,100,0.06,1/27/2023,Series E,India,390\r\nRuggable,Los Angeles,Retail,100,NULL,1/27/2023,Seed,United States,NULL\r\nSynopsys,SF Bay Area,Other,100,NULL,1/27/2023,Post-IPO,United States,NULL\r\nHeycar,Berlin,Transportation,73,NULL,1/27/2023,Unknown,Germany,NULL\r\nMatrixport,Singapore,Crypto,29,0.1,1/27/2023,Series C,Singapore,100\r\nShakepay,Montreal,Crypto,21,0.25,1/27/2023,Series A,Canada,45\r\n#Paid,Toronto,Marketing,19,0.17,1/27/2023,Series B,Canada,21\r\nDecent,SF Bay Area,Healthcare,NULL,NULL,1/27/2023,Series A,United States,18\r\nFeedzai,Coimbra,Finance,NULL,NULL,1/27/2023,Unknown,Portugal,273\r\nNate,New York City,Retail,NULL,NULL,1/27/2023,Series A,United States,47\r\nSAP,Walldorf,Other,3000,0.03,1/26/2023,Post-IPO,Germany,1300\r\nConfluent,SF Bay Area,Data,221,0.08,1/26/2023,Post-IPO,United States,455\r\nDriveWealth,Jersey City,Finance,NULL,0.2,1/26/2023,Series D,United States,550\r\nMode Global,London,Finance,NULL,1,1/26/2023,Post-IPO,United Kingdom,NULL\r\nPlus One Robotics,San Antonio,Other,NULL,0.1,1/26/2023,Series B,United States,43\r\nQuora,SF Bay Area,Consumer,NULL,NULL,1/26/2023,Series D,United States,226\r\nIBM,New York City,Hardware,3900,0.02,1/25/2023,Post-IPO,United States,NULL\r\nLam Research,SF Bay Area,Hardware,1300,0.07,1/25/2023,Post-IPO,United States,NULL\r\nShutterfly,SF Bay Area,Retail,360,NULL,1/25/2023,Acquired,United States,50\r\nLuno,London,Crypto,330,0.35,1/25/2023,Acquired,United Kingdom,13\r\nClear Capital,Reno,Real Estate,250,0.25,1/25/2023,Unknown,United States,NULL\r\nGuardant Health,SF Bay Area,Healthcare,130,0.07,1/25/2023,Post-IPO,United States,550\r\nSirionLabs,Seattle,Legal,130,0.15,1/25/2023,Series D,United States,171\r\nTier Mobility,Berlin,Transportation,80,0.07,1/25/2023,Series D,Germany,646\r\nCareRev,Los Angeles,Retail,NULL,NULL,1/25/2023,Series A,United States,51\r\nFinastra,Tel Aviv,Finance,NULL,NULL,1/25/2023,Unknown,Israel,NULL\r\nNoom,New York City,Fitness,NULL,NULL,1/25/2023,Series F,United States,657\r\nPagSeguro,Sao Paulo,Finance,NULL,0.07,1/25/2023,Post-IPO,Brazil,NULL\r\nProsus,Amsterdam,Other,NULL,0.3,1/25/2023,Unknown,Netherlands,NULL\r\nVacasa,Portland,Travel,1300,0.17,1/24/2023,Post-IPO,United States,834\r\nInnovaccer,SF Bay Area,Healthcare,245,0.15,1/24/2023,Series E,United States,379\r\nBolt,SF Bay Area,Finance,50,0.1,1/24/2023,Series E,United States,1300\r\nPartnerStack,Toronto,Sales,40,0.2,1/24/2023,Series B,Canada,36\r\nGitpod,Kiel,Product,21,0.28,1/24/2023,Series A,Germany,41\r\nOFFOR Health,Columbus,Healthcare,16,NULL,1/24/2023,Series A,United States,14\r\nVenngage,Toronto,Marketing,11,0.2,1/24/2023,Unknown,Canada,NULL\r\nCoachHub,Berlin,HR,NULL,0.1,1/24/2023,Series C,Germany,332\r\nCorvus Insurance,Boston,Finance,NULL,0.14,1/24/2023,Series C,United States,160\r\nIcon,Austin,Construction,NULL,NULL,1/24/2023,Series B,United States,451\r\nPagerDuty,SF Bay Area,Product,NULL,0.07,1/24/2023,Post-IPO,United States,173\r\nScoro,London,HR,NULL,0.09,1/24/2023,Series B,United Kingdom,23\r\nSpotify,Stockholm,Media,600,0.06,1/23/2023,Post-IPO,Sweden,2100\r\nUber Freight,SF Bay Area,Logistics,150,0.03,1/23/2023,Subsidiary,United States,2700\r\nInmobi,Bengaluru,Marketing,50,NULL,1/23/2023,Unknown,India,320\r\nInnovid,New York City,Marketing,40,0.1,1/23/2023,Post-IPO,United States,295\r\nBooktopia,Sydney,Retail,30,NULL,1/23/2023,Series A,Australia,23\r\nErmetic,SF Bay Area,Security,30,0.17,1/23/2023,Series B,United States,97\r\nNamogoo,Tel Aviv,Marketing,20,0.15,1/23/2023,Series C,United States,69\r\nCamp K12,Gurugram,Education,NULL,0.7,1/23/2023,Series A,India,16\r\nGemini,New York City,Crypto,NULL,0.1,1/23/2023,Unknown,United States,423\r\nYext,New York City,Marketing,NULL,0.08,1/23/2023,Post-IPO,United States,117\r\nBUX,Amsterdam,Finance,NULL,NULL,1/22/2023,Series C,Netherlands,115\r\nGoogle,SF Bay Area,Consumer,12000,0.06,1/20/2023,Post-IPO,United States,26\r\nWayfair,Boston,Retail,1750,0.1,1/20/2023,Post-IPO,United States,1700\r\nSwiggy,Bengaluru,Food,380,0.06,1/20/2023,Unknown,India,3600\r\nMediBuddy,Bengaluru,Healthcare,200,NULL,1/20/2023,Acquired,India,192\r\nVox Media,Washington D.C.,Media,130,0.07,1/20/2023,Series F,United States,307\r\nBitTorrent,SF Bay Area,Infrastructure,92,NULL,1/20/2023,Acquired,United States,NULL\r\nKarat,Seattle,HR,47,NULL,1/20/2023,Unknown,United States,169\r\nEnjoei,Sao Paulo,Retail,31,0.1,1/20/2023,Unknown,Brazil,14\r\nEdifecs,Seattle,Healthcare,30,NULL,1/20/2023,Unknown,United States,1\r\nCitrine Informatics,SF Bay Area,Data,22,0.27,1/20/2023,Series C,United States,64\r\nAvalara,Seattle,Finance,NULL,NULL,1/20/2023,Acquired,United States,341\r\nCyteir Therapeutics,Boston,Healthcare,NULL,0.7,1/20/2023,Series C,United States,156\r\nMorning Consult,Washington D.C.,Data,NULL,NULL,1/20/2023,Series B,United States,91\r\nTikTok,Los Angeles,Consumer,NULL,NULL,1/20/2023,Acquired,United States,NULL\r\nZappos,Las Vegas,Retail,NULL,NULL,1/20/2023,Acquired,United States,62\r\nCapital One,Washington D.C.,Finance,1100,NULL,1/19/2023,Post-IPO,United States,NULL\r\nProterra,SF Bay Area,Transportation,300,NULL,1/19/2023,Post-IPO,United States,1200\r\nWeWork ,New York City,Real Estate,300,NULL,1/19/2023,Post-IPO,United States,22200\r\nHubilo,SF Bay Area,Other,115,0.35,1/19/2023,Series B,United States,153\r\nSaks.com,New York City,Retail,100,0.05,1/19/2023,Unknown,United States,965\r\nCS Disco,Austin,Legal,62,0.09,1/19/2023,Post-IPO,United States,233\r\nRiot Games,Los Angeles,Consumer,46,NULL,1/19/2023,Acquired,United States,21\r\nHydrow,Boston,Fitness,30,NULL,1/19/2023,Series D,United States,269\r\nEarth Rides,Nashville,Transportation,NULL,1,1/19/2023,Unknown,United States,2\r\nFandom,SF Bay Area,Media,NULL,NULL,1/19/2023,Series E,United States,145\r\nIAM Robotics,Pittsburgh,Hardware,NULL,NULL,1/19/2023,Unknown,United States,21\r\nIcertis,Seattle,Legal,NULL,NULL,1/19/2023,Unknown,United States,521\r\nMagnite,Los Angeles,Marketing,NULL,0.06,1/19/2023,Post-IPO,United States,400\r\nMudafy,Mexico City,Real Estate,NULL,0.7,1/19/2023,Series A,United States,13\r\nPersonalis,SF Bay Area,Healthcare,NULL,0.3,1/19/2023,Post-IPO,United States,225\r\nPrisma,SF Bay Area,Data,NULL,0.28,1/19/2023,Series B,United States,56\r\nSpaceship,Sydney,Finance,NULL,NULL,1/19/2023,Series A,Australia,41\r\nWallbox,Barcelona,Energy,NULL,0.15,1/19/2023,Post-IPO,Spain,167\r\nMicrosoft,Seattle,Other,10000,0.05,1/18/2023,Post-IPO,United States,1\r\nSophos,Oxford,Security,450,0.1,1/18/2023,Acquired,United States,125\r\nTeladoc Health,New York City,Healthcare,300,0.06,1/18/2023,Post-IPO,United States,172\r\nVroom,New York City,Transportation,275,0.2,1/18/2023,Post-IPO,United States,1300\r\n8x8,SF Bay Area,Support,155,0.07,1/18/2023,Post-IPO,United States,253\r\nPagaya,New York City,Finance,140,0.2,1/18/2023,Post-IPO,United States,571\r\nBenevity,Calgary,Other,137,0.14,1/18/2023,Unknown,Canada,69\r\nInspirato,Denver,Travel,109,0.12,1/18/2023,Post-IPO,United States,179\r\nJumpcloud,Boulder,Security,100,0.12,1/18/2023,Series F,United States,416\r\nnCino,Wilmington,Finance,100,0.07,1/18/2023,Post-IPO,United States,1100\r\nStarry,Boston,Other,100,0.24,1/18/2023,Post-IPO,United States,260\r\nHootsuite,Vancouver,Marketing,70,0.07,1/18/2023,Series C,Canada,300\r\nClue,Berlin,Healthcare,31,0.31,1/18/2023,Unknown,Germany,47\r\nAddepar,SF Bay Area,Finance,20,0.03,1/18/2023,Series F,United States,491\r\n80 Acres Farms,Cincinnati,Food,NULL,0.1,1/18/2023,Unknown,United States,275\r\nAiven,Helsinki,Infrastructure,NULL,0.2,1/18/2023,Series D,Finland,420\r\nBally's Interactive,Providence,NULL,NULL,0.15,1/18/2023,Post-IPO,United States,946\r\nBetterfly,Santiago,Healthcare,NULL,0.3,1/18/2023,Series C,Chile,204\r\nCazoo,London,Transportation,NULL,NULL,1/18/2023,Post-IPO,United Kingdom,2000\r\nCoda,SF Bay Area,Other,NULL,NULL,1/18/2023,Series D,United States,240\r\nCypress.io,Atlanta,Product,NULL,NULL,1/18/2023,Series B,United States,54\r\nLucid Diagnostics,New York City,Healthcare,NULL,0.2,1/18/2023,Post-IPO,United States,NULL\r\nMavenir,Dallas,Infrastructure,NULL,NULL,1/18/2023,Acquired,United States,854\r\nRedbubble,Melbourne,Retail,NULL,0.14,1/18/2023,Post-IPO,Australia,55\r\nLightspeed Commerce,Montreal,Retail,300,0.1,1/17/2023,Post-IPO,Canada,1200\r\nUnity,SF Bay Area,Other,284,0.03,1/17/2023,Post-IPO,United States,1300\r\nBritishvolt,London,Transportation,206,1,1/17/2023,Unknown,United Kingdom,2400\r\nClutch,Toronto,Transportation,150,NULL,1/17/2023,Unknown,Canada,253\r\nExotel,Bengaluru,Support,142,0.15,1/17/2023,Series D,India,87\r\nUnico,Sao Paulo,Other,110,0.1,1/17/2023,Series D,Brazil,336\r\nTul,Bogota,Construction,100,NULL,1/17/2023,Series B,Colombia,218\r\nAmerican Robotics,Boston,Other,50,0.65,1/17/2023,Acquired,United States,92\r\nLuxury Presence,Los Angeles,Real Estate,44,NULL,1/17/2023,Series B,United States,31\r\nRingCentral,SF Bay Area,Other,30,NULL,1/17/2023,Post-IPO,United States,44\r\nFishbrain,Stockholm,Consumer,NULL,NULL,1/17/2023,Unknown,United States,59\r\nGoMechanic,Gurugram,Transportation,NULL,0.7,1/17/2023,Series C,India,54\r\nLiveVox,SF Bay Area,Support,NULL,0.16,1/17/2023,Post-IPO,United States,12\r\nOracle,SF Bay Area,Other,NULL,NULL,1/17/2023,Post-IPO,United States,NULL\r\nRappi,Buenos Aires,Food,NULL,NULL,1/17/2023,Unknown,Argentina,2300\r\nRateGenius,Austin,Finance,NULL,NULL,1/17/2023,Acquired,United States,2\r\nXP,Sao Paulo,Finance,NULL,NULL,1/17/2023,Post-IPO,Brazil,NULL\r\nPagBank,Sao Paulo,Finance,500,0.07,1/16/2023,Post-IPO,Brazil,NULL\r\nShareChat,Bengaluru,Consumer,500,0.2,1/16/2023,Series H,India,1700\r\nGramophone,Indore,Food,75,NULL,1/16/2023,Series B,India,17\r\nClearCo,Toronto,Finance,50,0.3,1/16/2023,Series C,Canada,698\r\nDunzo,Bengaluru,Food,NULL,0.03,1/16/2023,Unknown,India,382\r\nIgnition,Sydney,Finance,NULL,0.1,1/16/2023,Series C,Australia,74\r\nRebel Foods,Mumbai,Food,NULL,0.02,1/16/2023,Unknown,India,548\r\nCaptain Fresh ,Bengaluru,Food,120,NULL,1/15/2023,Series C,India,126\r\nSnappy,New York City,Marketing,100,0.3,1/15/2023,Series C,United States,104\r\nBharatAgri,Mumbai,Food,40,0.43,1/15/2023,Series A,India,21\r\nDeHaat,Patna,Food,NULL,0.05,1/15/2023,Series E,India,254\r\nBlack Shark,Shenzen,Hardware,900,NULL,1/13/2023,Unknown,China,NULL\r\nOla,Bengaluru,Transportation,200,NULL,1/13/2023,Series J,India,5000\r\nBonterra ,Austin,Other,140,0.1,1/13/2023,Unknown,United States,NULL\r\nVial,SF Bay Area,Healthcare,40,NULL,1/13/2023,Series B,United States,101\r\nArch Oncology,Brisbane,Healthcare,NULL,1,1/13/2023,Series C,United States,155\r\nCarvana,Phoenix,Transportation,NULL,NULL,1/13/2023,Post-IPO,United States,1600\r\nCoSchedule,Bismarck,Marketing,NULL,NULL,1/13/2023,Unknown,United States,2\r\nGoCanvas,Washington D.C.,Other,NULL,NULL,1/13/2023,Acquired,United States,21\r\nJellyfish,Boston,Product,NULL,0.09,1/13/2023,Series C,United States,114\r\nLending Club,SF Bay Area,Finance,225,0.14,1/12/2023,Post-IPO,United States,392\r\nSmartNews,Tokyo,Media,120,0.4,1/12/2023,Series F,United States,410\r\nSkit.ai,Bengaluru,Support,115,NULL,1/12/2023,Series B,India,28\r\nPier,Sao Paulo,Finance,111,0.39,1/12/2023,Series B,Brazil,42\r\nBlockchain.com,London,Crypto,110,0.28,1/12/2023,Series D,United Kingdom,490\r\nGreenlight,Atlanta,Finance,104,0.21,1/12/2023,Series D,United States,556\r\nCashfree Payments,Bengaluru,Finance,100,NULL,1/12/2023,Series B,India,41\r\nMapbox,Washington D.C.,Data,64,NULL,1/12/2023,Unknown,United States,334\r\nDefinitive Healthcare,Boston,Healthcare,55,0.06,1/12/2023,Post-IPO,United States,NULL\r\nAkili Labs,Baltimore,Healthcare,46,0.3,1/12/2023,Seed,United States,NULL\r\nCareer Karma,SF Bay Area,Education,22,NULL,1/12/2023,Series B,United States,51\r\nCrypto.com,Singapore,Crypto,NULL,0.2,1/12/2023,Unknown,United States,NULL\r\nLattice,SF Bay Area,HR,NULL,0.15,1/12/2023,Series F,United States,328\r\nLife360,SF Bay Area,Consumer,NULL,0.14,1/12/2023,Post-IPO,United States,158\r\nRock Content,Miami,Marketing,NULL,0.15,1/12/2023,Series B,United States,34\r\nFlexport,SF Bay Area,Logistics,640,0.2,1/11/2023,Series E,United States,2400\r\nQualtrics,Salt Lake City,Other,270,0.05,1/11/2023,Post-IPO,United States,400\r\nVerily,SF Bay Area,Healthcare,250,0.15,1/11/2023,NULL,United States,3500\r\nTipalti,SF Bay Area,Finance,123,0.11,1/11/2023,Series F,United States,565\r\nJumio,SF Bay Area,Security,100,0.06,1/11/2023,Private Equity,United States,205\r\nCoinDCX,Mumbai,Crypto,80,NULL,1/11/2023,Series D,India,244\r\nHashiCorp,SF Bay Area,Security,69,NULL,1/11/2023,Post-IPO,United States,349\r\nEmbark,Boston,Healthcare,41,NULL,1/11/2023,Series B,United States,94\r\nIntrinsic,SF Bay Area,Other,40,0.2,1/11/2023,Acquired,United States,NULL\r\nCitizen,New York City,Consumer,33,NULL,1/11/2023,Series C,United States,133\r\nCarta,SF Bay Area,HR,NULL,0.1,1/11/2023,Series G,United States,1100\r\nLimeade,Seattle,HR,NULL,0.15,1/11/2023,Series C,United States,33\r\nOyster,Charlotte,HR,NULL,NULL,1/11/2023,Series C,United States,224\r\nPaddle,London,Finance,NULL,0.08,1/11/2023,Series D,United Kingdom,293\r\nCoinbase,SF Bay Area,Crypto,950,0.2,1/10/2023,Post-IPO,United States,549\r\nInformatica,SF Bay Area,Data,450,0.07,1/10/2023,Post-IPO,United States,NULL\r\nBlend,SF Bay Area,Finance,340,0.28,1/10/2023,Post-IPO,United States,665\r\nTill Payments,Sydney,Finance,120,NULL,1/10/2023,Series C,Australia,95\r\nConsenSys,New York City,Crypto,100,0.11,1/10/2023,Series D,United States,726\r\nForeScout,SF Bay Area,Security,100,0.1,1/10/2023,Post-IPO,United States,125\r\nThinkific,Vancouver,Education,76,0.19,1/10/2023,Post-IPO,Canada,22\r\nLEAD,Mumbai,Education,60,NULL,1/10/2023,Series E,India,190\r\nParler,Nashville,Consumer,60,0.75,1/10/2023,Series B,United States,36\r\nGoBolt,Toronto,Logistics,55,0.05,1/10/2023,Series C,Canada,178\r\nRelevel,Bengaluru,HR,40,0.2,1/10/2023,NULL,India,NULL\r\nStreamElements,Tel Aviv,Media,40,0.2,1/10/2023,Series B,Israel,111\r\n100 Thieves,Los Angeles,Retail,NULL,NULL,1/10/2023,Series C,United States,120\r\nBeamery,London,HR,NULL,0.12,1/10/2023,Series D,United States,223\r\nCart.com,Austin,Retail,NULL,NULL,1/10/2023,Unknown,United States,383\r\nCitrix,Miami,Infrastructure,NULL,0.15,1/10/2023,Acquired,United States,20\r\nEsper,Seattle,Other,NULL,0.21,1/10/2023,Series A,United States,10\r\nWHOOP,Boston,Fitness,NULL,0.04,1/10/2023,Series F,United States,404\r\nFate Therapeutics,San Diego,Healthcare,315,0.57,1/9/2023,Post-IPO,United States,1200\r\nCentury Therapeutics,Philadelphia,Healthcare,NULL,NULL,1/9/2023,Post-IPO,United States,560\r\nEditas Medicine,Boston,Healthcare,NULL,0.2,1/9/2023,Post-IPO,United States,656\r\nScale AI,SF Bay Area,Data,NULL,0.2,1/9/2023,Series E,United States,602\r\nMinute Media,London,Media,50,0.1,1/8/2023,Series I,United Kingdom,160\r\nWalkMe,SF Bay Area,Other,43,0.03,1/8/2023,Post-IPO,United States,307\r\nHuobi,Beijing,Crypto,275,0.2,1/6/2023,Unknown,China,2\r\nCarbon Health,SF Bay Area,Healthcare,200,NULL,1/6/2023,Series D,United States,522\r\nBounce,Bengaluru,Transportation,40,0.05,1/6/2023,Series D,India,214\r\nAware,Columbus,Security,NULL,NULL,1/6/2023,Series C,United States,88\r\nCareerArc,Los Angeles,HR,NULL,NULL,1/6/2023,Private Equity,United States,30\r\nCreateMe,SF Bay Area,Manufacturing,NULL,NULL,1/6/2023,Unknown,United States,NULL\r\nLantern,Grand Rapids,Retail,NULL,1,1/6/2023,Seed,United States,40\r\nMojo Vision,SF Bay Area,Hardware,NULL,0.75,1/6/2023,Series B,United States,204\r\nSuperRare,Wilmington,Crypto,NULL,0.3,1/6/2023,Series A,United States,9\r\nCue,San Diego,Healthcare,388,NULL,1/5/2023,Post-IPO,United States,999\r\nSoundHound,SF Bay Area,Other,200,0.5,1/5/2023,Post-IPO,United States,326\r\nSocure,Reno,Finance,104,0.19,1/5/2023,Series E,United States,646\r\nGenesis,New York City,Crypto,60,0.3,1/5/2023,Series A,United States,NULL\r\nMoglix,Singapore,Retail,40,0.02,1/5/2023,Series F,Singapore,472\r\nTwitter,SF Bay Area,Consumer,40,NULL,1/5/2023,Post-IPO,United States,12900\r\nEverlane,SF Bay Area,Retail,30,0.17,1/5/2023,Unknown,United States,176\r\nPecan AI,Tel Aviv,Data,30,0.25,1/5/2023,Series C,Israel,116\r\nPersonetics,New York City,Support,30,0.08,1/5/2023,Private Equity,United States,178\r\nTwine Solutions ,Tel Aviv,Hardware,30,0.33,1/5/2023,Unknown,Israel,50\r\nUpScalio,Gurugram,Retail,25,0.15,1/5/2023,Series B,India,62\r\nAttentive,New York City,Marketing,NULL,0.15,1/5/2023,Series E,United States,863\r\nCompass,New York City,Real Estate,NULL,NULL,1/5/2023,Post-IPO,United States,1600\r\nMegaport,Brisbane,Infrastructure,NULL,NULL,1/5/2023,Post-IPO,Australia,98\r\nStitch Fix,SF Bay Area,Retail,NULL,0.2,1/5/2023,Post-IPO,United States,79\r\nTCR2,Boston,Healthcare,NULL,0.4,1/5/2023,Post-IPO,United States,173\r\nAmazon,Seattle,Retail,8000,0.02,1/4/2023,Post-IPO,United States,108\r\nSalesforce,SF Bay Area,Sales,8000,0.1,1/4/2023,Post-IPO,United States,65\r\nAstronomer,Cincinnati,Data,76,0.2,1/4/2023,Series C,United States,282\r\nKaltura,New York City,Media,75,0.11,1/4/2023,Post-IPO,United States,166\r\nAugury,New York City,Manufacturing,20,0.05,1/4/2023,Series E,United States,274\r\nButterfly Network,Boston,Healthcare,NULL,0.25,1/4/2023,Post-IPO,United States,530\r\nVimeo,New York City,Consumer,NULL,0.11,1/4/2023,Post-IPO,United States,450\r\nWyre,SF Bay Area,Crypto,NULL,1,1/4/2023,Unknown,United States,29\r\nPegasystems,Boston,HR,245,0.04,1/3/2023,Post-IPO,United States,NULL\r\nUniphore,SF Bay Area,Support,76,0.1,1/3/2023,Series E,United States,620\r\nHarappa,New Delhi,Education,60,0.3,1/3/2023,Acquired,India,NULL\r\nByteDance,Shanghai,Consumer,NULL,0.1,1/3/2023,Unknown,China,9400\r\nAmdocs,St. Louis,Support,700,0.03,1/2/2023,Post-IPO,United States,NULL\r\nBilibili,Shanghai,Media,NULL,0.3,12/27/2022,Post-IPO,China,3700\r\nOctopus Network,Beau Vallon,Crypto,NULL,0.4,12/27/2022,Series A,Seychelles,8\r\nPayU,Amsterdam,Finance,150,0.06,12/26/2022,Acquired,Netherlands,NULL\r\nElement,London,Other,NULL,0.15,12/25/2022,Series B,United Kingdom,96\r\nWillow,Sydney,Real Estate,99,0.22,12/23/2022,Unknown,Australia,NULL\r\nBack Market,Paris,Retail,93,0.13,12/23/2022,Series E,France,1000\r\nZoopla,London,Real Estate,50,NULL,12/23/2022,Series C,United Kingdom,25\r\nQualcomm,San Diego,Hardware,153,NULL,12/22/2022,Post-IPO,United States,NULL\r\nTuSimple,San Diego,Transportation,350,0.25,12/21/2022,Post-IPO,United States,648\r\nLendis,Berlin,Other,NULL,0.5,12/21/2022,Series A,Germany,90\r\nChope,Singapore,Food,65,0.24,12/20/2022,Series E,Singapore,50\r\nBriza,Toronto,Finance,26,0.4,12/20/2022,Series A,Canada,10\r\nStreetBees,London,Data,NULL,NULL,12/20/2022,Unknown,United Kingdom,63\r\nZhihu,Beijing,Consumer,NULL,0.1,12/20/2022,Series F,China,892\r\nHomebot,Denver,Real Estate,18,0.13,12/19/2022,Acquired,United States,4\r\nHealth IQ,SF Bay Area,Healthcare,NULL,NULL,12/19/2022,Series D,United States,136\r\nXiaomi,Beijing,Consumer,NULL,NULL,12/19/2022,Post-IPO,United States,7400\r\nYourGrocer,Melbourne,Food,NULL,1,12/19/2022,Unknown,Australia,2\r\nTomorrow,Hamburg,Finance,30,0.25,12/16/2022,Unknown,Germany,29\r\nRevelate,Montreal,Data,24,0.3,12/16/2022,Series A,Canada,26\r\n E Inc.,Toronto,Transportation,NULL,NULL,12/16/2022,Post-IPO,Canada,NULL\r\nAutograph,Los Angeles,Crypto,NULL,NULL,12/16/2022,Series B,United States,205\r\nImprobable,London,Other,NULL,0.1,12/16/2022,Unknown,United Kingdom,704\r\nModern Treasury,SF Bay Area,Finance,NULL,0.18,12/16/2022,Series C,United States,183\r\nReach,Calgary,Retail,NULL,0.12,12/16/2022,Series A,Canada,30\r\nSonderMind,Denver,Healthcare,NULL,0.15,12/16/2022,Series C,United States,183\r\nBigCommerce,Austin,Retail,180,0.13,12/15/2022,Post-IPO,United States,224\r\nFreshworks,SF Bay Area,Support,90,0.02,12/15/2022,Post-IPO,United States,484\r\nLeafLink,New York City,Other,80,0.31,12/15/2022,Series C,United States,379\r\nWorkmotion,Berlin,HR,60,0.2,12/15/2022,Series B,United States,76\r\nApollo,SF Bay Area,Product,NULL,0.15,12/15/2022,Series D,United States,183\r\nJD.ID,Jakarta,Retail,200,0.3,12/14/2022,Post-IPO,Indonesia,5100\r\nGoStudent,Vienna,Education,100,NULL,12/14/2022,Series D,Austria,686\r\nQuanergy Systems,SF Bay Area,Transportation,72,NULL,12/14/2022,Post-IPO,United States,175\r\nHeadspace,Los Angeles,Healthcare,50,0.04,12/14/2022,Unknown,United States,215\r\nChowNow,Los Angeles,Food,40,0.1,12/14/2022,Series C,United States,64\r\nLanding,Birmingham,Real Estate,NULL,NULL,12/14/2022,Series C,United States,347\r\nThumbtack,SF Bay Area,Consumer,160,0.14,12/13/2022,Series I,United States,698\r\nEdgio,Phoenix,Infrastructure,95,0.1,12/13/2022,Post-IPO,United States,462\r\nKomodo Health,SF Bay Area,Healthcare,78,0.09,12/13/2022,Series E,United States,514\r\nViant,Los Angeles,Marketing,46,0.13,12/13/2022,Post-IPO,United States,NULL\r\nTaxBit,Salt Lake City,Crypto,NULL,NULL,12/13/2022,Series B,United States,235\r\nPluralsight,Salt Lake City,Education,400,0.2,12/12/2022,Acquired,United States,192\r\nFreshly,Phoenix,Food,329,NULL,12/12/2022,Acquired,United States,107\r\nBalto,St. Louis,Sales,35,NULL,12/12/2022,Series B,United States,51\r\nCaribou,Washington D.C.,Finance,NULL,NULL,12/12/2022,Series C,United States,189\r\nOutschool,SF Bay Area,Education,43,0.25,12/10/2022,Series D,United States,240\r\nXentral,Munich,Product,20,0.1,12/10/2022,Series B,Germany,94\r\nAutobooks,Detroit,Finance,NULL,NULL,12/10/2022,Series C,United States,97\r\nConvene,New York City,Real Estate,NULL,NULL,12/10/2022,Unknown,United States,281\r\nPharmEasy,Mumbai,Healthcare,NULL,NULL,12/10/2022,Unknown,India,1600\r\nPlaytika,Tel Aviv,Consumer,600,0.15,12/9/2022,Post-IPO,Israel,NULL\r\nShare Now,Berlin,Transportation,150,0.36,12/9/2022,Acquired,Germany,NULL\r\nAlice,Sao Paulo,Healthcare,113,0.16,12/9/2022,Series C,Brazil,174\r\nPrimer,London,Finance,85,0.33,12/9/2022,Series B,United Kingdom,73\r\nOneFootball,Berlin,Marketing,62,0.115,12/9/2022,Series D,Germany,442\r\nC2FO,Kansas City,Finance,20,0.02,12/9/2022,Series H,United States,537\r\nBrodmann17,Tel Aviv,Other,NULL,1,12/9/2022,Series A,Israel,25\r\nDigital Surge,Brisbane,Crypto,NULL,1,12/9/2022,Unknown,Australia,NULL\r\nN-able Technologies,Raleigh,Other,NULL,NULL,12/9/2022,Post-IPO,United States,225\r\nZenLedger,Seattle,Crypto,NULL,0.1,12/9/2022,Series B,United States,25\r\nAirtable,SF Bay Area,Product,254,0.2,12/8/2022,Series F,United States,1400\r\nSwiggy,Bengaluru,Food,250,0.03,12/8/2022,Unknown,India,3600\r\nGlints,Singapore,HR,198,0.18,12/8/2022,Series D,Singapore,82\r\nBuser,Sao Paulo,Transportation,160,0.3,12/8/2022,Series C,Brazil,138\r\nBlackLine,Los Angeles,Finance,95,0.05,12/8/2022,Private Equity,United States,220\r\nChrono24,Karlsruhe,Retail,80,NULL,12/8/2022,Series C,Germany,205\r\nOtonomo,Tel Aviv,Transportation,80,0.5,12/8/2022,Post-IPO,Israel,231\r\nTechTarget,Boston,Marketing,60,0.05,12/8/2022,Post-IPO,United States,115\r\nInscripta,Boulder,Healthcare,43,NULL,12/8/2022,Series E,United States,459\r\nCyCognito,SF Bay Area,Security,30,0.15,12/8/2022,Series C,United States,153\r\nArmis,SF Bay Area,Security,25,0.04,12/8/2022,Private Equity,United States,537\r\nBakkt,Atlanta,Crypto,NULL,0.15,12/8/2022,Post-IPO,United States,932\r\nBlue Apron,New York City,Food,NULL,0.1,12/8/2022,Post-IPO,United States,352\r\nFireHydrant,New York City,Infrastructure,NULL,NULL,12/8/2022,Series B,United States,32\r\nLenovo,Raleigh,Hardware,NULL,NULL,12/8/2022,Post-IPO,United States,850\r\nNerdy,St. Louis,Education,NULL,0.17,12/8/2022,Post-IPO,United States,150\r\nVanta,SF Bay Area,Security,NULL,0.14,12/8/2022,Series B,United States,203\r\nVedantu,Bengaluru,Education,385,NULL,12/7/2022,Series E,India,292\r\nLoft,Sao Paulo,Real Estate,312,0.12,12/7/2022,Unknown,Brazil,788\r\nPlaid,SF Bay Area,Finance,260,0.2,12/7/2022,Series D,United States,734\r\nMotive,SF Bay Area,Transportation,237,0.06,12/7/2022,Series F,United States,567\r\nRecur Forever,Miami,Crypto,235,NULL,12/7/2022,Series A,United States,55\r\nRelativity,Chicago,Legal,150,0.1,12/7/2022,Private Equity,United States,125\r\nVoi,Stockholm,Transportation,130,0.13,12/7/2022,Series D,United States,515\r\nIntegral Ad Science,New York City,Marketing,120,0.13,12/7/2022,Acquired,United States,116\r\nHouzz,SF Bay Area,Consumer,95,0.08,12/7/2022,Series E,United States,613\r\nGrover,Berlin,Retail,40,0.1,12/7/2022,Unknown,United States,2300\r\nLev,New York City,Real Estate,30,0.3,12/7/2022,Series B,United States,114\r\nLithic,New York City,Finance,27,0.18,12/7/2022,Series C,United States,115\r\nCircleCI,SF Bay Area,Product,NULL,0.17,12/7/2022,Series F,United States,315\r\nSayurbox,Jakarta,Food,NULL,0.05,12/7/2022,Series C,Indonesia,139\r\nZywave,Milwaukee,Finance,NULL,NULL,12/7/2022,Acquired,United States,NULL\r\nDoma,SF Bay Area,Finance,515,0.4,12/6/2022,Post-IPO,United States,679\r\nIntel,SF Bay Area,Hardware,201,NULL,12/6/2022,Post-IPO,United States,12\r\nBuzzFeed,New York City,Media,180,0.12,12/6/2022,Post-IPO,United States,696\r\nWeedmaps,Los Angeles,Other,175,0.25,12/6/2022,Acquired,United States,NULL\r\nAdobe,SF Bay Area,Marketing,100,NULL,12/6/2022,Post-IPO,United States,2\r\nChipper Cash,SF Bay Area,Finance,50,0.125,12/6/2022,Series C,United States,302\r\nStash,New York City,Finance,32,0.08,12/6/2022,Unknown,United States,480\r\nPerimeter 81,Tel Aviv,Security,20,0.08,12/6/2022,Series C,Israel,165\r\nKoinly,London,Crypto,16,0.14,12/6/2022,Unknown,United Kingdom,NULL\r\nBridgit,Waterloo,Construction,13,0.13,12/6/2022,Series B,Canada,36\r\nFilevine,Salt Lake City,Legal,NULL,NULL,12/6/2022,Series D,United States,226\r\nMoove,Lagos,Transportation,NULL,NULL,12/6/2022,Unknown,Nigeria,630\r\nNextiva,Phoenix,Other,NULL,0.17,12/6/2022,Private Equity,United States,200\r\nOneStudyTeam,Boston,Healthcare,NULL,0.25,12/6/2022,Series D,United States,479\r\nZuora,SF Bay Area,Finance,NULL,0.11,12/6/2022,Post-IPO,United States,647\r\nSwyftx,Brisbane,Crypto,90,0.4,12/5/2022,Unknown,Australia,NULL\r\nAqua Security,Boston,Security,65,0.1,12/5/2022,Series E,United States,265\r\nDataRails,Tel Aviv,Finance,30,0.18,12/5/2022,Series B,Israel,103\r\nElemy,SF Bay Area,Healthcare,NULL,NULL,12/5/2022,Series B,United States,323\r\nRoute,Lehi,Retail,NULL,NULL,12/5/2022,Unknown,United States,481\r\nThinkific,Vancouver,Education,NULL,NULL,12/5/2022,Post-IPO,Canada,22\r\nOYO,Gurugram,Travel,600,NULL,12/3/2022,Series F,India,4000\r\nHealthifyMe,Bengaluru,Fitness,150,NULL,12/3/2022,Series C,India,100\r\nBybit,Singapore,Crypto,NULL,0.3,12/3/2022,Unknown,Singapore,NULL\r\nCognyte,Tel Aviv,Security,100,0.05,12/2/2022,Unknown,Israel,NULL\r\nShareChat,Bengaluru,Consumer,100,NULL,12/2/2022,Unknown,India,1700\r\nPolly,Burlington,Finance,47,0.15,12/2/2022,Series C,United States,184\r\nHomebound,SF Bay Area,Real Estate,NULL,NULL,12/2/2022,Unknown,United States,128\r\nLora DiCarlo,Bend,Consumer,NULL,1,12/2/2022,Unknown,United States,9\r\nCarousell,Singapore,Retail,110,0.1,12/1/2022,Private Equity,Singapore,372\r\nBizzabo,New York City,Marketing,100,0.37,12/1/2022,Series E,United States,194\r\nBloomTech,SF Bay Area,Education,88,0.5,12/1/2022,Unknown,United States,NULL\r\nNetlify,SF Bay Area,Product,48,0.16,12/1/2022,Series D,United States,212\r\nSpringbig,Miami,Sales,37,0.23,12/1/2022,Post-IPO,United States,32\r\nPodium,Lehi,Support,NULL,0.12,12/1/2022,Series D,United States,419\r\nSQZ Biotech,Boston,Healthcare,NULL,0.6,12/1/2022,Post-IPO,United States,229\r\nStrava,SF Bay Area,Fitness,NULL,0.14,12/1/2022,Series F,United States,151\r\nSynlogic,Boston,Healthcare,NULL,0.25,12/1/2022,Post-IPO,United States,321\r\nYapily,London,Finance,NULL,NULL,12/1/2022,Series A,United Kingdom,69\r\nDoorDash,SF Bay Area,Food,1250,0.06,11/30/2022,Post-IPO,United States,2500\r\nKraken,SF Bay Area,Crypto,1100,0.3,11/30/2022,Unknown,United States,134\r\nHappy Money,Los Angeles,Finance,158,0.34,11/30/2022,Series D,United States,191\r\nUla,Jakarta,Retail,134,0.23,11/30/2022,Series B,Indonesia,140\r\nWonder,New York City,Food,130,0.07,11/30/2022,Series B,United States,850\r\nStudySmarter,Berlin,Education,70,NULL,11/30/2022,Series A,Germany,64\r\nGrin,Sacramento,Marketing,60,0.13,11/30/2022,Series B,United States,145\r\nUalá,Buenos Aires,Finance,53,0.03,11/30/2022,Series D,Argentina,544\r\nTeachmint,Bengaluru,Education,45,0.05,11/30/2022,Series B,India,118\r\nEtermax,Buenos Aires,Other,40,NULL,11/30/2022,Unknown,Argentina,NULL\r\nThread,London,Retail,30,0.5,11/30/2022,Acquired,United Kingdom,40\r\nElastic,SF Bay Area,Data,NULL,0.13,11/30/2022,Post-IPO,United States,162\r\nMotional,Boston,Transportation,NULL,NULL,11/30/2022,Unknown,United States,NULL\r\nPinterest,SF Bay Area,Consumer,NULL,NULL,11/30/2022,Post-IPO,United States,1500\r\nSana,Seattle,Healthcare,NULL,0.15,11/30/2022,Series A,United States,700\r\nVenafi,Salt Lake City,Security,NULL,NULL,11/30/2022,Acquired,United States,167\r\nBitso,Mexico City,Crypto,100,NULL,11/29/2022,Series C,Mexico,378\r\nLyst,London,Retail,50,0.25,11/29/2022,Unknown,United Kingdom,144\r\nCoinJar,Melbourne,Crypto,10,0.2,11/29/2022,Unknown,Australia,1\r\nBitfront,SF Bay Area,Crypto,NULL,1,11/29/2022,Unknown,United States,NULL\r\nCodexis,SF Bay Area,Healthcare,NULL,0.18,11/29/2022,Post-IPO,United States,162\r\nFirework,SF Bay Area,Retail,NULL,0.1,11/29/2022,Series B,United States,269\r\nLazerpay,Lagos,Crypto,NULL,NULL,11/29/2022,Unknown,Nigeria,NULL\r\nMessageBird,Amsterdam,Other,NULL,0.31,11/29/2022,Series C,Netherlands,1100\r\nPlerk,Guadalajara,Finance,NULL,0.4,11/29/2022,Series A,Mexico,13\r\nProton.ai,Boston,Sales,NULL,NULL,11/29/2022,Series A,United States,20\r\nInfarm,Berlin,Other,500,0.5,11/28/2022,Series D,Germany,604\r\nWildlife Studios,Sao Paulo,Consumer,300,0.2,11/28/2022,Unknown,Brazil,260\r\nHirect,Bengaluru,Recruiting,200,0.4,11/28/2022,Series A,India,NULL\r\nApplyBoard,Waterloo,Education,90,0.06,11/28/2022,Series D,Canada,483\r\nAjaib,Jakarta,Finance,67,0.08,11/28/2022,Unknown,Indonesia,245\r\nCandy Digital,New York City,Crypto,33,0.33,11/28/2022,Series A,United States,100\r\nResearchGate,Berlin,Other,25,0.1,11/28/2022,Series D,Germany,87\r\nBlockFi,New York City,Crypto,NULL,1,11/28/2022,Series E,United States,1000\r\nFutureLearn,London,Education,NULL,NULL,11/28/2022,Unknown,United Kingdom,50\r\nInspectify,Seattle,Real Estate,NULL,NULL,11/28/2022,Series A,United States,11\r\nLedn,Toronto,Crypto,NULL,NULL,11/28/2022,Series B,Canada,103\r\nNCX,SF Bay Area,Energy,NULL,0.4,11/28/2022,Series B,United States,78\r\nChange Invest,Amsterdam,Finance,NULL,0.24,11/27/2022,Unknown,Netherlands,22\r\nZilch,London,Finance,NULL,NULL,11/26/2022,Unknown,United Kingdom,389\r\nVerSe Innovation,Bengaluru,Media,150,0.05,11/25/2022,Series J,India,1700\r\nCarwow,London,Transportation,70,0.2,11/25/2022,Unknown,United Kingdom,157\r\nVendease,Lagos,Food,27,0.09,11/25/2022,Series A,Nigeria,43\r\nLemon,Buenos Aires,Crypto,100,0.38,11/24/2022,Series A,Argentina,17\r\nQuidax,Lagos,Crypto,20,0.2,11/24/2022,Unknown,Nigeria,3\r\nMenulog,Sydney,Food,NULL,NULL,11/24/2022,Acquired,Australia,NULL\r\nUtopia Music,Zug,Media,NULL,NULL,11/24/2022,Series B,Switzerland,NULL\r\nAssure,Salt Lake City,Finance,NULL,1,11/23/2022,Seed,United States,2\r\nGoodGood,Toronto,Retail,NULL,1,11/23/2022,Seed,Canada,6\r\nSWVL,Cairo,Transportation,NULL,0.5,11/23/2022,Post-IPO,Egypt,264\r\nWestern Digital,SF Bay Area,Hardware,251,NULL,11/22/2022,Post-IPO,United States,900\r\nSIRCLO,Jakarta,Retail,160,0.08,11/22/2022,Series B,Indonesia,92\r\nTrax,Singapore,Retail,80,0.08,11/22/2022,Series E,Singapore,1000\r\nFlash Coffee,Singapore,Food,NULL,NULL,11/22/2022,Series B,Singapore,57\r\nNatera,SF Bay Area,Healthcare,NULL,NULL,11/22/2022,Post-IPO,United States,809\r\nRapyd,Tel Aviv,Finance,NULL,NULL,11/22/2022,Unknown,Israel,770\r\nJumia,Lagos,Retail,900,0.2,11/21/2022,Post-IPO,Nigeria,1200\r\nKitopi,Dubai,Food,93,0.1,11/21/2022,Series C,United States,804\r\nDevo,Boston,Security,NULL,0.15,11/21/2022,Series F,United States,481\r\nGloriFi,Dallas,Finance,NULL,1,11/21/2022,Unknown,United States,NULL\r\nZomato,Gurugram,Food,100,0.04,11/19/2022,Series J,India,914\r\nCarvana,Phoenix,Transportation,1500,0.08,11/18/2022,Post-IPO,United States,1600\r\nNuro,SF Bay Area,Transportation,300,0.2,11/18/2022,Series D,United States,2100\r\nSynthego,SF Bay Area,Healthcare,105,0.2,11/18/2022,Series E,United States,459\r\nSplyt,London,Transportation,57,NULL,11/18/2022,Series B,United Kingdom,34\r\nCapitolis,New York City,Finance,NULL,0.25,11/18/2022,Series D,United States,281\r\nKavak,Sao Paulo,Transportation,NULL,NULL,11/18/2022,Series E,Brazil,1600\r\nMetaplex,Chicago,Crypto,NULL,NULL,11/18/2022,Unknown,United States,NULL\r\nRuangguru,Jakarta,Education,NULL,NULL,11/18/2022,Unknown,Indonesia,205\r\nStoryBlocks,Washington D.C.,Media,NULL,0.25,11/18/2022,Acquired,United States,18\r\nUnchained Capital,Austin,Crypto,NULL,0.15,11/18/2022,Series A,United States,33\r\nRoku,SF Bay Area,Media,200,0.07,11/17/2022,Post-IPO,United States,208\r\nOrchard,New York City,Real Estate,180,NULL,11/17/2022,Series D,United States,472\r\nHomepoint,Phoenix,Real Estate,113,NULL,11/17/2022,Post-IPO,United States,NULL\r\nJuni,Gothenburg,Finance,72,0.33,11/17/2022,Unknown,Sweden,281\r\nChili Piper,New York City,Sales,58,NULL,11/17/2022,Series B,United States,54\r\nCapitolis,New York City,Finance,37,0.25,11/17/2022,Series D,United States,281\r\nTealBook,Toronto,Other,34,0.19,11/17/2022,Series B,Canada,73\r\nKoho,Toronto,Finance,15,0.04,11/17/2022,Series D,Canada,278\r\n&Open,Dublin,Marketing,9,0.09,11/17/2022,Series A,Ireland,35\r\nKandji,San Diego,Other,NULL,0.17,11/17/2022,Series C,United States,188\r\nMorning Brew,New York City,Media,NULL,0.14,11/17/2022,Acquired,United States,NULL\r\nSymend,Calgary,Other,NULL,0.13,11/17/2022,Series C,Canada,148\r\nAmazon,Seattle,Retail,10000,0.03,11/16/2022,Post-IPO,United States,108\r\nCisco,SF Bay Area,Infrastructure,4100,0.05,11/16/2022,Post-IPO,United States,2\r\nTwiga,Nairobi,Food,211,0.21,11/16/2022,Series C,Kenya,157\r\nWayflyer,Dublin,Marketing,200,0.4,11/16/2022,Unknown,Ireland,889\r\nSimilarWeb,New York City,Other,120,0.1,11/16/2022,Post-IPO,United States,235\r\nSalsify,Boston,Retail,90,0.11,11/16/2022,Series F,United States,452\r\nLokalise,Dover,Other,76,0.23,11/16/2022,Series B,United States,56\r\nYotpo,New York City,Marketing,70,0.09,11/16/2022,Unknown,United States,436\r\nPear Therapeutics,Boston,Healthcare,59,0.22,11/16/2022,Post-IPO,United States,409\r\nD2L,Waterloo,Education,NULL,0.05,11/16/2022,Post-IPO,Canada,168\r\nDance,Berlin,Transportation,NULL,0.16,11/16/2022,Unknown,Germany,63\r\nHomeward,Austin,Real Estate,NULL,0.25,11/16/2022,Unknown,United States,501\r\nHopin,London,Other,NULL,0.17,11/16/2022,Series D,United Kingdom,1000\r\nInfogrid,London,Other,NULL,NULL,11/16/2022,Series A,United Kingdom,15\r\nKite,SF Bay Area,Product,NULL,1,11/16/2022,Series A,United States,21\r\nUiPath,New York City,Data,241,0.06,11/15/2022,Post-IPO,United States,2000\r\nAsana,SF Bay Area,Other,180,0.09,11/15/2022,Post-IPO,United States,453\r\nOwnBackup,New York City,Security,170,0.17,11/15/2022,Series E,United States,507\r\nDeliveroo Australia,Melbourne,Food,120,1,11/15/2022,Post-IPO,Australia,1700\r\nProductboard,SF Bay Area,Product,100,0.2,11/15/2022,Series D,United States,NULL\r\nProperly,Toronto,Real Estate,71,NULL,11/15/2022,Series B,Canada,154\r\nProtocol,SF Bay Area,Media,60,1,11/15/2022,Acquired,United States,NULL\r\nJimdo,Hamburg,Other,50,0.16,11/15/2022,Unknown,Germany,28\r\nThe Zebra,Austin,Finance,50,NULL,11/15/2022,Series D,United States,256\r\nViber,Luxembourg,Consumer,45,0.08,11/15/2022,Acquired,Luxembourg,NULL\r\nCaptivateIQ,SF Bay Area,Sales,31,0.1,11/15/2022,Series C,United States,164\r\nApollo Insurance,Vancouver,Finance,NULL,0.25,11/15/2022,Series B,Canada,11\r\nNirvana Money,Miami,Finance,NULL,1,11/15/2022,Unknown,United States,NULL\r\nOatly,Malmö,Food,NULL,NULL,11/15/2022,Post-IPO,Sweden,441\r\nOfferUp,Seattle,Retail,NULL,0.19,11/15/2022,Unknown,United States,381\r\nOutside,Boulder,Media,NULL,0.12,11/15/2022,Series B,United States,174\r\nRubicon Technologies,Lexington,Other,NULL,0.11,11/15/2022,Post-IPO,United States,382\r\nTencent,Shenzen,Consumer,NULL,NULL,11/15/2022,Post-IPO,China,12600\r\nTypeform,Barcelona,Marketing,NULL,NULL,11/15/2022,Series C,Spain,187\r\nWhispir,Melbourne,Other,NULL,0.3,11/15/2022,Post-IPO,Australia,68\r\nIllumina,San Diego,Healthcare,500,0.05,11/14/2022,Post-IPO,United States,28\r\nSema4,Stamford,Healthcare,500,NULL,11/14/2022,Post-IPO,United States,791\r\niFit,Logan,Fitness,300,0.2,11/14/2022,Private Equity,United States,200\r\nRibbon,New York City,Real Estate,170,0.85,11/14/2022,Series C,United States,405\r\nPipedrive,Tallinn,Sales,143,0.15,11/14/2022,Private Equity,Estonia,90\r\nIntercom,SF Bay Area,Support,124,0.13,11/14/2022,Series D,United States,240\r\nScience 37 ,Los Angeles,Healthcare,90,NULL,11/14/2022,Post-IPO,United States,347\r\nPear Therapeutics,Boston,Healthcare,59,0.22,11/14/2022,Post-IPO,United States,409\r\nCardlytics,Atlanta,Marketing,51,NULL,11/14/2022,Post-IPO,United States,212\r\nCloudinary,SF Bay Area,Media,40,0.08,11/14/2022,Unknown,United States,100\r\nNestcoin,Lagos,Crypto,30,NULL,11/14/2022,Seed,Nigeria,6\r\nGokada,Lagos,Transportation,20,NULL,11/14/2022,Unknown,Nigeria,12\r\nShopee,Jakarta,Food,NULL,NULL,11/14/2022,Unknown,Indonesia,NULL\r\nTricida,SF Bay Area,Healthcare,NULL,0.57,11/14/2022,Post-IPO,United States,624\r\nVeev,SF Bay Area,Real Estate,100,0.3,11/11/2022,Series D,United States,597\r\nForto,Berlin,Logistics,60,0.08,11/11/2022,Series D,United States,593\r\nChipax,Santiago,Finance,NULL,NULL,11/11/2022,Seed,Chile,2\r\nJuniper,Atlanta,Marketing,NULL,NULL,11/11/2022,Acquired,United States,NULL\r\nOfferpad,Phoenix,Real Estate,NULL,0.07,11/11/2022,Post-IPO,United States,355\r\nGoTo Group,Jakarta,Transportation,1300,0.12,11/10/2022,Post-IPO,Indonesia,1300\r\nJuul,SF Bay Area,,400,0.3,11/10/2022,Unknown,United States,1500\r\nBlend,SF Bay Area,Finance,100,0.06,11/10/2022,Post-IPO,United States,665\r\nInfluxData,SF Bay Area,Data,65,0.27,11/10/2022,Series D,United States,119\r\nCoinbase,SF Bay Area,Crypto,60,NULL,11/10/2022,Post-IPO,United States,549\r\nSoundHound,SF Bay Area,Other,45,0.1,11/10/2022,Post-IPO,United States,326\r\nWistia,Boston,Marketing,40,NULL,11/10/2022,Unknown,United States,18\r\nOcavu,Lehi,Crypto,20,0.48,11/10/2022,Series A,United States,11\r\nAvast,Phoenix,Security,NULL,0.25,11/10/2022,Unknown,United States,NULL\r\nReforge,SF Bay Area,Education,NULL,NULL,11/10/2022,Series B,United States,81\r\nSendCloud,Eindhoven,Logistics,NULL,0.1,11/10/2022,Series C,United States,200\r\nVoly,Sydney,Food,NULL,NULL,11/10/2022,Seed,Australia,13\r\nWavely,SF Bay Area,HR,NULL,1,11/10/2022,Unknown,United States,NULL\r\nZenBusiness,Austin,Other,NULL,NULL,11/10/2022,Series C,United States,277\r\nMeta,SF Bay Area,Consumer,11000,0.13,11/9/2022,Post-IPO,United States,26000\r\nRedfin,Seattle,Real Estate,862,0.13,11/9/2022,Post-IPO,United States,320\r\nFlyhomes,Seattle,Real Estate,300,0.4,11/9/2022,Series C,United States,310\r\nAvantStay,Los Angeles,Travel,144,0.22,11/9/2022,Private Equity,United States,686\r\nRoot Insurance,Columbus,Finance,137,0.2,11/9/2022,Post-IPO,United States,527\r\nLiftoff,SF Bay Area,Marketing,130,0.15,11/9/2022,Acquired,United States,6\r\nCameo,Chicago,Consumer,80,NULL,11/9/2022,Unknown,United States,165\r\nPlum,Bengaluru,Healthcare,36,0.1,11/9/2022,Series A,India,20\r\nKabam,SF Bay Area,Consumer,35,0.07,11/9/2022,Acquired,United States,244\r\nHighRadius,Bengaluru,Finance,25,NULL,11/9/2022,Series C,India,475\r\nNamogoo,Tel Aviv,Marketing,25,0.15,11/9/2022,Series C,United States,69\r\nAmobee,SF Bay Area,Marketing,NULL,NULL,11/9/2022,Acquired,United States,72\r\nCloudFactory,Nairobi,Data,NULL,0.12,11/9/2022,Private Equity,Kenya,78\r\nCoursera,SF Bay Area,Education,NULL,NULL,11/9/2022,Post-IPO,United States,458\r\nFaze Medicines,Boston,Healthcare,NULL,1,11/9/2022,Series B,United States,81\r\nRingCentral,SF Bay Area,Other,NULL,0.1,11/9/2022,Post-IPO,United States,44\r\nSpotify,Stockholm,Media,NULL,NULL,11/9/2022,Post-IPO,Sweden,2100\r\nEverBridge,Boston,Other,200,NULL,11/8/2022,Post-IPO,United States,476\r\nRepertoire Immune Medicines,Boston,Healthcare,65,0.5,11/8/2022,Series B,United States,257\r\nAstra,SF Bay Area,Aerospace,NULL,0.16,11/8/2022,Post-IPO,United States,300\r\nBeat,Athens,Transportation,NULL,NULL,11/8/2022,Series B,Greece,6\r\nNanoString,Seattle,Healthcare,NULL,0.1,11/8/2022,Post-IPO,United States,731\r\nSADA,Los Angeles,Other,NULL,0.11,11/8/2022,Acquired,United States,NULL\r\nSalesforce,SF Bay Area,Sales,1000,0.01,11/7/2022,Post-IPO,United States,65\r\nUnacademy,Bengaluru,Education,350,0.1,11/7/2022,Series H,India,838\r\nZendesk,SF Bay Area,Support,350,0.05,11/7/2022,Acquired,United States,85\r\nDock,Sao Paulo,Finance,190,0.12,11/7/2022,Private Equity,Brazil,280\r\nCode42,Minneapolis,Security,NULL,0.15,11/7/2022,Unknown,United States,137\r\nDomino Data Lab,SF Bay Area,Data,NULL,0.25,11/7/2022,Series F,United States,223\r\nVaronis,New York City,Security,110,0.05,11/6/2022,Post-IPO,United States,30\r\nBrainly,Krakow,Education,25,NULL,11/6/2022,Series D,Poland,148\r\nPractically,Hyderabad,Education,NULL,NULL,11/6/2022,Unknown,India,14\r\nTwitter,SF Bay Area,Consumer,3700,0.5,11/4/2022,Post-IPO,United States,12900\r\nUdaan,Bengaluru,Retail,350,NULL,11/4/2022,Unknown,India,1500\r\nPlanetly,Berlin,Other,200,1,11/4/2022,Acquired,Germany,5\r\nKoinWorks,Jakarta,Finance,70,0.08,11/4/2022,Unknown,Indonesia,180\r\nExodus,Nebraska City,Crypto,59,0.22,11/4/2022,Unknown,United States,60\r\nBenitago Group,New York City,Retail,NULL,0.14,11/4/2022,Series A,United States,380\r\nMythical Games,Los Angeles,Crypto,NULL,0.1,11/4/2022,Series C,United States,260\r\nStripe,SF Bay Area,Finance,1000,0.14,11/3/2022,Series H,United States,2300\r\nLyft,SF Bay Area,Transportation,700,0.13,11/3/2022,Post-IPO,United States,4900\r\nLendingTree,Charlotte,Finance,200,NULL,11/3/2022,Post-IPO,United States,NULL\r\nPleo,Copenhagen,Finance,150,0.15,11/3/2022,Series C,United States,428\r\nDelivery Hero,Berlin,Food,100,NULL,11/3/2022,Post-IPO,Germany,8300\r\nShippo,SF Bay Area,Logistics,60,0.2,11/3/2022,Series E,United States,154\r\nAffirm,SF Bay Area,Finance,NULL,0.01,11/3/2022,Post-IPO,United States,1500\r\nCloudKitchens,Los Angeles,Real Estate,NULL,NULL,11/3/2022,Unknown,United States,1300\r\nLiveRamp,SF Bay Area,Marketing,NULL,0.1,11/3/2022,Post-IPO,United States,16\r\nProvi,Chicago,Food,NULL,NULL,11/3/2022,Series C,United States,150\r\nRubius,Boston,Healthcare,NULL,0.82,11/3/2022,Post-IPO,United States,445\r\nSnapdocs,SF Bay Area,Real Estate,NULL,0.15,11/3/2022,Series D,United States,253\r\nStudio,SF Bay Area,Education,NULL,NULL,11/3/2022,Series B,United States,50\r\nOpendoor,SF Bay Area,Real Estate,550,0.18,11/2/2022,Post-IPO,United States,1900\r\nChime,SF Bay Area,Finance,156,0.12,11/2/2022,Series G,United States,2300\r\nChargebee,SF Bay Area,Finance,142,0.1,11/2/2022,Series H,United States,468\r\nDapper Labs,Vancouver,Crypto,134,0.22,11/2/2022,Series D,United States,607\r\nCheckmarx,Tel Aviv,Security,100,0.1,11/2/2022,Series C,Israel,92\r\nSmava,Berlin,Finance,100,0.15,11/2/2022,Unknown,Germany,188\r\nIron Ox,SF Bay Area,Food,50,0.5,11/2/2022,Series C,United States,103\r\nDigital Currency Gruop,Stamford,Crypto,10,0.13,11/2/2022,Unknown,United States,NULL\r\nBitMEX,Non-U.S.,Crypto,NULL,0.3,11/2/2022,Seed,Seychelles,0\r\nSignicat,Trondheim,Security,NULL,NULL,11/2/2022,Acquired,Norway,8\r\nArgo AI,SF Bay Area,Transportation,259,NULL,11/1/2022,Unknown,United States,3600\r\nBooking.com,Grand Rapids,Travel,226,NULL,11/1/2022,Acquired,United States,NULL\r\nOracle,SF Bay Area,Other,200,NULL,11/1/2022,Post-IPO,United States,NULL\r\nUpstart,SF Bay Area,Finance,140,0.07,11/1/2022,Post-IPO,United States,144\r\nGem,SF Bay Area,Recruiting,100,0.33,11/1/2022,Series C,United States,148\r\nOda,Oslo,Food,70,0.18,11/1/2022,Unknown,Sweden,377\r\nOda,Oslo,Food,70,0.18,11/1/2022,Unknown,Norway,477\r\nOda,Oslo,Food,70,0.06,11/1/2022,Unknown,Norway,479\r\nHootsuite,Vancouver,Marketing,50,0.05,11/1/2022,Series C,Canada,300\r\nDrop,Toronto,Marketing,24,NULL,11/1/2022,Series B,Canada,56\r\nTapps Games,Sao Paulo,Consumer,10,NULL,11/1/2022,Unknown,Brazil,NULL\r\nBrightline,SF Bay Area,Healthcare,NULL,0.2,11/1/2022,Series C,United States,212\r\nHelp Scout,Boston,Support,NULL,NULL,11/1/2022,Series B,United States,28\r\nKry,Stockholm,Healthcare,300,0.1,10/31/2022,Series D,Sweden,568\r\nNotarize,Boston,Legal,60,NULL,10/31/2022,Series D,United States,213\r\nEquityZen,New York City,Finance,30,0.27,10/31/2022,Series B,United States,11\r\nEquitybee,SF Bay Area,Finance,25,0.2,10/31/2022,Series B,United States,85\r\nDukaan,Bengaluru,Retail,23,NULL,10/31/2022,Series A,India,17\r\nAmazon,Seattle,Retail,150,NULL,10/28/2022,Post-IPO,United States,108\r\nFifth Season,Pittsburgh,Food,100,1,10/28/2022,Series B,United States,35\r\nAdvata,Seattle,Healthcare,32,0.21,10/28/2022,NULL,United States,NULL\r\nSpringlane,Düsseldorf,Food,NULL,0.35,10/28/2022,Series C,Germany,11\r\nRenoRun,Montreal,Construction,210,0.43,10/27/2022,Series B,Canada,163\r\nRecharge,Los Angeles,Finance,84,0.17,10/27/2022,Series B,United States,277\r\nLattice,SF Bay Area,HR,13,NULL,10/27/2022,Series F,United States,328\r\nGoNuts,Mumbai,Media,NULL,1,10/27/2022,Seed,India,NULL\r\nSpreetail,Austin,Retail,NULL,NULL,10/27/2022,NULL,United States,NULL\r\nMindBody,San Luis Obispo,Fitness,400,0.15,10/26/2022,Post-IPO,United States,114\r\nZillow,Seattle,Real Estate,300,0.05,10/26/2022,Post-IPO,United States,97\r\nCybereason,Boston,Security,200,0.17,10/26/2022,Series F,United States,750\r\nArgo AI,Pittsburgh,Transportation,173,NULL,10/26/2022,Unknown,United States,3600\r\nGoFundMe,SF Bay Area,Finance,94,0.12,10/26/2022,Series A,United States,NULL\r\nCarbon,SF Bay Area,Hardware,NULL,NULL,10/26/2022,Series E,United States,683\r\nFundbox,SF Bay Area,Finance,150,0.42,10/25/2022,Series D,United States,553\r\nEmbroker,SF Bay Area,Finance,30,0.12,10/25/2022,Series C,United States,142\r\nVee,Tel Aviv,HR,17,0.5,10/25/2022,Seed,Israel,15\r\nCallisto Media,SF Bay Area,Media,NULL,0.35,10/25/2022,Series D,United States,NULL\r\nConvoy,Seattle,Logistics,NULL,NULL,10/25/2022,Series E,United States,1100\r\nPhilips,Amsterdam,Healthcare,4000,0.05,10/24/2022,Post-IPO,Netherlands,NULL\r\nCerebral,SF Bay Area,Healthcare,400,0.2,10/24/2022,Series C,United States,462\r\nSnyk,Boston,Security,198,0.14,10/24/2022,Series F,United States,849\r\nMcMakler,Berlin,Real Estate,100,NULL,10/24/2022,Unknown,Germany,214\r\nUnico,Sao Paulo,Other,50,0.04,10/24/2022,Series D,Brazil,336\r\nOrCam,Jerusalem,Healthcare,62,0.16,10/23/2022,Unknown,Israel,86\r\nAntidote Health,Tel Aviv,Healthcare,23,0.38,10/23/2022,Unknown,Israel,36\r\nKhoros,Austin,Sales,120,0.1,10/21/2022,Private Equity,United States,138\r\nF5,Seattle,Security,100,0.01,10/21/2022,Post-IPO,United States,NULL\r\nElinvar,Berlin,Finance,43,0.33,10/21/2022,Unknown,Germany,30\r\nSynapsica,New Delhi,Healthcare,30,0.3,10/21/2022,Series A,India,4\r\nVolta,SF Bay Area,Transportation,NULL,0.54,10/21/2022,Post-IPO,United States,575\r\nHotmart,Belo Horizonte,Marketing,227,0.12,10/20/2022,Series C,Brazil,127\r\nZeus Living,SF Bay Area,Real Estate,64,0.46,10/20/2022,Series C,United States,151\r\nLoom,SF Bay Area,Product,23,0.11,10/20/2022,Series C,United States,203\r\nSales Boomerang,Baltimore,Sales,20,NULL,10/20/2022,Private Equity,United States,5\r\nArrival,London,Transportation,NULL,NULL,10/20/2022,Post-IPO,United Kingdom,629\r\nRoofstock,SF Bay Area,Real Estate,NULL,0.2,10/20/2022,Series E,United States,365\r\nStarry,Boston,Other,NULL,0.5,10/20/2022,Post-IPO,United States,260\r\nGopuff,Philadelphia,Food,250,NULL,10/19/2022,Series H,United States,3400\r\nAtoB,SF Bay Area,Finance,32,0.3,10/19/2022,Series B,United States,177\r\nInfoSum,London,Security,20,0.12,10/19/2022,Series B,United Kingdom,88\r\nClever Real Estate,St. Louis,Real Estate,NULL,NULL,10/19/2022,Series B,United States,13\r\nCollibra,Brussels,Data,NULL,NULL,10/19/2022,Series G,Belgium,596\r\nSide,SF Bay Area,Real Estate,NULL,NULL,10/19/2022,Unknown,United States,313\r\nFaire,SF Bay Area,Retail,84,0.07,10/18/2022,Series G,United States,1700\r\nLeafly,Seattle,Retail,56,0.21,10/18/2022,Post-IPO,United States,71\r\nAda Health,Berlin,Healthcare,50,NULL,10/17/2022,Series B,Germany,189\r\nTiendanube,Buenos Aires,Marketing,50,0.05,10/17/2022,Unknown,Argentina,NULL\r\nMicrosoft,Seattle,Other,NULL,NULL,10/17/2022,Post-IPO,United States,1\r\nNuri,Berlin,Crypto,NULL,1,10/17/2022,Series B,Germany,42\r\nFlipboard,SF Bay Area,Media,24,0.21,10/16/2022,Unknown,United States,235\r\nHuawei,Shenzen,Hardware,NULL,NULL,10/16/2022,Unknown,China,NULL\r\nClear Capital,Reno,Real Estate,378,0.27,10/14/2022,Unknown,United States,NULL\r\nBeyond Meat,Los Angeles,Food,200,0.19,10/14/2022,Post-IPO,United States,122\r\nFlux Systems,London,Finance,NULL,1,10/14/2022,Unknown,United Kingdom,9\r\nQin1,Noida,Education,NULL,1,10/14/2022,Seed,India,NULL\r\nSalesforce,SF Bay Area,Sales,90,NULL,10/13/2022,Post-IPO,United States,65\r\nPlaydots,New York City,Consumer,65,1,10/13/2022,Acquired,United States,10\r\nExtraHop,Seattle,Security,NULL,NULL,10/13/2022,Series C,United States,61\r\nByju's,Bengaluru,Education,2500,0.05,10/12/2022,Private Equity,India,5500\r\n6sense,SF Bay Area,Sales,150,0.1,10/12/2022,Series E,United States,426\r\nSinch,Stockholm,Other,150,NULL,10/12/2022,Post-IPO,Sweden,1500\r\nFrontRow,Bengaluru,Education,130,0.75,10/12/2022,Series A,India,17\r\nNoom,New York City,Healthcare,500,0.1,10/11/2022,Series F,United States,657\r\nMX,Lehi,Finance,200,NULL,10/11/2022,Series C,United States,450\r\nBrex,SF Bay Area,Finance,136,0.11,10/11/2022,Series D,United States,1500\r\nPacaso,SF Bay Area,Real Estate,100,0.3,10/11/2022,Series C,United States,217\r\nSketch,The Hague,Other,80,NULL,10/11/2022,Series A,Netherlands,20\r\nUdacity,SF Bay Area,Education,55,0.13,10/11/2022,Unknown,United States,235\r\nLinkfire,Copenhagen,Marketing,35,0.35,10/11/2022,Seed,Denmark,2\r\nEmitwise,London,Energy,NULL,NULL,10/11/2022,Series A,United Kingdom,16\r\nGSR,Hong Kong,Crypto Currency,NULL,NULL,10/11/2022,Unknown,Hong Kong,NULL\r\nVanHack,Vancouver,HR,NULL,NULL,10/11/2022,Seed,United States,NULL\r\nHelloFresh,SF Bay Area,Food,611,NULL,10/10/2022,Post-IPO,United States,367\r\nMomentive,SF Bay Area,Marketing,180,0.11,10/10/2022,Post-IPO,United States,1100\r\nPavilion Data,SF Bay Area,Infrastructure,96,0.96,10/10/2022,Series D,United States,103\r\nRedesign Health,New York City,Healthcare,67,0.2,10/10/2022,Series C,United States,315\r\nNyriad,Austin,Infrastructure,NULL,0.33,10/10/2022,Unknown,United States,58\r\nBioMarin,SF Bay Area,Healthcare,120,0.04,10/7/2022,Post-IPO,United States,585\r\nRev.com,Austin,Data,85,NULL,10/7/2022,Series D,United States,30\r\nCrypto.com,Singapore,Crypto,2000,0.3,10/6/2022,Unknown,Singapore,156\r\nPeloton,New York City,Fitness,500,0.12,10/6/2022,Post-IPO,United States,1900\r\nLanding,Birmingham,Real Estate,110,NULL,10/6/2022,Series C,United States.,347\r\nTurnitin,SF Bay Area,Education,51,0.05,10/6/2022,Acquired,United States,NULL\r\nImpossible Foods,SF Bay Area,Food,50,0.06,10/6/2022,Series H,United States,1900\r\nAtome,Singapore,Finance,NULL,NULL,10/6/2022,Unknown,Singapore,645\r\nFirst AML,Auckland,Finance,NULL,NULL,10/6/2022,Series B,New Zealand,29\r\nForesight Insurance,SF Bay Area,Finance,NULL,0.4,10/6/2022,Series B,United States,58\r\nSpotify,Stockholm,Media,NULL,NULL,10/6/2022,Post-IPO,Sweden,2100\r\nBuilt In,Chicago,Recruiting,50,0.25,10/5/2022,Series C,United States,29\r\nTwinStrand,Seattle,Healthcare,NULL,0.5,10/5/2022,Series B,United States,73\r\n8x8,SF Bay Area,Support,200,0.09,10/4/2022,Post-IPO,United States,253\r\nHomie,Salt Lake City,Real Estate,40,0.13,10/4/2022,Series B,United States,35\r\nFivetran,SF Bay Area,Data,NULL,0.05,10/4/2022,Series D,United States,NULL\r\nXendit,Jakarta,Finance,NULL,0.05,10/4/2022,Series D,Indonesia,534\r\nZoomo,Sydney,Transportation,NULL,0.16,10/4/2022,Series B,Australia,105\r\nActiveCampaign,Chicago,Marketing,NULL,0.15,10/3/2022,Series C,United States,360\r\nTempo,SF Bay Area,Fitness,NULL,NULL,10/3/2022,Series C,United States,298\r\nWazirX,Mumbai,Crypto,60,0.4,10/2/2022,Acquired,India,NULL\r\nSpin,SF Bay Area,Transportation,78,0.1,9/30/2022,Acquired,United States,8\r\nCarsome,Kuala Lumpur,Transportation,NULL,0.1,9/30/2022,Series E,Malaysia,607\r\nPastel,SF Bay Area,Food,NULL,1,9/30/2022,Unknown,United States,NULL\r\nTruepill,SF Bay Area,Healthcare,NULL,NULL,9/30/2022,Series D,United States,255\r\nWestwing,Munich,Retail,125,NULL,9/29/2022,Post-IPO,Germany,237\r\nMux,SF Bay Area,Infrastructure,40,0.2,9/29/2022,Series D,United States,173\r\nSolarisbank,Berlin,Finance,NULL,0.1,9/29/2022,Unknown,Germany,385\r\nZenjob,Berlin,HR,NULL,NULL,9/29/2022,Series D,Germany,107\r\nDocuSign,SF Bay Area,Sales,671,0.09,9/28/2022,Post-IPO,United States,536\r\nFront,SF Bay Area,Support,NULL,NULL,9/28/2022,Series D,United States,203\r\nVolta,SF Bay Area,Transportation,NULL,0.1,9/28/2022,Post-IPO,United States,575\r\nDivvy Homes,SF Bay Area,Real Estate,40,0.12,9/27/2022,Series B,United States,180\r\nGraphcore,Bristol,Data,NULL,NULL,9/27/2022,Unknown,United Kingdom,692\r\nInstacart,SF Bay Area,Food,NULL,NULL,9/24/2022,Unknown,United States,2900\r\nKonfio,Mexico City,Finance,180,NULL,9/23/2022,Series E,United States,706\r\nMoss,Berlin,Fin-Tech,70,0.15,9/23/2022,Series B,Germany,150\r\nFoxtrot,Chicago,Food,26,0.035,9/23/2022,Series C,United States,166\r\nTruiloo,Vancouver,Security,24,0.05,9/23/2022,Series D,Canada,474\r\nPesto,SF Bay Area,Other,NULL,1,9/23/2022,Seed,United States,6\r\nNYDIG,New York City,Crypto,110,0.33,9/22/2022,Private Equity,United States,1400\r\nKlarna,Stockholm,Finance,100,NULL,9/22/2022,Unknown,Sweden,3700\r\nMade.com,London,Retail,NULL,0.35,9/22/2022,Series D,United Kingdom,136\r\nKitty Hawk,SF Bay Area,Aerospace,100,1,9/21/2022,Unknown,United States,1\r\nCandidate Labs,SF Bay Area,HR,NULL,NULL,9/21/2022,Seed,United States,5\r\nCompass,New York City,Real Estate,271,NULL,9/20/2022,Post-IPO,United States,1600\r\nCurative,Los Angeles,Healthcare,109,NULL,9/20/2022,Seed,United States,8\r\nAda,Toronto,Support,78,0.16,9/20/2022,Series C,Canada,190\r\n99,Sao Paulo,Transportation,75,0.02,9/20/2022,Acquired,Brazil,244\r\nOuster,SF Bay Area,Transportation,NULL,0.1,9/20/2022,Post-IPO,United States,282\r\nZappos,Las Vegas,Retail,NULL,0.04,9/20/2022,Acquired,United States,62\r\nOla,Bengaluru,Transportation,200,NULL,9/19/2022,Series J,India,5000\r\nVesalius Therapeutics,Boston,Healthcare,29,0.43,9/19/2022,Series B,United States,75\r\nVideoAmp,Los Angeles,Marketing,NULL,0.02,9/19/2022,Series F,United States,456\r\nShopee,Jakarta,Food,NULL,NULL,9/18/2022,Unknown,Indonesia,NULL\r\nClear,Bengaluru,Finance,190,0.2,9/16/2022,Series C,India,140\r\nTrueLayer,London,Finance,40,0.1,9/16/2022,Series E,United States,271\r\nLivePerson,New York City,Support,193,0.11,9/15/2022,Post-IPO,United States,42\r\nAcast,Stockholm,Media,70,0.15,9/15/2022,Post-IPO,Sweden,126\r\nWorkRamp,SF Bay Area,HR,35,0.2,9/15/2022,Series C,United States,67\r\nDayTwo,SF Bay Area,Healthcare,NULL,NULL,9/15/2022,Series B,United States,90\r\nNextRoll,SF Bay Area,Marketing,NULL,0.07,9/15/2022,Unknown,United States,108\r\nTwilio,SF Bay Area,Other,800,0.11,9/14/2022,Post-IPO,United States,614\r\nPitch,Berlin,Marketing,59,0.3,9/14/2022,Series B,Germany,137\r\nInfarm,Berlin,Other,50,0.05,9/14/2022,Series D,Germany,604\r\nNetflix,SF Bay Area,Media,30,NULL,9/14/2022,Post-IPO,United States,121900\r\nBitrise,Budapest,Infrastructure,NULL,0.14,9/14/2022,Series C,Hungary,83\r\nRubius,Boston,Healthcare,160,0.75,9/13/2022,Post-IPO,United States,445\r\nCheckout.com,London,Finance,100,0.05,9/13/2022,Series D,United Kingdom,1800\r\nTaboola,New York City,Marketing,100,0.06,9/13/2022,Post-IPO,United States,445\r\nPatreon,SF Bay Area,Media,80,0.17,9/13/2022,Series F,United States,413\r\nFullStory,Atlanta,Marketing,NULL,0.12,9/13/2022,Unknown,United States,197\r\nPropzy,Ho Chi Minh City,Real Estate,NULL,1,9/13/2022,Series A,Vietnam,33\r\nQuicko,Sao Paulo,Transportation,60,NULL,9/12/2022,Acquired,Brazil,28\r\nMode Analytics,SF Bay Area,Data,25,NULL,9/12/2022,Series D,United States,81\r\nCompete,Tel Aviv,HR,11,0.28,9/12/2022,Series A,Israel,17\r\nKarbon,SF Bay Area,Other,NULL,0.23,9/12/2022,Series B,United States,91\r\nRent the Runway,New York City,Retail,NULL,0.24,9/12/2022,Post-IPO,United States,526\r\nSama,SF Bay Area,Data,NULL,NULL,9/12/2022,Series B,United States,84\r\nSkipTheDishes,Winnipeg,Food,350,NULL,9/9/2022,Acquired,Canada,6\r\nBrighte,Sydney,Energy,58,NULL,9/9/2022,Series C,Australia,145\r\nPatreon,SF Bay Area,Media,5,NULL,9/9/2022,Series F,United States,413\r\nAmber Group,Hong Kong,Crypto,NULL,0.1,9/9/2022,Series B,Hong Kong,328\r\nCapiter,Cairo,Finance,NULL,NULL,9/9/2022,Series A,Egypt,33\r\nCommonBond,New York City,Finance,NULL,1,9/9/2022,Series D,United States,125\r\nDreamBox Learning,Seattle,Education,NULL,NULL,9/9/2022,Acquired,United States,175\r\nFlowhub,Denver,Retail,NULL,0.15,9/9/2022,Unknown,United States,45\r\nLido Learning,Mumbai,Education,NULL,1,9/9/2022,Series C,India,20\r\nGoStudent,Vienna,Education,200,NULL,9/8/2022,Series D,Austria,686\r\nPomelo Fashion,Bangkok,Retail,55,0.08,9/8/2022,Unknown,Thailand,120\r\nGenome Medical,SF Bay Area,Healthcare,23,NULL,9/8/2022,Series C,United States,120\r\nBigBear.ai,Baltimore,Data,NULL,0.07,9/8/2022,Post-IPO,United States,200\r\nRealtor.com,SF Bay Area,Real Estate,NULL,NULL,9/8/2022,Acquired,United States,NULL\r\nSimple Feast,Copenhagen,Food,150,1,9/7/2022,Unknown,Denmark,173\r\nFoodpanda,Singapore,Food,60,NULL,9/7/2022,Acquired,Singapore,749\r\nUber,Vilnius,Transportation,60,NULL,9/7/2022,Post-IPO,Lithuania,24700\r\nRupeek,Bengaluru,Finance,50,NULL,9/7/2022,Unknown,India,172\r\nIntercom,SF Bay Area,Support,49,0.05,9/7/2022,Series D,United States,240\r\nPendo,Raleigh,Product,45,0.05,9/7/2022,Series F,United States,469\r\nDemandbase,SF Bay Area,Sales,27,0.03,9/7/2022,Series H,United States,143\r\nFirebolt,Tel Aviv,Data,NULL,NULL,9/7/2022,Series C,Israel,264\r\nMedly,New York City,Healthcare,NULL,0.5,9/7/2022,Series C,United States,100\r\nXsight Labs,Tel Aviv,Other,NULL,NULL,9/7/2022,Series D,Israel,100\r\nBrave Care,Portland,Healthcare,40,0.33,9/6/2022,Series B,United States,42\r\nLawgeex,Tel Aviv,Legal,30,0.33,9/6/2022,Series C,Israel,41\r\nJuniper Square,SF Bay Area,Real Estate,NULL,0.14,9/6/2022,Series C,United States,108\r\nMedium,SF Bay Area,Media,NULL,0.25,9/6/2022,Unknown,United States,163\r\nKuda,Lagos,Finance,23,0.05,9/2/2022,Series B,Nigeria,91\r\nAlerzo,Ibadan,Retail,NULL,NULL,9/2/2022,Series B,Nigeria,16\r\nSea,Singapore,Consumer,NULL,NULL,9/2/2022,Post-IPO,Singapore,8600\r\n2TM,Sao Paulo,Crypto,100,0.15,9/1/2022,Unknown,Brazil,250\r\nInnovaccer,SF Bay Area,Healthcare,90,0.08,9/1/2022,Series E,United States,379\r\nShopify,Ottawa,Retail,70,NULL,9/1/2022,Post-IPO,Canada,122\r\nUrban Sports Club,Berlin,Fitness,55,0.15,9/1/2022,Unknown,Germany,95\r\nHedvig,Stockholm,Finance,12,NULL,9/1/2022,Series B,Sweden,67\r\nSnap,Los Angeles,Consumer,1280,0.2,8/31/2022,Post-IPO,United States,4900\r\nGoodRx,Los Angeles,Healthcare,140,0.16,8/31/2022,Post-IPO,United States,910\r\nSmava,Berlin,Finance,100,0.1,8/31/2022,Unknown,Germany,188\r\nHippo Insurance,SF Bay Area,Finance,70,0.1,8/31/2022,Post-IPO,United States,1300\r\nClari,SF Bay Area,Sales,45,NULL,8/31/2022,Series F,United States,496\r\nKoo,Bengaluru,Consumer,40,NULL,8/31/2022,Series B,India,44\r\nTCR2,Boston,Healthcare,30,0.2,8/31/2022,Post-IPO,United States,173\r\nApartment List,SF Bay Area,Real Estate,29,0.1,8/31/2022,Series D,United States,169\r\nArtnight,Berlin,Retail,26,0.36,8/31/2022,Unknown,Germany,NULL\r\nSnagajob,Richmond,HR,NULL,NULL,8/31/2022,Unknown,United States,221\r\nThe Wing,New York City,Real Estate,NULL,1,8/31/2022,Series C,United States,117\r\nViamo,Accra,Other,NULL,NULL,8/31/2022,Unknown,Ghana,NULL\r\nElectric,New York City,Other,81,NULL,8/30/2022,Series D,United States,212\r\nImmersive Labs,Bristol,Security,38,0.1,8/30/2022,Series C,United Kingdom,123\r\nNate,New York City,Retail,30,NULL,8/30/2022,Series A,United States,47\r\nMeesho,Bengaluru,Retail,300,NULL,8/29/2022,Series F,India,1100\r\n54gene,Washington D.C.,Healthcare,95,0.3,8/29/2022,Series B,United States,44\r\nFungible,SF Bay Area,Crypto,NULL,NULL,8/29/2022,Series C,United States,310\r\nSkillz,SF Bay Area,Consumer,NULL,NULL,8/29/2022,Post-IPO,United States,287\r\nOtonomo,Tel Aviv,Transportation,NULL,NULL,8/28/2022,Post-IPO,Israel,231\r\nZymergen,SF Bay Area,Other,80,NULL,8/26/2022,Acquired,United States,974\r\nOkta,SF Bay Area,Security,24,NULL,8/26/2022,Post-IPO,United States,1200\r\nArgyle,New York City,Finance,20,0.07,8/26/2022,Series B,United States,78\r\nBetter.com,New York City,Real Estate,NULL,NULL,8/26/2022,Unknown,United States,905\r\nFreshDirect,Philadelphia,Food,40,NULL,8/25/2022,Acquired,United States,280\r\nLoja Integrada,Sao Paulo,Retail,25,0.1,8/25/2022,Acquired,Brazil,NULL\r\nImpact.com,Los Angeles,Marketing,NULL,0.1,8/25/2022,Private Equity,United States,361\r\nShipBob,Chicago,Logistics,NULL,0.07,8/25/2022,Series E,United States,330\r\nReali,SF Bay Area,Real Estate,140,1,8/24/2022,Series B,United States,117\r\nLoop,Washington D.C.,Finance,15,0.2,8/24/2022,Series A,United States,24\r\nPix,SF Bay Area,Food,NULL,NULL,8/24/2022,Unknown,United States,NULL\r\nTier Mobility,Berlin,Transportation,180,0.16,8/23/2022,Series D,Germany,646\r\nPackable,New York City,Retail,138,0.2,8/23/2022,Unknown,United States,472\r\nQ4,Toronto,Other,50,0.08,8/23/2022,Series C,Canada,91\r\nSkedulo,SF Bay Area,HR,31,0.08,8/23/2022,Series C,United States,114\r\nPlato,SF Bay Area,HR,29,0.5,8/23/2022,Seed,United States,3\r\nDataRobot,Boston,Data,NULL,0.26,8/23/2022,Series G,United States,1000\r\nKogan,Melbourne,Retail,NULL,NULL,8/23/2022,Post-IPO,Australia,NULL\r\nSkillshare,New York City,Education,NULL,NULL,8/23/2022,Unknown,United States,136\r\nMr. Yum,Melbourne,Food,NULL,0.17,8/22/2022,Series A,United States,73\r\nShopX,Bengaluru,Retail,NULL,1,8/22/2022,Unknown,India,56\r\nNSO,Tel Aviv,Security,100,0.14,8/21/2022,Seed,Israel,1\r\nTufin,Boston,Security,55,0.1,8/21/2022,Acquired,United States,21\r\nAmperity,Seattle,Marketing,13,0.03,8/20/2022,Series D,United States,187\r\nWayfair,Boston,Retail,870,0.05,8/19/2022,Post-IPO,United States,1700\r\nStripe,SF Bay Area,Finance,50,NULL,8/19/2022,Series H,United States,2300\r\nHodlnaut,Singapore,Crypto,40,0.8,8/19/2022,Unknown,Singapore,NULL\r\nNew Relic,SF Bay Area,Infrastructure,110,0.05,8/18/2022,Post-IPO,United States,214\r\nWheel,Austin,Healthcare,35,0.17,8/18/2022,Series C,United States,215\r\nPetal,New York City,Finance,NULL,NULL,8/18/2022,Series D,United States,704\r\nThirty Madison,New York City,Healthcare,NULL,0.1,8/18/2022,Series C,United States,209\r\nVendasta,Saskatoon,Marketing,NULL,0.05,8/18/2022,Series D,Canada,178\r\nMalwarebytes,SF Bay Area,Security,125,0.14,8/17/2022,Series B,United States,80\r\nFluke,Sao Paulo,Other,83,0.82,8/17/2022,Seed,Brazil,NULL\r\nSwyftx,Brisbane,Crypto,74,0.21,8/17/2022,Unknown,Australia,NULL\r\nTempo Automation,SF Bay Area,Other,54,NULL,8/17/2022,Series C,United States,74\r\nGenesis,New York City,Crypto,52,0.2,8/17/2022,Series A,United States,NULL\r\nWarren,Porto Alegre,Finance,50,NULL,8/17/2022,Series C,Brazil,104\r\nAlayaCare,Montreal,Healthcare,80,0.14,8/16/2022,Series D,Canada,293\r\nPliops,Tel Aviv,Data,12,0.09,8/16/2022,Series D,Israel,205\r\nWoven,Indianapolis,HR,5,0.15,8/16/2022,Series A,United States,11\r\nCrypto.com,Singapore,Crypto,NULL,NULL,8/16/2022,Unknown,Singapore,156\r\nEdmodo,SF Bay Area,Education,NULL,1,8/16/2022,Acquired,United States,77\r\nSnapdocs,SF Bay Area,Real Estate,NULL,0.1,8/16/2022,Series D,United States,253\r\nUpdater,New York City,Other,NULL,NULL,8/16/2022,Unknown,United States,467\r\nSema4,Stamford,Healthcare,250,0.13,8/15/2022,Post-IPO,United States,791\r\nBlend,SF Bay Area,Finance,220,0.12,8/15/2022,Post-IPO,United States,665\r\nContraFect,New York City,Healthcare,16,0.37,8/15/2022,Post-IPO,United States,380\r\nThredUp,Chicago,Retail,NULL,0.15,8/15/2022,Post-IPO,United States,305\r\nAnywell,Tel Aviv,Real Estate,11,NULL,8/14/2022,Series A,Israel,15\r\nAlmanac,SF Bay Area,Other,NULL,NULL,8/13/2022,Series A,United States,45\r\nPeloton,New York City,Fitness,784,0.13,8/12/2022,Post-IPO,United States,1900\r\nCore Scientific,Austin,Crypto,NULL,0.1,8/12/2022,Post-IPO,United States,169\r\nOrbit,SF Bay Area,Other,NULL,NULL,8/12/2022,Series A,United States,20\r\nTruepill,SF Bay Area,Healthcare,175,0.33,8/11/2022,Series D,United States,255\r\nCalm,SF Bay Area,Healthcare,90,0.2,8/11/2022,Series C,United States,218\r\nFourKites,Chicago,Logistics,60,0.08,8/11/2022,Series D,United States,201\r\nMarketforce,Nairobi,Retail,54,0.09,8/11/2022,Series A,Kenya,42\r\nBetterfly,Santiago,Healthcare,30,NULL,8/11/2022,Series C,Chile,204\r\nExpert360,Sydney,Recruiting,7,NULL,8/11/2022,Series C,Australia,26\r\nGuidewire,SF Bay Area,Finance,NULL,0.02,8/11/2022,Post-IPO,United States,24\r\nTrybe,Sao Paulo,Education,47,0.1,8/10/2022,Series B,Brazil,40\r\nPermutive,London,Marketing,30,0.12,8/10/2022,Series C,United Kingdom,105\r\nHomeward,Austin,Real Estate,NULL,0.2,8/10/2022,Series B,United States,160\r\nPollen,London,Marketing,NULL,1,8/10/2022,Series C,United Kingdom,238\r\nVedanta Biosciences,Boston,Healthcare,NULL,0.2,8/10/2022,Series D,United States,301\r\nGoHealth,Chicago,Healthcare,800,0.2,8/9/2022,Post-IPO,United States,75\r\nHootsuite,Vancouver,Marketing,400,0.3,8/9/2022,Series C,Canada,300\r\nNutanix,SF Bay Area,Infrastructure,270,0.04,8/9/2022,Post-IPO,United States,1100\r\nQuanterix,Boston,Healthcare,130,0.25,8/9/2022,Post-IPO,United States,533\r\nWix,Tel Aviv,Marketing,100,NULL,8/9/2022,Post-IPO,Israel,58\r\nMadeiraMadeira,Curitiba,Retail,60,0.03,8/9/2022,Series E,Brazil,338\r\nMelio,New York City,Finance,60,NULL,8/9/2022,Series D,United States,504\r\nLinktree,Melbourne,Consumer,50,0.17,8/9/2022,Unknown,Australia,165\r\nShogun,SF Bay Area,Retail,48,0.3,8/9/2022,Series C,United States,114\r\nAbsci,Vancouver,Healthcare,40,NULL,8/9/2022,Post-IPO,United States,237\r\nDooly,Vancouver,Sales,12,NULL,8/9/2022,Series B,Canada,102\r\nBerkeley Lights,SF Bay Area,Healthcare,NULL,0.12,8/9/2022,Post-IPO,United States,272\r\nDailyPay,New York City,Finance,NULL,0.15,8/9/2022,Unknown,United States,814\r\nHaus,SF Bay Area,Food,NULL,1,8/9/2022,Seed,United States,7\r\nKaltura,New York City,Media,NULL,0.1,8/9/2022,Post-IPO,United States,166\r\nShift,SF Bay Area,Transportation,NULL,NULL,8/9/2022,Post-IPO,United States,504\r\nSweetgreen,Los Angeles,Food,NULL,NULL,8/9/2022,Post-IPO,United States,478\r\nGroupon,Chicago,Retail,500,0.15,8/8/2022,Post-IPO,United States,1400\r\nLoggi,Sao Paulo,Logistics,500,0.15,8/8/2022,Series F,Brazil,507\r\nVroom,New York City,Transportation,337,NULL,8/8/2022,Post-IPO,United States,1300\r\nWarby Parker,New York City,Consumer,63,NULL,8/8/2022,Post-IPO,United States,535\r\nLabelbox,SF Bay Area,Data,36,NULL,8/8/2022,Series D,United States,188\r\nPerion,Tel Aviv,Marketing,20,0.05,8/8/2022,Post-IPO,Israel,76\r\nDaily Harvest,New York City,Food,NULL,0.15,8/8/2022,Series D,United States,120\r\nDataRobot,Boston,Data,NULL,NULL,8/8/2022,Series G,United States,1000\r\niRobot,Boston,Consumer,140,0.1,8/5/2022,Acquired,United States,30\r\nMejuri,Toronto,Retail,50,0.1,8/5/2022,Series B,Canada,28\r\nUberflip,Toronto,Marketing,31,0.17,8/5/2022,Series A,Canada,42\r\nSlync,Dallas,Logistics,NULL,NULL,8/5/2022,Series B,United States,76\r\nTalkdesk,SF Bay Area,Support,NULL,NULL,8/5/2022,Series D,United States,497\r\nDoma,SF Bay Area,Finance,250,0.13,8/4/2022,Post-IPO,United States,679\r\nArticle,Vancouver,Retail,216,0.17,8/4/2022,Series B,Canada,NULL\r\nJam City,Los Angeles,Consumer,200,0.17,8/4/2022,Unknown,United States,652\r\n10X Genomics,SF Bay Area,Healthcare,100,0.08,8/4/2022,Post-IPO,United States,242\r\nLEAD,Mumbai,Education,80,0.04,8/4/2022,Series E,India,166\r\nZendesk,SF Bay Area,Support,80,NULL,8/4/2022,Acquired,United States,85\r\nOn Deck,SF Bay Area,Education,73,0.33,8/4/2022,Series A,United States,20\r\nRenoRun,Montreal,Construction,70,0.12,8/4/2022,Series B,Canada,163\r\nRingCentral,SF Bay Area,Support,50,NULL,8/4/2022,Post-IPO,United States,44\r\nMedly,New York City,Healthcare,NULL,0.16,8/4/2022,Series C,United States,100\r\nNomad,Sao Paulo,Finance,NULL,0.2,8/4/2022,Series B,Brazil,290\r\nStubHub,SF Bay Area,Consumer,NULL,NULL,8/4/2022,Acquired,United States,59\r\nWeedmaps,Los Angeles,Other,NULL,0.1,8/4/2022,Acquired,United States,NULL\r\nZenius,Jakarta,Education,NULL,0.3,8/4/2022,Series B,Indonesia,20\r\nHealthcare.com,Miami,Healthcare,149,NULL,8/3/2022,Series C,United States,244\r\nUnbounce,Vancouver,Marketing,47,0.2,8/3/2022,Series A,Canada,39\r\nBeyond Meat,Los Angeles,Food,40,NULL,8/3/2022,Post-IPO,United States,122\r\nThe Org,New Delhi,HR,13,NULL,8/3/2022,Series B,United States,39\r\nCarDekho,Gurugram,Transportation,NULL,NULL,8/3/2022,Series E,India,497\r\nPuppet,Portland,Infrastructure,NULL,0.15,8/3/2022,Acquired,United States,189\r\nSoundCloud,Berlin,Consumer,NULL,0.2,8/3/2022,Unknown,Germany,542\r\nTalkwalker,Luxembourg,Marketing,NULL,0.15,8/3/2022,Private Equity,Luxembourg,9\r\nRobinhood,SF Bay Area,Finance,713,0.23,8/2/2022,Post-IPO,United States,5600\r\nLatch,New York City,Security,115,0.37,8/2/2022,Post-IPO,United States,342\r\nVedantu,Bengaluru,Education,100,NULL,8/2/2022,Series E,India,292\r\nSeegrid,Pittsburgh,Logistics,90,NULL,8/2/2022,Unknown,United States,107\r\nNylas,SF Bay Area,Product,80,0.25,8/2/2022,Series C,United States,175\r\nOutreach,Seattle,Sales,60,0.05,8/2/2022,Series G,United States,489\r\nSendy,Nairobi,Logistics,54,0.2,8/2/2022,Series B,Kenya,26\r\nThe Predictive Index,Boston,HR,40,NULL,8/2/2022,Acquired,United States,71\r\nSendy,Nairobi,Logistics,30,0.1,8/2/2022,Series B,Kenya,26\r\nStedi,Boulder,Product,23,0.3,8/2/2022,Series B,United States,75\r\nGlossier,New York City,Retail,19,0.08,8/2/2022,Series E,United States,266\r\nButterfly Network,New Haven,Healthcare,NULL,0.1,8/2/2022,Post-IPO,United States,530\r\nFuboTV,New York City,Media,NULL,NULL,8/2/2022,Post-IPO,United States,151\r\nHash,Sao Paulo,Finance,58,0.5,8/1/2022,Series C,Brazil,58\r\nClasskick,Chicago,Education,NULL,NULL,8/1/2022,Seed,United States,1\r\nDeHaat,Gurugram,Food,NULL,NULL,8/1/2022,Series D,India,194\r\nOnlyFans,London,Media,NULL,NULL,8/1/2022,Unknown,United Kingdom,NULL\r\nOracle,SF Bay Area,Other,NULL,NULL,8/1/2022,Post-IPO,United States,NULL\r\nPerceptive Automata,Boston,Transportation,NULL,1,8/1/2022,Series A,United States,20\r\nWhereby,Oslo,Other,NULL,NULL,8/1/2022,Series A,Norway,10\r\nMetigy,Sydney,Marketing,75,1,7/31/2022,Series B,Australia,18\r\nVee,Tel Aviv,HR,16,0.32,7/31/2022,Seed,Israel,15\r\nGatherly,Atlanta,Marketing,NULL,0.5,7/31/2022,NULL,United States,NULL\r\nOla,Bengaluru,Transportation,1000,NULL,7/29/2022,Series J,India,5000\r\nClearco,Toronto,Fin-Tech,125,0.25,7/29/2022,Series C,Canada,681\r\nImperfect Foods,SF Bay Area,Food,50,NULL,7/29/2022,Series D,United States,229\r\nShelf Engine,Seattle,Food,43,NULL,7/29/2022,Series B,United States,58\r\nQuantcast,SF Bay Area,Marketing,40,0.06,7/29/2022,Series C,United States,65\r\nSherpa,Toronto,Travel,22,NULL,7/29/2022,Unknown,Canada,11\r\nCoinFLEX,Victoria,Crypto,NULL,NULL,7/29/2022,Unknown,Seychelles,11\r\nMissFresh,Beijing,Food,NULL,NULL,7/29/2022,Post-IPO,China,1700\r\nYabonza,Sydney,Real Estate,NULL,1,7/29/2022,Unknown,Australia,6\r\nRibbon,New York City,Real Estate,136,NULL,7/28/2022,Series C,United States,405\r\nCareer Karma,SF Bay Area,Education,60,0.33,7/28/2022,Series B,United States,51\r\nMetromile,SF Bay Area,Finance,60,0.2,7/28/2022,Acquired,United States,510\r\nLaybuy,Auckland,Finance,45,NULL,7/28/2022,Post-IPO,New Zealand,130\r\nAllbirds,SF Bay Area,Retail,23,NULL,7/28/2022,Post-IPO,United States,202\r\nTextNow,Waterloo,Consumer,22,NULL,7/28/2022,Seed,Canada,1\r\n2U,Washington D.C.,Education,NULL,0.2,7/28/2022,Post-IPO,United States,426\r\nBikayi,Bengaluru,Retail,NULL,NULL,7/28/2022,Series A,India,12\r\nBrainbase,Los Angeles,Sales,NULL,NULL,7/28/2022,Series A,United States,12\r\nChange.org,SF Bay Area,Other,NULL,0.19,7/28/2022,Series D,United States,72\r\nTapas Media,SF Bay Area,Media,NULL,NULL,7/28/2022,Acquired,United States,17\r\nTurntide,SF Bay Area,Energy,NULL,0.2,7/28/2022,Unknown,United States,491\r\nRivian,Detroit,Transportation,840,0.06,7/27/2022,Post-IPO,United States,10700\r\nVox Media,Washington D.C.,Media,39,0.02,7/27/2022,Series F,United States,307\r\nCoinsquare,Toronto,Crypto,30,0.24,7/27/2022,Unknown,Canada,98\r\nSkai,Tel Aviv,Marketing,30,0.04,7/27/2022,Series E,Israel,60\r\nShopify,Ottawa,Retail,1000,0.1,7/26/2022,Post-IPO,Canada,122\r\nMcMakler,Berlin,Real Estate,90,NULL,7/26/2022,Unknown,Germany,214\r\nFiverr,Tel Aviv,Other,60,0.08,7/26/2022,Post-IPO,Israel,111\r\nInDebted,Sydney,Finance,40,0.17,7/26/2022,Series B,Australia,41\r\nOutbrain,New York City,Marketing,38,0.03,7/26/2022,Post-IPO,United States,394\r\nDover,SF Bay Area,Recruiting,23,0.3,7/26/2022,Series A,United States,22\r\nImmutable,Sydney,Crypto,20,0.06,7/26/2022,Series C,Australia,279\r\nZymergen,SF Bay Area,Other,80,NULL,7/25/2022,Acquired,United States,974\r\nPear Therapeutics ,Boston,Healthcare,25,0.09,7/25/2022,Post-IPO,United States,409\r\n Included Health,SF Bay Area,Healthcare,NULL,0.06,7/25/2022,Series E,United States,272\r\nSoluto,Tel Aviv,Support,120,1,7/24/2022,Acquired,Israel,18\r\nEucalyptus,Sydney,Healthcare,50,0.2,7/22/2022,Series C,United States,69\r\nWorkstream,SF Bay Area,HR,45,NULL,7/22/2022,Series B,United States,58\r\nQuanto,Sao Paulo,Finance,28,0.22,7/22/2022,Series A,Brazil,15\r\nClarify Health,SF Bay Area,Healthcare,15,0.05,7/22/2022,Series D,United States,328\r\nArete,Miami,Security,NULL,NULL,7/22/2022,Unknown,United States,NULL\r\nBoosted Commerce,Los Angeles,Retail,NULL,0.05,7/22/2022,Series B,United States,137\r\nOwlet,Lehi,Healthcare,NULL,NULL,7/22/2022,Post-IPO,United States,178\r\nPeople.ai,SF Bay Area,Sales,NULL,NULL,7/22/2022,Series D,United States,200\r\nWizeline,SF Bay Area,Product,NULL,NULL,7/22/2022,Acquired,United States,62\r\nBlockchain.com,London,Crypto,150,0.25,7/21/2022,Series D,United Kingdom,490\r\nCallisto Media,SF Bay Area,Media,140,0.35,7/21/2022,Series D,United States,NULL\r\nAppGate,Miami,Security,130,0.22,7/21/2022,Post-IPO,United States,NULL\r\nWHOOP,Boston,Fitness,95,0.15,7/21/2022,Series F,United States,404\r\nRad Power Bikes,Seattle,Transportation,63,0.1,7/21/2022,Series D,United States,329\r\nLunchbox,New York City,Food,60,0.33,7/21/2022,Series B,United States,72\r\nRealSelf,Seattle,Healthcare,11,0.05,7/21/2022,Series B,United States,42\r\n98point6,Seattle,Healthcare,NULL,0.1,7/21/2022,Series E,United States,247\r\nCatalyst,New York City,Support,NULL,NULL,7/21/2022,Series B,United States,45\r\nInVision,New York City,Product,NULL,0.5,7/21/2022,Series F,United States,356\r\nMural,SF Bay Area,Product,NULL,NULL,7/21/2022,Series C,United States,192\r\nSmarsh,Portland,Other,NULL,NULL,7/21/2022,Private Equity,United States,NULL\r\nJust Eat Takeaway,Amsterdam,Food,390,NULL,7/20/2022,Post-IPO,Netherlands,2800\r\nFlyhomes,Seattle,Real Estate,200,0.2,7/20/2022,Series C,United States,310\r\nVaro,SF Bay Area,Finance,75,NULL,7/20/2022,Series E,United States,992\r\nBlueStacks,SF Bay Area,Other,60,NULL,7/20/2022,Series C,United States,48\r\nLyft,SF Bay Area,Transportation,60,0.02,7/20/2022,Post-IPO,United States,4900\r\nIntrohive,Ferdericton,Sales,57,0.16,7/20/2022,Series C,Canada,125\r\nZencity,Tel Aviv,Other,30,0.2,7/20/2022,Unknown,Israel,51\r\nSplice,New York City,Media,23,NULL,7/20/2022,Series D,United States,159\r\nForma.ai,Toronto,Sales,15,0.09,7/20/2022,Series B,Canada,58\r\nArc,SF Bay Area,HR,13,NULL,7/20/2022,Seed,United States,1\r\nInvitae,SF Bay Area,Healthcare,1000,NULL,7/19/2022,Post-IPO,United States,2000\r\nOlive,Columbus,Healthcare,450,0.31,7/19/2022,Series H,United States,856\r\nM1,Chicago,Finance,38,NULL,7/19/2022,Series E,United States,323\r\nSellerX,Berlin,Retail,28,NULL,7/19/2022,Unknown,Germany,766\r\nStint,London,HR,28,0.2,7/19/2022,Unknown,United Kingdom,NULL\r\nCapsule,New York City,Healthcare,NULL,0.13,7/19/2022,Series D,United States,570\r\nPACT Pharma,SF Bay Area,Healthcare,94,NULL,7/18/2022,Series C,United States,200\r\nGemini,New York City,CryptoCurrency,68,0.07,7/18/2022,Unknown,United States,423\r\nLusha,New York City,Marketing,30,0.1,7/18/2022,Series B,United States,245\r\nElemy,SF Bay Area,Healthcare,NULL,NULL,7/18/2022,Series B,United States,323\r\nFreshly,New York City,Food,NULL,0.25,7/18/2022,Acquired,United States,107\r\nHydrow,Boston,Fitness,NULL,0.35,7/18/2022,Series D,United States,269\r\nTikTok,Los Angeles,Consumer,NULL,NULL,7/18/2022,Acquired,United States,NULL\r\nVimeo,New York City,Consumer,NULL,0.06,7/18/2022,Post-IPO,United States,450\r\nBright Money,Bengaluru,Finance,100,0.5,7/15/2022,Series A,India,31\r\nProject44,Chicago,Logistics,63,0.05,7/15/2022,Unknown,United States,817\r\nHeroes,London,Retail,24,0.2,7/15/2022,Unknown,United States,265\r\nAspire,SF Bay Area,Marketing,23,NULL,7/15/2022,Series A,United States,27\r\nStyleSeat,SF Bay Area,Consumer,NULL,0.17,7/15/2022,Series C,United States,40\r\nZego,London,Finance,85,0.17,7/14/2022,Series C,United Kingdom,202\r\nThe Mom Project,Chicago,HR,54,0.15,7/14/2022,Series C,United States,115\r\nUnstoppable Domains,SF Bay Area,Crypto Currency,42,0.25,7/14/2022,Series B,United States,7\r\nKiavi,SF Bay Area,Real Estate,39,0.07,7/14/2022,Series E,United States,240\r\nAlto Pharmacy,SF Bay Area,Healthcare,NULL,NULL,7/14/2022,Series E,United States,560\r\nCosuno,Berlin,Construction,NULL,NULL,7/14/2022,Series B,Germany,45\r\nOpenSea,New York City,Crypto,NULL,0.2,7/14/2022,Series C,United States,427\r\nWave,Dakar,Finance,300,0.15,7/13/2022,Series A,Senegal,292\r\nTonal,SF Bay Area,Fitness,262,0.35,7/13/2022,Series E,United States,450\r\nFabric,New York City,Logistics,120,0.4,7/13/2022,Series C,United States,336\r\nBryter,Berlin,Product,100,0.3,7/13/2022,Series B,Germany,89\r\nChowNow,Los Angeles,Food,100,0.2,7/13/2022,Series C,United States,64\r\nInvolves,Florianópolis,Retail,70,0.18,7/13/2022,Unknown,Brazil,23\r\n100 Thieves,Los Angeles,Consumer,12,NULL,7/13/2022,Series C,United States,120\r\nNuro,SF Bay Area,Transportation,7,NULL,7/13/2022,Series D,United States,2100\r\nArrival,London,Transportation,NULL,0.3,7/13/2022,Post-IPO,United Kingdom,629\r\nCircleUp,SF Bay Area,Finance,NULL,NULL,7/13/2022,Series C,United States,53\r\nPapa,Miami,Other,NULL,0.15,7/13/2022,Series D,United States,241\r\nGopuff,Philadelphia,Food,1500,0.1,7/12/2022,Series H,United States,3400\r\nFraazo,Mumbai,Food,150,NULL,7/12/2022,Series B,India,63\r\nBabylon,London,Healthcare,100,NULL,7/12/2022,Post-IPO,United Kingdom,1100\r\nHubilo,SF Bay Area,Marketing,45,0.12,7/12/2022,Series B,United States,153\r\nAirlift,Lahore,Logistics,NULL,1,7/12/2022,Series B,Pakistan,109\r\nMicrosoft,Seattle,Other,NULL,NULL,7/12/2022,Post-IPO,United States,1\r\nSpring,SF Bay Area,Retail,NULL,NULL,7/12/2022,Unknown,United States,61\r\nHopin,London,Other,242,0.29,7/11/2022,Series D,United Kingdom,1000\r\nAlice,Sao Paulo,Healthcare,63,NULL,7/11/2022,Series C,Brazil,174\r\nSundaySky,New York City,Marketing,24,0.13,7/11/2022,Series D,United States,74\r\nApeel Sciences,Santa Barbara,Food,NULL,NULL,7/11/2022,Series E,United States,640\r\nForward,SF Bay Area,Healthcare,NULL,0.05,7/11/2022,Series D,United States,225\r\nIgnite,SF Bay Area,Crypto,NULL,0.5,7/11/2022,Series A,United States,9\r\nNextbite,Denver,Food,NULL,NULL,7/9/2022,Series C,United States,150\r\nPuduTech,Shenzen,Other,1500,NULL,7/8/2022,Series C,China,184\r\nButler Hospitality,New York City,Food,1000,1,7/8/2022,Series B,United States,50\r\nCalibrate,New York City,Healthcare,156,0.24,7/8/2022,Series B,United States,127\r\nNextRoll,SF Bay Area,Marketing,NULL,0.03,7/8/2022,Unknown,United States,108\r\nArgo AI,Pittsburgh,Transportation,150,0.05,7/7/2022,Unknown,United States,3600\r\nNext Insurance,SF Bay Area,Finance,150,0.17,7/7/2022,Series E,United States,881\r\nAdwerx,Durham,Marketing,40,NULL,7/7/2022,Unknown,United States,20\r\nEmotive,Los Angeles,Marketing,30,0.18,7/7/2022,Series B,United States,78\r\nCedar,New York City,Healthcare,NULL,0.24,7/7/2022,Series D,United States,351\r\nTwitter,SF Bay Area,Consumer,NULL,NULL,7/7/2022,Post-IPO,United States,5700\r\nRemote,SF Bay Area,HR,100,0.09,7/6/2022,Series C,United States,496\r\nShopify,Ottawa,Retail,50,NULL,7/6/2022,Post-IPO,Canada,122\r\nAnodot,Tel Aviv,Data,35,0.27,7/6/2022,Series C,United States,64\r\nSQream,New York City,Data,30,0.18,7/6/2022,Series B,United States,77\r\nMotif Foodworks,Boston,Food,NULL,NULL,7/6/2022,Series B,United States,344\r\nLoft,Sao Paulo,Real Estate,384,0.12,7/5/2022,Unknown,Brazil,788\r\nBizzabo,New York City,Marketing,120,0.3,7/5/2022,Series E,United States,194\r\neToro,Tel Aviv,Finance,100,0.06,7/5/2022,Unknown,Israel,322\r\nVerbit,New York City,Data,80,0.1,7/5/2022,Series E,United States,569\r\nOutschool,SF Bay Area,Education,31,0.18,7/5/2022,Series D,United States,240\r\nBullish,Hong Kong,Crypto,30,0.08,7/5/2022,Unknown,Hong Kong,300\r\nTransmit Security,Boston,Security,27,0.07,7/5/2022,Series A,United States,583\r\nThimble,New York City,Fin-Tech,20,0.33,7/5/2022,Series A,United States,28\r\nSyte,Tel Aviv,Retail,13,0.08,7/5/2022,Series C,Israel,71\r\nLightricks,Jerusalem,Consumer,80,0.12,7/4/2022,Series D,Israel,335\r\nChessable,London,Consumer,29,NULL,7/4/2022,Acquired,United Kingdom,0\r\nSendle,Sydney,Logistics,27,0.12,7/4/2022,Series C,Australia,69\r\nLendis,Berlin,Other,18,0.15,7/4/2022,Series A,Germany,90\r\nAirtasker,Sydney,Consumer,NULL,NULL,7/4/2022,Series C,Australia,26\r\nGorillas,Berlin,Food,540,NULL,7/3/2022,Series C,Germany,1300\r\nCelsius,New York City,Crypto,150,0.25,7/3/2022,Series B,United States,864\r\nLetsGetChecked,New York City,Healthcare,NULL,NULL,7/2/2022,Series D,United States,263\r\nPerx Health,Sydney,Healthcare,NULL,NULL,7/2/2022,Seed,Australia,2\r\nZepto,Brisbane,Finance,NULL,0.1,7/2/2022,Series A,Australia,25\r\nWanderJaunt,SF Bay Area,Travel,85,1,7/1/2022,Series B,United States,26\r\nCanoo,Los Angeles,Transportation,58,0.06,7/1/2022,Post-IPO,United States,300\r\nBamboo Health,Louisville,Healthcare,52,NULL,7/1/2022,Unknown,United States,NULL\r\nTeleport,SF Bay Area,Infrastructure,15,0.06,7/1/2022,Series C,United States,169\r\nRemesh,New York City,Support,NULL,NULL,7/1/2022,Series A,United States,38\r\nEnjoy,SF Bay Area,Retail,400,0.18,6/30/2022,Post-IPO,United States,310\r\nCrejo.Fun,Bengaluru,Education,170,1,6/30/2022,Seed,India,3\r\nStash Financial,New York City,Finance,40,0.08,6/30/2022,Unknown,United States,480\r\nNate,New York City,Retail,30,0.2,6/30/2022,Series A,United States,47\r\nSnyk,Boston,Security,30,NULL,6/30/2022,Series F,United States,849\r\nStream,Boulder,Product,20,0.12,6/30/2022,Series B,United States,58\r\nFinleap Connect,Hamburg,Finance,14,0.1,6/30/2022,Series A,Germany,22\r\nAbra,SF Bay Area,Crypto,12,0.05,6/30/2022,Series C,United States,106\r\nGavelytics,Los Angeles,Legal,NULL,1,6/30/2022,Seed,United States,5\r\nSecfi,SF Bay Area,Finance,NULL,NULL,6/30/2022,Series A,United States,7\r\nSundae,SF Bay Area,Real Estate,NULL,0.15,6/30/2022,Series C,United States,135\r\nToppr,Mumbai,Education,350,NULL,6/29/2022,Acquired,India,112\r\nUnity,SF Bay Area,Other,200,0.04,6/29/2022,Post-IPO,United States,1300\r\nNiantic,SF Bay Area,Consumer,85,0.08,6/29/2022,Series D,United States,770\r\nAvantStay,Los Angeles,Travel,80,NULL,6/29/2022,Unknown,United States,811\r\nQumulo,Seattle,Data,80,0.19,6/29/2022,Series E,United States,347\r\nClutch,Toronto,Transportation,76,0.22,6/29/2022,Series B,Canada,153\r\nParallel Wireless,Nashua,Infrastructure,60,NULL,6/29/2022,Unknown,United States,8\r\nOye Rickshaw,New Delhi,Transportation,40,0.2,6/29/2022,Unknown,India,13\r\nRows,Berlin,Other,18,0.3,6/29/2022,Series B,Germany,25\r\nBaton,SF Bay Area,Transportation,16,0.25,6/29/2022,Series A,United States,13\r\nSubstack,SF Bay Area,Media,13,0.14,6/29/2022,Series B,United States,82\r\nCommentSold,Huntsville,Retail,NULL,NULL,6/29/2022,Unknown,United States,NULL\r\nDegreed,SF Bay Area,Education,NULL,0.15,6/29/2022,Series D,United States,411\r\nHomeLight,SF Bay Area,Real Estate,NULL,0.19,6/29/2022,Series D,United States,743\r\nModsy,SF Bay Area,Retail,NULL,NULL,6/29/2022,Series C,United States,72\r\nVolt Bank,Sydney,Finance,NULL,1,6/29/2022,Series E,Australia,90\r\nHuobi,Beijing,Crypto,300,0.3,6/28/2022,Unknown,China,2\r\nWhiteHat Jr,Mumbai,Education,300,NULL,6/28/2022,Acquired,India,11\r\nStockX,Detroit,Retail,120,0.08,6/28/2022,Series E,United States,690\r\nSidecar Health,Los Angeles,Healthcare,110,0.4,6/28/2022,Series C,United States,163\r\nStockX,Detroit,Retail,80,NULL,6/28/2022,Series E,United States,690\r\nVezeeta,Dubai,Healthcare,50,0.1,6/28/2022,Series D,United Arab Emirates,71\r\nBright Machines,SF Bay Area,Data,30,0.08,6/28/2022,Unknown,Israel,250\r\nHealthMatch,Sydney,Healthcare,18,0.5,6/28/2022,Series B,Australia,20\r\nBooktopia,Sydney,Retail,NULL,NULL,6/28/2022,Series A,Australia,23\r\nNova Benefits,Bengaluru,Healthcare,NULL,0.3,6/28/2022,Series B,India,41\r\nUna Brands,Singapore,Retail,NULL,0.1,6/28/2022,Series A,Singapore,55\r\nAppLovin,SF Bay Area,Marketing,300,0.12,6/27/2022,Post-IPO,United States,1600\r\nUiPath,New York City,Data,210,0.05,6/27/2022,Post-IPO,United States,2000\r\nUdaan,Bengaluru,Retail,180,0.04,6/27/2022,Unknown,India,1500\r\nCue,San Diego,Healthcare,170,NULL,6/27/2022,Post-IPO,United States,999\r\nBanxa,Melbourne,Crypto,70,0.3,6/27/2022,Post-IPO,Australia,13\r\nSafeGraph,SF Bay Area,Data,27,0.25,6/27/2022,Series B,United States,61\r\nAmount,Chicago,Finance,NULL,0.18,6/27/2022,Unknown,United States,283\r\nPostscript,SF Bay Area,Marketing,43,NULL,6/26/2022,Series C,United States,106\r\nBitpanda,Vienna,Crypto,270,0.27,6/24/2022,Series C,Austria,546\r\nSunday,Atlanta,Finance,90,0.23,6/24/2022,Series A,United States,124\r\nBestow,Dallas,Finance,41,0.14,6/24/2022,Series C,United States,137\r\nEthos Life,SF Bay Area,Finance,40,0.12,6/24/2022,Series D,United States,406\r\nFeather,New York City,Retail,NULL,NULL,6/24/2022,Unknown,United States,76\r\nGive Legacy,Boston,Healthcare,NULL,NULL,6/24/2022,Series B,United States,45\r\nNetflix,SF Bay Area,Media,300,0.03,6/23/2022,Post-IPO,United States,121900\r\nAura,Boston,Security,70,0.09,6/23/2022,Series F,United States,500\r\nPipl,Spokane,Security,22,0.13,6/23/2022,Unknown,United States,19\r\nVouch,SF Bay Area,Finance,15,0.07,6/23/2022,Series C,United States,159\r\nVoyage SMS,Los Angeles,Marketing,8,0.13,6/23/2022,Unknown,United States,10\r\nEsper,Seattle,Other,NULL,0.12,6/23/2022,Series A,United States,10\r\nKune,Nairobi,Food,NULL,1,6/23/2022,Seed,Kenya,1\r\nMark43,New York City,Other,NULL,NULL,6/23/2022,Series E,United States,229\r\nOrchard,New York City,Real Estate,NULL,0.1,6/23/2022,Series D,United States,472\r\nRo,New York City,Healthcare,NULL,0.18,6/23/2022,Unknown,United States,1000\r\nStreamElements,Tel Aviv,Media,NULL,0.2,6/23/2022,Series B,Israel,111\r\nMasterClass,SF Bay Area,Education,120,0.2,6/22/2022,Series E,United States,461\r\nIronNet,Washington D.C.,Security,90,0.35,6/22/2022,Post-IPO,United States,410\r\nBungalow,SF Bay Area,Real Estate,70,NULL,6/22/2022,Series C,United States,171\r\nIronNet,Washington D.C.,Security,55,0.17,6/22/2022,Post-IPO,United States,410\r\nSprinklr,New York City,Support,50,NULL,6/22/2022,Post-IPO,United States,429\r\nSuperpedestrian,Boston,Transportation,35,0.07,6/22/2022,Series C,United States,261\r\nVoi,Stockholm,Transportation,35,0.1,6/22/2022,Series D,United States,515\r\nBalto,St. Louis,Sales,30,NULL,6/22/2022,Series B,United States,51\r\nRitual,Toronto,Food,23,0.16,6/22/2022,Series C,Canada,134\r\nMindgeek,Luxembourg,Media,NULL,NULL,6/22/2022,Unknown,Luxembourg,NULL\r\nVoly,Sydney,Food,NULL,0.5,6/22/2022,Seed,Australia,13\r\nEbanx,Curitiba,Finance,340,0.2,6/21/2022,Series B,Brazil,460\r\nTaskUs,Los Angeles,Support,52,0,6/21/2022,Post-IPO,United States,279\r\nCommunity,Los Angeles,Marketing,40,0.3,6/21/2022,Unknown,United States,40\r\nSourcegraph,SF Bay Area,Product,24,0.08,6/21/2022,Series D,United States,248\r\nFrubana,Bogota,Food,NULL,0.03,6/21/2022,Series C,Colombia,202\r\nSuperLearn,Bengaluru,Education,NULL,1,6/21/2022,Seed,India,0\r\nTray.io,SF Bay Area,Data,NULL,0.1,6/21/2022,Series C,United States,109\r\nBybit,Singapore,Crypto,600,0.3,6/20/2022,Unknown,Singapore,NULL\r\nSummerBio,SF Bay Area,Healthcare,101,1,6/20/2022,Unknown,United States,7\r\nTrax,Singapore,Retail,100,0.12,6/20/2022,Series E,Singapore,1000\r\nAqgromalin,Chennai,Food,80,0.3,6/20/2022,Unknown,India,12\r\nBonsai,Toronto,Retail,30,0.55,6/20/2022,Series A,Canada,27\r\nBrighte,Sydney,Energy,30,0.15,6/20/2022,Series C,Australia,145\r\nBuzzer,New York City,Media,NULL,0.2,6/20/2022,Unknown,United States,32\r\nBybit,Singapore,Crypto,NULL,NULL,6/20/2022,Unknown,Singapore,NULL\r\nCityMall,Gurugram,Retail,191,0.3,6/19/2022,Series C,India,112\r\nBitOasis,Dubai,Crypto,9,0.05,6/19/2022,Series B,United Arab Emirates,30\r\nUnacademy,Bengaluru,Education,150,0.03,6/18/2022,Series H,India,838\r\nBytedance,Shanghai,Consumer,150,NULL,6/17/2022,Unknown,China,9400\r\nSocure,Reno,Finance,69,0.13,6/17/2022,Series E,United States,646\r\nFinite State,Columbus,Security,16,0.2,6/17/2022,Series B,United States,49\r\nJOKR,New York City,Food,50,0.05,6/16/2022,Series B,United States,430\r\nZumper,SF Bay Area,Real Estate,45,0.15,6/16/2022,Series D,United States,178\r\nPharmEasy,Mumbai,Healthcare,40,NULL,6/16/2022,Unknown,India,1600\r\nCirculo Health,Columbus,Healthcare,NULL,0.5,6/16/2022,Series A,United States,50\r\nSwappie,Helsinki,Retail,250,0.17,6/15/2022,Series C,Finland,169\r\nWealthsimple,Toronto,Finance,159,0.13,6/15/2022,Unknown,Canada,900\r\nWeee!,SF Bay Area,Food,150,0.1,6/15/2022,Series E,United States,863\r\nNotarize,Boston,Legal,110,0.25,6/15/2022,Series D,United States,213\r\nElementor,Tel Aviv,Media,60,0.15,6/15/2022,Unknown,Israel,65\r\nTonkean,SF Bay Area,Other,23,0.23,6/15/2022,Series B,United States,83\r\nAirtame,Copenhagen,Consumer,15,NULL,6/15/2022,Unknown,Denmark,7\r\nOpenWeb,Tel Aviv,Media,14,0.05,6/15/2022,Series E,Israel,223\r\nSwyft,Toronto,Logistics,10,0.3,6/15/2022,Series A,Canada,20\r\nCrehana,Lima,Education,NULL,NULL,6/15/2022,Series B,Peru,93\r\nJetClosing,Seattle,Real Estate,NULL,1,6/15/2022,Series B,United States,44\r\nCoinbase,SF Bay Area,Crypto,1100,0.18,6/14/2022,Post-IPO,United States,549\r\nRedfin,Seattle,Real Estate,470,0.08,6/14/2022,Post-IPO,United States,319\r\nCompass,New York City,Real Estate,450,0.1,6/14/2022,Post-IPO,United States,1600\r\nSami,Sao Paulo,Healthcare,75,0.15,6/14/2022,Unknown,Brazil,36\r\nBreathe,New Delhi,Healthcare,50,0.33,6/14/2022,Series A,India,6\r\nHunty,Bogota,HR,30,NULL,6/14/2022,Seed,Colombia,6\r\nTIFIN,Boulder,Crypto,24,0.1,6/14/2022,Series D,United States,204\r\nAddi,Bogota,Finance,NULL,NULL,6/14/2022,Series C,Colombia,376\r\nShopee,Singapore,Food,NULL,NULL,6/14/2022,Unknown,Singapore,NULL\r\nBlockFi,New York City,Crypto,250,0.2,6/13/2022,Series E,United States,1000\r\nWave Sports and Entertainment,Los Angeles,Media,56,0.33,6/13/2022,Series B,United States,65\r\nStudio,SF Bay Area,Education,33,0.4,6/13/2022,Series B,United States,50\r\nAutomox,Boulder,Infrastructure,NULL,NULL,6/13/2022,Series C,United States,152\r\nDesktop Metal,Boston,Other,NULL,0.12,6/13/2022,Post-IPO,United States,811\r\nCrypto.com,Singapore,Crypto,260,0.05,6/10/2022,Unknown,Singapore,156\r\nFarEye,New Delhi,Logistics,250,0.3,6/10/2022,Series E,India,150\r\nBerlin Brands Group,Berlin,Retail,100,0.1,6/10/2022,Unknown,Germany,1000\r\nSanar,Sao Paulo,Healthcare,60,0.2,6/10/2022,Unknown,Brazil,11\r\nFreetrade,London,Finance,45,0.15,6/10/2022,Unknown,United Kingdom,133\r\nAlbert,Los Angeles,Finance,20,0.08,6/10/2022,Series C,United States,175\r\nKeepe,Seattle,Real Estate,NULL,NULL,6/10/2022,Unknown,United States,6\r\nLiongard,Houston,Infrastructure,NULL,NULL,6/10/2022,Series B,United States,22\r\nZiroom,Beijing,Real Estate,NULL,0.2,6/10/2022,Unknown,China,2100\r\nOneTrust,Atlanta,Security,950,0.25,6/9/2022,Series C,United States,926\r\nStitch Fix,SF Bay Area,Retail,330,0.15,6/9/2022,Post-IPO,United States,79\r\nDaniel Wellington,Stockholm,Retail,200,0.15,6/9/2022,Unknown,Sweden,NULL\r\nConvoy,Seattle,Logistics,90,0.07,6/9/2022,Series E,United States,1100\r\nHologram,Chicago,Infrastructure,80,0.4,6/9/2022,Series B,United States,82\r\nBoozt,Malmo,Retail,70,0.05,6/9/2022,Post-IPO,Sweden,56\r\nThe Grommet,Boston,Retail,40,1,6/9/2022,Acquired,United States,5\r\nStashaway,Singapore,Finance,31,0.14,6/9/2022,Series D,Singapore,61\r\nPreply,Boston,Education,26,0.05,6/9/2022,Series C,United States,100\r\nJellysmack,New York City,Media,NULL,0.08,6/9/2022,Series C,United States,22\r\nStarship,SF Bay Area,Transportation,NULL,0.11,6/9/2022,Series B,United States,197\r\nTrade Republic,Berlin,Finance,NULL,NULL,6/9/2022,Series C,Germany,1200\r\nSonder,SF Bay Area,Travel,250,0.21,6/8/2022,Post-IPO,United States,839\r\nKavak,Sao Paulo,Transportation,150,NULL,6/8/2022,Series E,Brazil,1600\r\nTruepill,SF Bay Area,Healthcare,150,0.15,6/8/2022,Series D,United States,255\r\niPrice Group,Kuala Lumpur,Retail,50,0.2,6/8/2022,Unknown,Malaysia,26\r\nMemmo,Stockholm,Consumer,NULL,0.4,6/8/2022,Unknown,Sweden,24\r\nCazoo,London,Transportation,750,0.15,6/7/2022,Post-IPO,United Kingdom,2000\r\nCazoo,London,Transportation,750,0.15,6/7/2022,Post-IPO,United Kingdom,2000\r\nRupeek,Bengaluru,Finance,180,0.15,6/7/2022,Unknown,India,172\r\nLummo,Jakarta,Marketing,150,NULL,6/7/2022,Series C,Indonesia,149\r\nBird,Los Angeles,Transportation,138,0.23,6/7/2022,Post-IPO,United States,783\r\nID.me,Washington D.C.,Security,130,NULL,6/7/2022,Unknown,United States,275\r\nKiwiCo,SF Bay Area,Retail,40,NULL,6/7/2022,Unknown,United States,11\r\nBond,SF Bay Area,Finance,NULL,NULL,6/7/2022,Series A,United States,42\r\nCyberCube,SF Bay Area,Finance,NULL,NULL,6/7/2022,Series B,United States,35\r\nPropzy,Ho Chi Minh City,Real Estate,NULL,0.5,6/7/2022,Series A,Vietnam,33\r\nClearco,Toronto,Finance,60,NULL,6/6/2022,Series C,Canada,681\r\nDutchie,Bend,Other,50,0.07,6/6/2022,Series D,United States,603\r\nClearco,Dublin,Finance,NULL,NULL,6/6/2022,Series C,Ireland,681\r\nDeep Instinct,New York City,Security,NULL,0.1,6/6/2022,Series D,United States,259\r\nSendoso,SF Bay Area,Marketing,NULL,0.14,6/6/2022,Series C,United States,152\r\nEruditus,Mumbai,Education,40,NULL,6/4/2022,Series E,India,1200\r\nAfterverse,Brasilia,Consumer,60,0.2,6/3/2022,Unknown,Brazil,NULL\r\nSuperhuman,SF Bay Area,Consumer,23,0.22,6/3/2022,Series C,United States,108\r\nFood52,New York City,Food,21,0.15,6/3/2022,Acquired,United States,176\r\n5B Solar,Sydney,Energy,NULL,0.25,6/3/2022,Series A,Australia,12\r\nClubhouse,SF Bay Area,Consumer,NULL,NULL,6/3/2022,Series C,United States,110\r\nTesla,Austin,Transportation,NULL,0.1,6/3/2022,Post-IPO,United States,20200\r\nCarbon Health,SF Bay Area,Healthcare,250,0.08,6/2/2022,Series D,United States,522\r\nFavo,Sao Paulo,Food,170,NULL,6/2/2022,Series A,Brazil,31\r\nPolicyGenius,New York City,Finance,170,0.25,6/2/2022,Series E,United States,286\r\nYojak,Gurugram,Construction,140,0.5,6/2/2022,Seed,India,3\r\nEnvato,Melbourne,Marketing,100,NULL,6/2/2022,Unknown,Australia,NULL\r\nGemini,New York City,Crypto,100,0.1,6/2/2022,Unknown,United States,423\r\nStord,Atlanta,Logistics,59,0.08,6/2/2022,Series D,United States,325\r\nGather,SF Bay Area,Consumer,30,0.33,6/2/2022,Series B,United States,76\r\nShef,SF Bay Area,Food,29,NULL,6/2/2022,Series A,United States,28\r\nIRL,SF Bay Area,Consumer,25,0.25,6/2/2022,Series C,United States,197\r\nEsme Learning,Boston,Education,NULL,NULL,6/2/2022,Unknown,United States,37\r\nKaodim,Selangor,Consumer,NULL,1,6/2/2022,Series B,United States,17\r\nRain,Manama,Finance,NULL,NULL,6/2/2022,Series B,Bahrain,202\r\nTomTom,Amsterdam,Transportation,500,0.1,6/1/2022,Post-IPO,Netherlands,NULL\r\nCybereason,Boston,Security,100,0.06,6/1/2022,Series F,United States,750\r\nUdayy,Gurugram,Education,100,1,6/1/2022,Seed,India,2\r\n2TM,Sao Paulo,Crypto,90,0.12,6/1/2022,Unknown,Brazil,250\r\nCurve,London,Finance,65,0.1,6/1/2022,Series C,United Kingdom,182\r\nLoom,SF Bay Area,Product,34,0.14,6/1/2022,Series C,United States,203\r\nSkillshare,New York City,Education,31,NULL,6/1/2022,Unknown,United States,136\r\nImpala,London,Travel,30,0.35,6/1/2022,Series B,United Kingdom,32\r\nEaze,SF Bay Area,Consumer,25,NULL,6/1/2022,Series D,United States,202\r\nSide,SF Bay Area,Real Estate,NULL,0.1,6/1/2022,Unknown,United States,313\r\nTruck It In,Karachi,Logistics,NULL,0.3,6/1/2022,Seed,Pakistan,17\r\nPlaytika,Tel Aviv,Consumer,250,0.06,5/31/2022,Post-IPO,Israel,NULL\r\nReplicated,Los Angeles,Infrastructure,50,NULL,5/31/2022,Series C,United States,85\r\nTomo,Stamford,Finance,44,0.33,5/31/2022,Series A,United States,110\r\nGetta,Tel Aviv,Transportation,30,NULL,5/31/2022,Series A,Israel,49\r\nBookClub,Salt Lake City,Education,12,0.25,5/31/2022,Series A,United States,26\r\nCerebral,SF Bay Area,Healthcare,NULL,NULL,5/31/2022,Series C,United States,462\r\nSWVL,Dubai,Transportation,400,0.32,5/30/2022,Post-IPO,United Arab Emirates,132\r\nMobile Premier League,Bengaluru,Consumer,100,0.1,5/30/2022,Series E,India,375\r\nSumUp,Sao Paulo,Finance,100,0.03,5/30/2022,Unknown,Brazil,50\r\nTempus Ex,SF Bay Area,Data,NULL,NULL,5/30/2022,Unknown,United States,36\r\nFrontRow,Bengaluru,Education,145,0.3,5/27/2022,Series A,India,17\r\nDaloopa,New Delhi,Data,40,NULL,5/27/2022,Series A,India,23\r\nUncapped,London,Finance,29,0.26,5/27/2022,Unknown,United Kingdom,118\r\nAkerna,Denver,Logistics,NULL,NULL,5/27/2022,Unknown,United States,46\r\nTerminus,Atlanta,Marketing,NULL,NULL,5/27/2022,Series C,United States,120\r\nTerminus,Atlanta,Marketing,NULL,NULL,5/27/2022,Unknown,United States,192\r\nVTEX,Sao Paulo,Retail,200,0.13,5/26/2022,Post-IPO,Brazil,365\r\nBitso,Mexico City,Crypto,80,0.11,5/26/2022,Series C,Mexico,378\r\nFoodpanda,Bucharest,Food,80,NULL,5/26/2022,Acquired,Romania,749\r\nDazn,London,Consumer,50,0.02,5/26/2022,Unknown,United Kingdom,NULL\r\nLacework,SF Bay Area,Security,300,0.2,5/25/2022,Series D,United States,1900\r\nBolt,SF Bay Area,Finance,240,0.27,5/25/2022,Series E,United States,1300\r\nPeerStreet,Los Angeles,Finance,75,NULL,5/25/2022,Series C,United States,121\r\nKontist,Berlin,Finance,50,0.25,5/25/2022,Series B,Germany,53\r\nNuri,Berlin,Finance,45,0.2,5/25/2022,Series B,Germany,42\r\nCoterie Insurance,Cincinnati,Finance,30,0.2,5/25/2022,Series B,United States,70\r\nAirlift,Lahore,Logistics,NULL,0.31,5/25/2022,Series B,Pakistan,109\r\nGetir,Istanbul,Food,NULL,0.14,5/25/2022,Series E,Turkey,1800\r\nZapp,London,Food,NULL,0.1,5/25/2022,NULL,United Kingdom,300\r\nGorillas,Berlin,Food,300,0.5,5/24/2022,Series C,Germany,1300\r\nZenius,Jakarta,Education,200,NULL,5/24/2022,Series B,Indonesia,20\r\nThe Zebra,Austin,Finance,40,NULL,5/24/2022,Series D,United States,256\r\nKlarna,Stockholm,Finance,700,0.1,5/23/2022,Unknown,Sweden,3700\r\nPayPal,SF Bay Area,Finance,83,NULL,5/23/2022,Post-IPO,United States,216\r\nBuenbit,Buenos Aires,Crypto,80,0.45,5/23/2022,Series A,Argentina,11\r\nBeyondMinds,Tel Aviv,Data,65,1,5/23/2022,Series A,Israel,16\r\nClickUp,San Diego,Other,60,0.07,5/23/2022,Series C,United States,537\r\nAirtime,New York City,Consumer,30,0.2,5/23/2022,Series B,United States,33\r\nOlist,Curitiba,Retail,NULL,NULL,5/23/2022,Series E,Brazil,322\r\nMFine,Bengaluru,Healthcare,600,0.75,5/21/2022,Series C,India,97\r\nLatch,New York City,Security,130,0.28,5/20/2022,Post-IPO,United States,342\r\nOutside,Boulder,Media,87,0.15,5/20/2022,Series B,United States,174\r\nSkillz,SF Bay Area,Consumer,70,0.1,5/20/2022,Post-IPO,United States,287\r\nCars24,Gurugram,Transportation,600,0.06,5/19/2022,Series G,India,1300\r\nVedantu,Bengaluru,Education,424,0.07,5/18/2022,Series E,India,292\r\nNetflix,SF Bay Area,Media,150,0.01,5/17/2022,Post-IPO,United States,121900\r\nKry,Stockholm,Healthcare,100,0.1,5/17/2022,Series D,Sweden,568\r\nPicsart,Miami,Consumer,90,0.08,5/17/2022,Series C,United States,195\r\nZak,Sao Paulo,Food,100,0.4,5/16/2022,Series A,United States,29\r\nZulily,Seattle,Retail,NULL,NULL,5/16/2022,Acquired,United States,194\r\nAliExpress Russia,Moscow,Retail,400,0.4,5/14/2022,Acquired,Russia,60\r\nThirty Madison,New York City,Healthcare,24,NULL,5/14/2022,Acquired,United States,209\r\nCommonBond,New York City,Finance,22,NULL,5/13/2022,Series D,United States,130\r\nSubspace,Los Angeles,Infrastructure,NULL,1,5/13/2022,Series B,United States,NULL\r\nZwift,Los Angeles,Fitness,150,NULL,5/12/2022,Series C,United States,619\r\nSection4,New York City,Education,32,NULL,5/12/2022,Series A,United States,37\r\nTripwire,Portland,Security,NULL,NULL,5/12/2022,Acquired,United States,29\r\nDataRobot,Boston,Data,70,0.07,5/11/2022,Series G,United States,1000\r\nCarvana,Phoenix,,2500,0.12,5/10/2022,Post-IPO,United States,1600\r\nDoma,SF Bay Area,Finance,310,0.15,5/10/2022,Post-IPO,United States,679\r\nPollen,London,Travel,200,0.33,5/10/2022,Series C,United Kingdom,238\r\nLatch,New York City,Security,30,0.06,5/10/2022,Post-IPO,United States,342\r\nMeero,Paris,Other,350,0.5,5/9/2022,Series C,France,293\r\nVroom,New York City,Transportation,270,0.14,5/9/2022,Post-IPO,United States,1300\r\nReef,Miami,Transportation,750,0.05,5/6/2022,Unknown,United States,1500\r\ndivvyDOSE,Davenport,Healthcare,62,NULL,5/6/2022,Acquired,United States,0\r\nVedantu,Bengaluru,Education,200,0.03,5/5/2022,Series E,India,292\r\nMural,SF Bay Area,Product,90,0.1,5/5/2022,Series C,United States,192\r\nOn Deck,SF Bay Area,Education,72,0.25,5/5/2022,Series A,United States,20\r\nSEND,Sydney,Food,300,1,5/4/2022,Seed,Australia,3\r\nColossus,Boston,Energy,97,NULL,5/4/2022,Series A,United States,41\r\nCameo,Chicago,Consumer,87,0.25,5/4/2022,Unknown,United States,165\r\nMainstreet,SF Bay Area,Finance,45,0.3,5/4/2022,Series A,United States,64\r\nIdeoclick,Seattle,Retail,40,NULL,5/4/2022,Series A,United States,7\r\nVise,New York City,Finance,25,NULL,5/4/2022,Series C,United States,128\r\nBizpay,Sydney,Finance,NULL,0.3,5/4/2022,Unknown,Australia,45\r\nThrasio,Boston,Retail,NULL,NULL,5/2/2022,Unknown,United States,3400\r\nAvo,Tel Aviv,Food,500,0.67,5/1/2022,Series B,Israel,45\r\nNoom,New York City,Healthcare,495,NULL,4/29/2022,Series F,United States,657\r\nDomestika,SF Bay Area,Education,150,0.19,4/28/2022,Series D,United States,130\r\nNetflix,SF Bay Area,Media,25,NULL,4/28/2022,Post-IPO,United States,121900\r\nWahoo Fitness,Atlanta,Fitness,50,NULL,4/27/2022,Unknown,United States,NULL\r\nRobinhood,SF Bay Area,Finance,340,0.09,4/26/2022,Post-IPO,United States,5600\r\nBonsai,Toronto,Retail,29,0.34,4/26/2022,Series A,Canada,27\r\nSigfox,Toulouse,Other,64,NULL,4/25/2022,Acquired,France,277\r\nClyde,New York City,Marketing,22,NULL,4/25/2022,Series B,United States,58\r\nXiaohongshu,Shanghai,Consumer,180,0.09,4/21/2022,Series E,China,917\r\nFacily,Sao Paulo,Retail,260,0.3,4/20/2022,Series D,Brazil,502\r\nLemonade,New York City,Finance,52,NULL,4/20/2022,Post-IPO,United States,481\r\nBlend,SF Bay Area,Finance,200,0.1,4/19/2022,Post-IPO,United States,665\r\nQuintoAndar,Sao Paulo,Real Estate,160,0.04,4/19/2022,Series E,Brazil,755\r\nLoft,Sao Paulo,Real Estate,159,NULL,4/19/2022,Unknown,Brazil,788\r\nBetter.com,New York City,Real Estate,NULL,NULL,4/19/2022,Unknown,United States,905\r\nAutomox,Boulder,Infrastructure,NULL,0.11,4/18/2022,Series C,United States,152\r\nHumble,SF Bay Area,Media,10,NULL,4/15/2022,Acquired,United States,4\r\nHalcyon Health,New York City,Healthcare,NULL,1,4/15/2022,Unknown,United States,2\r\nAhead,SF Bay Area,Healthcare,44,1,4/14/2022,Unknown,United States,9\r\nTruepill,SF Bay Area,Healthcare,NULL,NULL,4/14/2022,Series D,United States,255\r\nRad Power Bikes,Seattle,Transportation,100,0.14,4/12/2022,Series D,United States,329\r\nMeesho,Bengaluru,Retail,150,NULL,4/11/2022,Series F,India,1100\r\nFood52,New York City,Food,20,0.1,4/8/2022,Acquired,United States,176\r\nUnacademy,Bengaluru,Education,1000,0.17,4/7/2022,Series H,India,838\r\nGoodfood,Calgary,Food,70,NULL,4/7/2022,Post-IPO,Canada,16\r\nWorkrise,Austin,Energy,450,NULL,4/5/2022,Series E,United States,752\r\nFast,SF Bay Area,Finance,NULL,1,4/5/2022,Series B,United States,124\r\nBitMEX,Non-U.S.,Crypto,75,0.25,4/4/2022,Seed,Seychelles,0\r\nLegible,Vancouver,Media,23,0.38,4/4/2022,Post-IPO,Canada,3\r\nLightico,Tel Aviv,Finance,20,NULL,3/31/2022,Series B,Israel,42\r\nSea,Bengaluru,Retail,350,NULL,3/30/2022,Post-IPO,India,8600\r\nRasa,Berlin,Data,59,0.4,3/30/2022,Series B,Germany,40\r\nGopuff,Philadelphia,Food,450,0.03,3/29/2022,Series H,United States,3400\r\nThinkific,Vancouver,Education,100,0.2,3/29/2022,Post-IPO,Canada,22\r\nFurlenco,Bengaluru,Retail,180,NULL,3/26/2022,Series D,India,228\r\nGrove Collaborative,SF Bay Area,Retail,NULL,0.17,3/19/2022,Series E,United States,474\r\nStorytel,Stockholm,Media,100,0.1,3/17/2022,Post-IPO,Sweden,275\r\nCurology,SF Bay Area,Healthcare,150,NULL,3/16/2022,Series D,United States,19\r\nTrell,Bengaluru,Retail,300,0.5,3/15/2022,Series B,India,62\r\nKnock,New York City,Real Estate,115,0.46,3/15/2022,Unknown,United States,654\r\nTalis Biomedical,SF Bay Area,Healthcare,NULL,0.25,3/15/2022,Post-IPO,United States,8\r\nSezzle,Minneapolis,Finance,NULL,0.2,3/10/2022,Post-IPO,United States,301\r\nBetter.com,New York City,Real Estate,3000,0.33,3/8/2022,Unknown,United States,905\r\nAdaptive Biotechnologies,Seattle,Healthcare,100,0.12,3/8/2022,Post-IPO,United States,406\r\nHyperscience,New York City,Data,100,0.25,3/3/2022,Series E,United States,289\r\nWeDoctor,Non-U.S.,Healthcare,500,NULL,3/2/2022,Series F,China,1400\r\nWish,SF Bay Area,Retail,190,0.15,3/1/2022,Post-IPO,United States,1600\r\niFit,Logan,Fitness,NULL,NULL,2/25/2022,Private Equity,United States,200\r\nOKCredit,New Delhi,Finance,30,NULL,2/24/2022,Series B,India,85\r\nLido,Mumbai,Education,150,NULL,2/21/2022,Series A,India,24\r\nVirgin Hyperloop,Los Angeles,Transportation,111,0.5,2/21/2022,Unknown,United States,368\r\nTrustly,Stockholm,Finance,120,NULL,2/17/2022,Acquired,Sweden,28\r\nLiv Up,Sao Paulo,Food,100,0.15,2/16/2022,Series D,Brazil,118\r\nHomie,Salt Lake City,Real Estate,119,0.29,2/14/2022,Series B,United States,35\r\nHopin,London,Other,138,0.12,2/10/2022,Series D,United States,1000\r\nDaily Harvest,New York City,Food,60,0.2,2/10/2022,Series D,United States,120\r\nPeloton,New York City,Fitness,2800,0.2,2/8/2022,Post-IPO,United States,1900\r\nDefined.ai,Seattle,Data,NULL,NULL,2/7/2022,Series B,United States,78\r\nRhino,New York City,Real Estate,57,0.2,2/3/2022,Unknown,United States,133\r\nGopuff,Philadelphia,Food,100,NULL,1/26/2022,Series H,United States,3400\r\nGlossier,New York City,Consumer,80,0.33,1/26/2022,Series E,United States,266\r\nRoot Insurance,Columbus,Finance,330,NULL,1/20/2022,Post-IPO,United States,527\r\nSpin,SF Bay Area,Transportation,NULL,0.25,1/8/2022,Acquired,United States,8\r\nDelivery Hero,Berlin,Food,300,NULL,12/22/2021,Post-IPO,Germany,8300\r\niFit,Logan,Fitness,NULL,NULL,12/8/2021,Private Equity,United States,200\r\nBetter.com,New York City,Real Estate,900,0.09,12/1/2021,Unknown,United States,905\r\nBitTitan,Seattle,Data,70,0.27,11/18/2021,Acquired,United States,46\r\nZillow,Seattle,Real Estate,2000,0.25,11/2/2021,Post-IPO,United States,97\r\nInVision,New York City,Product,22,NULL,10/5/2021,Unknown,United States,356\r\nOzy Media,SF Bay Area,Media,NULL,1,10/1/2021,Series C,United States,70\r\nZymergen,SF Bay Area,Other,120,NULL,9/23/2021,Post-IPO,United States,974\r\nImperfect Foods,SF Bay Area,Food,NULL,NULL,9/22/2021,Series D,United States,229\r\nGenius,New York City,Consumer,NULL,NULL,9/15/2021,Acquired,United States,78\r\nTreehouse,Portland,Education,41,0.9,9/14/2021,Series B,United States,12\r\nCasper,New York City,Retail,NULL,NULL,9/14/2021,Post-IPO,United States,339\r\nTanium,Seattle,Security,30,NULL,8/30/2021,Unknown,United States,1000\r\nFlockjay,SF Bay Area,Education,37,0.5,8/24/2021,Series A,United States,14\r\nBytedance,Shanghai,Consumer,1800,NULL,8/5/2021,Unknown,China,9400\r\nPagarbook,Bengaluru,HR,80,NULL,7/26/2021,Series A,India,17\r\nKaterra,SF Bay Area,Construction,2434,1,6/1/2021,Unknown,United States,1600\r\nSumUp,London,Finance,NULL,NULL,5/5/2021,Unknown,United Kingdom,53\r\nLambda School,SF Bay Area,Education,65,NULL,4/29/2021,Series C,United States,122\r\nMadefire,SF Bay Area,Media,NULL,1,4/29/2021,Series B,United States,16\r\nPatreon,SF Bay Area,Media,36,NULL,4/26/2021,Series D,United States,165\r\nNew Relic,SF Bay Area,Infrastructure,160,0.07,4/6/2021,Post-IPO,United States,214.5\r\nMedium,SF Bay Area,Media,NULL,NULL,3/24/2021,Series C,United States,132\r\nHuffPo,New York City,Media,47,NULL,3/9/2021,Acquired,United States,37\r\nSubspace,Los Angeles,Infrastructure,NULL,NULL,3/3/2021,Series B,United States,NULL\r\nClumio,SF Bay Area,Data,NULL,NULL,3/1/2021,Series C,United States,186\r\nDJI,SF Bay Area,Consumer,NULL,NULL,2/24/2021,Unknown,United States,105\r\nNinjacart,Bengaluru,Food,200,NULL,2/23/2021,Unknown,India,194\r\nBounce,Bengaluru,Transportation,200,0.4,2/22/2021,Series D,India,214.2\r\nThredUp,Chicago,Retail,243,NULL,2/9/2021,Series F,United States,305\r\nIndigo,Boston,Other,80,NULL,2/9/2021,Series F,United States,1200\r\nLimelight,New York City,Recruiting,13,1,2/4/2021,Unknown,United States,NULL\r\nQuandoo,Berlin,Food,87,0.2,2/3/2021,Acquired,Germany,39\r\nHubba,Toronto,Retail,45,1,2/1/2021,Series B,Canada,61\r\nBytedance,Mumbai,Consumer,1800,NULL,1/27/2021,Unknown,India,7400\r\nPrivitar,London,Data,20,NULL,1/27/2021,Series C,United Kingdom,150\r\nShutterfly,SF Bay Area,Retail,800,NULL,1/25/2021,Acquired,United States,50\r\nPostmates,SF Bay Area,Food,180,0.15,1/23/2021,Acquired,United States,763\r\nInstacart,SF Bay Area,Food,1877,NULL,1/21/2021,Unknown,United States,2400\r\nPocketmath,Singapore,Marketing,21,1,1/20/2021,Unknown,Singapore,20\r\nDropbox,SF Bay Area,Other,315,0.15,1/13/2021,Post-IPO,United States,1700\r\nAura Financial,SF Bay Area,Finance,NULL,1,1/11/2021,Unknown,United States,584\r\nSimple,Portland,Finance,NULL,1,1/7/2021,Acquired,United States,15\r\nWhiteHat Jr,Mumbai,Education,1800,NULL,1/6/2021,Acquired,India,11\r\nPulse Secure,SF Bay Area,Security,78,NULL,12/23/2020,Acquired,United States,NULL\r\nBreather,Montreal,Real Estate,120,0.8,12/16/2020,Series D,Canada,131\r\nActifio,Boston,Data,54,NULL,12/16/2020,Acquired,United States,352\r\nOYO,Gurugram,Travel,600,NULL,12/8/2020,Series F,India,3200\r\nZinier,SF Bay Area,HR,NULL,NULL,11/25/2020,Series C,United States,120\r\nAya,Toronto,Finance,5,0.25,11/19/2020,Seed,United States,3\r\nDomio,New York City,Real Estate,NULL,0.5,11/18/2020,Series B,United States,116\r\nBridge Connector,Nashville,Healthcare,154,1,11/17/2020,Series B,United States,45\r\nTidepool,SF Bay Area,Healthcare,18,0.4,11/17/2020,Unknown,United States,NULL\r\nIgenous,Seattle,Data,NULL,NULL,11/17/2020,Series C,United States,67\r\nScoop,SF Bay Area,Transportation,NULL,NULL,11/17/2020,Series C,United States,95\r\nWorksmith,Austin,Retail,30,0.5,11/9/2020,Unknown,United States,3.8\r\nRubica,Seattle,Security,NULL,1,11/5/2020,Series A,United States,14\r\nBossa Nova,SF Bay Area,Retail,NULL,0.5,11/2/2020,Unknown,United States,101.6\r\nLivePerson,New York City,Support,30,NULL,11/1/2020,Post-IPO,United States,42\r\nRemedy,Austin,Healthcare,82,NULL,10/29/2020,Series A,United States,12.5\r\nKnotel,New York City,Real Estate,20,0.08,10/29/2020,Series C,United States,560\r\nCheetah,SF Bay Area,Food,NULL,NULL,10/25/2020,Series B,United States,67\r\nCodeCombat,SF Bay Area,Education,8,NULL,10/23/2020,Series A,United States,8\r\nQuibi,Los Angeles,Media,NULL,1,10/21/2020,Private Equity,United States,1800\r\nZomato,Jakarta,Food,NULL,NULL,10/20/2020,Series J,Indonesia,914\r\nGetYourGuide,Berlin,Travel,90,0.17,10/14/2020,Series E,Germany,656\r\nOLX India,New Delhi,Marketing,250,NULL,10/10/2020,Acquired,India,28\r\nChef,Seattle,Infrastructure,NULL,NULL,10/8/2020,Acquired,United States,105\r\nAlto Pharmacy,SF Bay Area,Healthcare,47,0.06,9/29/2020,Series D,United States,356\r\nTheWrap,Los Angeles,Media,NULL,NULL,9/29/2020,Series B,United States,3.5\r\nHumanForest,London,Transportation,NULL,NULL,9/25/2020,Private Equity,United Kingdom,1.54\r\nWeWork ,Shenzen,Real Estate,NULL,NULL,9/23/2020,Series H,China,19500\r\nAir,New York City,Marketing,NULL,0.16,9/16/2020,Series A,United States,18\r\nNS8,Las Vegas,Data,240,0.95,9/11/2020,Series A,United States,157.9\r\nBleacher Report,London,Media,20,NULL,9/11/2020,Acquired,United Kingdom,40.5\r\nHubHaus,SF Bay Area,Real Estate,NULL,1,9/11/2020,Series A,United States,13.4\r\nWaze,SF Bay Area,Transportation,30,0.054,9/9/2020,Acquired,United States,67\r\nOuster,SF Bay Area,Transportation,NULL,0.1,9/8/2020,Series B,United States,132\r\nSwing Education,SF Bay Area,Education,NULL,NULL,9/5/2020,Series B,United States,22.8\r\nAkerna,Denver,Logistics,NULL,NULL,9/2/2020,Post-IPO,United States,NULL\r\nAwok,Dubai,Retail,NULL,1,9/2/2020,Series A,United Arab Emirates,30\r\nBig Fish Games,Seattle,Media,250,NULL,9/1/2020,Acquired,United States,95.2\r\nGoBear,Singapore,Finance,22,0.11,9/1/2020,Unknown,Singapore,97\r\nMakeMyTrip,New Delhi,Travel,350,0.1,8/31/2020,Post-IPO,India,548\r\nSalesforce,SF Bay Area,Sales,1000,0.0185,8/26/2020,Post-IPO,United States,65.4\r\nkununu,Boston,Recruiting,NULL,NULL,8/26/2020,Acquired,United States,NULL\r\nSuperloop,Brisbane,Infrastructure,30,NULL,8/24/2020,Post-IPO,Australia,36\r\nSpaces,Los Angeles,Media,NULL,NULL,8/24/2020,Acquired,United States,9.5\r\nStreamSets,SF Bay Area,Data,NULL,NULL,8/20/2020,Series C,United States,76.2\r\nDocly,London,Healthcare,8,0.8,8/19/2020,Seed,United Kingdom,15.5\r\nMapify,Berlin,Travel,NULL,NULL,8/19/2020,Seed,Germany,1.3\r\nLumina Networks,SF Bay Area,Infrastructure,NULL,1,8/18/2020,Series A,United States,10\r\nDJI,Shenzen,Consumer,NULL,NULL,8/17/2020,Unknown,China,105\r\nShopify,Ottawa,Retail,30,NULL,8/14/2020,Post-IPO,Canada,122.3\r\nInVision,New York City,Product,NULL,NULL,8/14/2020,Series F,United States,350.2\r\nHopSkipDrive,Los Angeles,Transportation,NULL,NULL,8/12/2020,Unknown,United States,45\r\nMozilla,SF Bay Area,Consumer,250,0.25,8/11/2020,Unknown,United States,2.3\r\nEatsy,Singapore,Food,20,1,8/8/2020,Seed,Singapore,0.9755\r\nGlossier,New York City,Retail,150,0.38,8/7/2020,Series D,United States,186.4\r\nThe Appraisal Lane,Montevideo,Transportation,NULL,NULL,8/7/2020,Seed,Uruguay,1.8\r\nThriver,Toronto,Food,75,0.5,8/6/2020,Series B,Canada,53\r\nVesta,Atlanta,Sales,56,NULL,8/5/2020,Unknown,United States,20\r\nBooking.com,Amsterdam,Travel,4375,0.25,7/30/2020,Acquired,Netherlands,NULL\r\nBuy.com / Rakuten,SF Bay Area,Retail,87,1,7/30/2020,Acquired,United States,42.4\r\ntZero,New York City,Finance,NULL,NULL,7/30/2020,Acquired,United States,472\r\nPared,SF Bay Area,Food,NULL,NULL,7/29/2020,Unknown,United States,13\r\nProcore,Los Angeles,Construction,180,0.09,7/28/2020,Unknown,United States,649\r\nSwiggy,Bengaluru,Food,350,0.05,7/27/2020,Series I,India,1600\r\nZeitgold,Berlin,Finance,75,0.72,7/27/2020,Series B,Germany,60.18\r\nPerkbox,London,HR,NULL,NULL,7/27/2020,Unknown,United Kingdom,29.7\r\nCheckr,SF Bay Area,HR,64,0.12,7/23/2020,Series D,United States,309\r\nSorabel,Jakarta,Retail,NULL,1,7/23/2020,Series B,Indonesia,27\r\nLinkedIn,SF Bay Area,Recruiting,960,0.06,7/21/2020,Acquired,United States,154\r\nLighter Capital,Seattle,Finance,22,0.49,7/20/2020,Series B,United States,15\r\nCurefit,Bengaluru,Fitness,120,NULL,7/17/2020,Series D,India,404.6\r\nSnaptravel,Toronto,Travel,NULL,NULL,7/16/2020,Series A,Canada,22.4\r\nOptimizely,SF Bay Area,Marketing,60,0.15,7/15/2020,Series D,United States,251.2\r\nSkyscanner,Edinburgh,Travel,300,0.2,7/14/2020,Acquired,United Kingdom,197.2\r\nVox Media,Washington D.C.,Media,NULL,NULL,7/14/2020,Series F,United States,307.6\r\nYelp,SF Bay Area,Consumer,63,NULL,7/13/2020,Post-IPO,United States,56\r\nBizongo,Mumbai,Logistics,140,NULL,7/10/2020,Series C,India,70\r\nZilingo,Singapore,Retail,100,0.12,7/9/2020,Series D,Singapore,307\r\nPaySense,Mumbai,Finance,40,NULL,7/9/2020,Acquired,India,25.6\r\nFunding Circle,SF Bay Area,Finance,85,NULL,7/8/2020,Post-IPO,United States,746\r\nOnDeck,New York City,Finance,NULL,NULL,7/8/2020,Post-IPO,United States,1200\r\nThe Wing,New York City,Real Estate,56,NULL,7/1/2020,Series C,United States,117\r\nSharethrough,SF Bay Area,Marketing,18,NULL,7/1/2020,Series D,United States,38\r\nKongregate,SF Bay Area,Media,12,NULL,7/1/2020,Series C,United States,19\r\nHavenly,Denver,Consumer,5,NULL,7/1/2020,Series C,United States,57.8\r\nG2,Chicago,Marketing,17,0.05,6/30/2020,Series C,United States,100.8\r\nHired,SF Bay Area,Recruiting,NULL,NULL,6/30/2020,Series D,United States,132\r\nKaterra,SF Bay Area,Construction,400,0.07,6/29/2020,Series E,United States,1300\r\nBounce,Bengaluru,Transportation,130,0.22,6/29/2020,Series D,India,214.2\r\nArgo AI,Munich,Transportation,100,NULL,6/29/2020,Unknown,Germany,3600\r\nBossa Nova,SF Bay Area,Retail,61,NULL,6/29/2020,Unknown,United States,101.6\r\nNew Relic,Portland,Infrastructure,20,NULL,6/29/2020,Post-IPO,United States,214.5\r\nEngine eCommerce,Fayetteville,Retail,NULL,1,6/28/2020,Unknown,United States,4\r\nByton,SF Bay Area,Transportation,NULL,NULL,6/27/2020,Series C,United States,1200\r\nSprinklr,New York City,Support,NULL,NULL,6/25/2020,Series F,United States,228\r\nGoDaddy,Austin,Marketing,451,0.06,6/24/2020,Post-IPO,United States,NULL\r\nSonos,New York City,Consumer,174,0.12,6/24/2020,Post-IPO,United States,455.2\r\nOYO,Dallas,Travel,NULL,NULL,6/24/2020,Series F,United States,3200\r\nGojek,Jakarta,Transportation,430,0.09,6/23/2020,Series F,Indonesia,4800\r\nScaleFactor,Austin,Finance,90,0.9,6/23/2020,Series C,United States,103\r\nDark,SF Bay Area,Product,6,1,6/23/2020,Seed,United States,3\r\nIntuit,SF Bay Area,Finance,715,0.07,6/22/2020,Post-IPO,United States,18\r\nWeWork,London,Real Estate,200,NULL,6/19/2020,Series H,United Kingdom,19500\r\nAtlas Obscura ,New York City,Media,15,NULL,6/18/2020,Series B,United States,32\r\nNavi,Bengaluru,Finance,40,0.25,6/17/2020,Private Equity,India,582\r\nPaisaBazaar,Gurugram,Finance,1500,0.5,6/16/2020,Series G,India,496\r\nGrab,Singapore,Transportation,360,0.05,6/16/2020,Series I,Singapore,9900\r\nSplunk,SF Bay Area,Data,70,0.01,6/16/2020,Post-IPO,United States,40\r\nRedox,Madison,Healthcare,44,0.25,6/16/2020,Series C,United States,50\r\nConga,Denver,Data,NULL,0.11,6/15/2020,Acquired,United States,117\r\nStockwell AI,SF Bay Area,Retail,NULL,1,6/15/2020,Series B,United States,10\r\nUber,Amsterdam,Transportation,225,0.25,6/12/2020,Post-IPO,Netherlands,24700\r\nSynapseFI,SF Bay Area,Finance,63,0.48,6/12/2020,Series B,United States,50\r\nScaleFocus,Sofia,Infrastructure,120,0.1,6/11/2020,Unknown,Bulgaria,NULL\r\nBranch,New York City,Retail,3,0.27,6/11/2020,Seed,United States,2\r\nHer Campus Media,Boston,Media,10,0.18,6/10/2020,Unknown,United States,NULL\r\nIntegrate.ai,Toronto,Support,26,NULL,6/9/2020,Unknown,Canada,39.6\r\nThe Athletic,SF Bay Area,Media,46,0.08,6/5/2020,Series D,United States,139\r\nEthos Life,SF Bay Area,Finance,18,0.14,6/5/2020,Series C,United States,106\r\nLastline,SF Bay Area,Security,50,0.4,6/4/2020,Series C,United States,52\r\nBuilder,Los Angeles,Product,39,0.14,6/4/2020,Series A,United States,29\r\nOutdoorsy,SF Bay Area,Transportation,NULL,NULL,6/4/2020,Series C,United States,75\r\nMonzo,London,Finance,120,0.08,6/3/2020,Series F,United Kingdom,324\r\nKitty Hawk,SF Bay Area,Aerospace,70,NULL,6/3/2020,Unknown,United States,1\r\nSpotHero,Chicago,Transportation,40,0.21,6/3/2020,Series D,United States,117\r\nCredit Sesame,SF Bay Area,Finance,22,0.14,6/3/2020,Series F,United States,120\r\nCirc,Berlin,Transportation,100,NULL,6/2/2020,Acquired,Germany,55\r\nRivian,Detroit,Transportation,40,0.02,6/2/2020,Private Equity,United States,3100\r\nFundbox,SF Bay Area,Finance,14,0.15,6/2/2020,Series C,United States,453\r\nDescartes Labs,Santa Fe,Data,12,0.16,6/2/2020,Series B,United States,58\r\nMakerBot,New York City,Consumer,12,NULL,6/2/2020,Acquired,United States,10\r\nStitch Fix,SF Bay Area,Retail,1400,0.18,6/1/2020,Post-IPO,United States,79\r\nMakeMyTrip,Gurugram,Travel,350,0.1,6/1/2020,Post-IPO,India,548\r\nCrowdStreet,Portland,Real Estate,24,0.22,6/1/2020,Series C,United States,24\r\nBrex,SF Bay Area,Finance,62,0.15,5/29/2020,Series C,United States,732\r\nLoftium,Seattle,Real Estate,32,0.6,5/29/2020,Series A,United States,17\r\nBookMyShow,Mumbai,Consumer,270,0.18,5/28/2020,Unknown,India,224\r\nTrueCar,Los Angeles,Transportation,219,0.3,5/28/2020,Post-IPO,United States,340\r\nStubHub,SF Bay Area,Consumer,200,NULL,5/28/2020,Acquired,United States,19\r\nCulture Amp,Melbourne,HR,36,0.08,5/28/2020,Series E,Australia,157\r\nThe Sill,New York City,Retail,20,0.25,5/28/2020,Series A,United States,7\r\nInstructure,Salt Lake City,Education,150,0.12,5/27/2020,Acquired,United States,89\r\nEbanx,Curitiba,Finance,62,0.08,5/27/2020,Unknown,Brazil,30\r\nUber,Bengaluru,Transportation,600,0.23,5/26/2020,Post-IPO,India,24700\r\nBluprint,Denver,Education,137,1,5/26/2020,Acquired,United States,108\r\nAcorns,Portland,Finance,50,NULL,5/26/2020,Unknown,United States,207\r\nCarDekho,Gurugram,Transportation,200,NULL,5/25/2020,Series D,India,247\r\nTeamwork,Cork,Other,21,NULL,5/22/2020,Unknown,Ireland,NULL\r\nCvent,Washington D.C.,Marketing,400,0.1,5/21/2020,Acquired,United States,146\r\nPickYourTrail,Chennai,Travel,70,0.35,5/21/2020,Series A,India,3\r\nGlitch,New York City,Product,18,0.36,5/21/2020,Series A,United States,30\r\nKapten / Free Now,Paris,Transportation,NULL,NULL,5/21/2020,Acquired,France,100\r\nOla,Bengaluru,Transportation,1400,0.35,5/20/2020,Series J,India,3800\r\nSamsara,SF Bay Area,Logistics,300,0.18,5/20/2020,Series F,United States,530\r\nStay Alfred,Spokane,Travel,221,1,5/20/2020,Series B,United States,62\r\nSoFi,SF Bay Area,Finance,112,0.07,5/20/2020,Private Equity,United States,2500\r\nShareChat,Bengaluru,Marketing,101,0.25,5/20/2020,Series D,India,222\r\nIntercom,SF Bay Area,Support,39,0.06,5/20/2020,Series D,United States,240\r\nLivspace,Bengaluru,Retail,450,0.15,5/19/2020,Series D,India,157\r\nPollen,London,Travel,69,0.31,5/19/2020,Series B,United Kingdom,88\r\nDotscience,London,Product,10,1,5/19/2020,Unknown,United Kingdom,NULL\r\nDivvy,Salt Lake City,Finance,NULL,NULL,5/19/2020,Series C,United States,252\r\nUber,SF Bay Area,Transportation,3000,0.13,5/18/2020,Post-IPO,United States,24700\r\nAgoda,Singapore,Travel,1500,0.25,5/18/2020,Acquired,Singapore,NULL\r\nSwiggy,Bengaluru,Food,1100,0.14,5/18/2020,Series I,India,1600\r\nWeWork,New Delhi,Real Estate,100,0.2,5/18/2020,Series H,India,19500\r\nRubrik,SF Bay Area,Infrastructure,57,NULL,5/18/2020,Series E,United States,553\r\nIntapp,SF Bay Area,Legal,45,0.05,5/18/2020,Private Equity,United States,NULL\r\nCheckmarx,Tel Aviv,Security,NULL,NULL,5/18/2020,Acquired,Israel,92\r\nDatera,SF Bay Area,Infrastructure,NULL,0.1,5/18/2020,Series C,United States,63\r\nMagicbricks,Noida,Real Estate,250,NULL,5/17/2020,Unknown,India,300\r\nZomato,Gurugram,Food,520,0.13,5/15/2020,Series J,India,914\r\nLendingkart,Ahmedabad,Finance,500,0.5,5/15/2020,Unknown,India,200\r\nLattice,SF Bay Area,HR,16,0.1,5/15/2020,Series C,United States,49\r\nMasse,New York City,Retail,NULL,1,5/15/2020,Seed,United States,3\r\nCruise,SF Bay Area,Transportation,150,0.08,5/14/2020,Acquired,United States,5300\r\nQuartz,New York City,Media,80,0.4,5/14/2020,Acquired,United States,NULL\r\nIntegral Ad Science,New York City,Marketing,70,0.1,5/14/2020,Acquired,United States,116\r\nRidecell,SF Bay Area,Transportation,35,0.15,5/14/2020,Series B,United States,73\r\nVeem,SF Bay Area,Finance,30,NULL,5/14/2020,Unknown,United States,69\r\nSift,SF Bay Area,Security,NULL,NULL,5/14/2020,Series D,United States,106\r\nWorkfront,Salt Lake City,Marketing,NULL,NULL,5/14/2020,Series C,United States,375\r\nDeliv,SF Bay Area,Retail,669,1,5/13/2020,Series C,United States,80\r\nMercos,Joinville,Sales,51,0.4,5/13/2020,Unknown,Brazil,2\r\nKickstarter,New York City,Finance,25,0.18,5/13/2020,Unknown,United States,10\r\nIntersect,Toronto,Product,19,0.11,5/13/2020,Acquired,Canada,NULL\r\nMode Analytics,SF Bay Area,Data,17,NULL,5/13/2020,Series C,United States,46\r\nBetterCloud,New York City,Security,NULL,NULL,5/13/2020,Series E,United States,111\r\nWeFit,Hanoi,Fitness,NULL,1,5/13/2020,Seed,Vietnam,1\r\nZymergen ,SF Bay Area,Other,NULL,0.1,5/13/2020,Series C,United States,574\r\nStone,Sao Paulo,Finance,1300,0.2,5/12/2020,Post-IPO,Brazil,NULL\r\nZeus Living,SF Bay Area,Real Estate,73,0.5,5/12/2020,Series B,United States,79\r\nMixpanel,SF Bay Area,Data,65,0.19,5/12/2020,Series B,United States,77\r\nHireology,Chicago,Recruiting,36,0.17,5/12/2020,Series D,United States,52\r\nTop Hat,Toronto,Education,16,0.03,5/12/2020,Series D,Canada,104\r\nDatto,Norwalk,Infrastructure,NULL,NULL,5/12/2020,Acquired,United States,100\r\nPetal,New York City,Finance,NULL,NULL,5/12/2020,Series B,United States,47\r\nRevolut,London,Finance,60,0.03,5/11/2020,Series D,United Kingdom,837\r\nCadre,New York City,Real Estate,28,0.25,5/11/2020,Series C,United States,133\r\nN26,New York City,Finance,9,0.01,5/8/2020,Series D,United States,782\r\nJump,New York City,Transportation,500,1,5/7/2020,Acquired,United States,11\r\nGlassdoor,SF Bay Area,Recruiting,300,0.3,5/7/2020,Acquired,United States,204\r\nNumbrs,Zurich,Finance,62,0.5,5/7/2020,Series B,Switzerland,78\r\nFlywire,Boston,Finance,60,0.12,5/7/2020,Series E,United States,263\r\nSalesLoft,Atlanta,Sales,55,NULL,5/7/2020,Series D,United States,145\r\nTally,SF Bay Area,Finance,28,0.23,5/7/2020,Series C,United States,92\r\nAiry Rooms,Jakarta,Travel,NULL,1,5/7/2020,Unknown,Indonesia,NULL\r\nUber,SF Bay Area,Transportation,3700,0.14,5/6/2020,Post-IPO,United States,24700\r\nValidity,Boston,Data,130,0.33,5/6/2020,Private Equity,United States,NULL\r\nFlatiron School,New York City,Education,100,NULL,5/6/2020,Acquired,United States,15\r\nRubicon Project,Los Angeles,Marketing,50,0.08,5/6/2020,Post-IPO,United States,60\r\nSegment,SF Bay Area,Data,50,0.1,5/6/2020,Series D,United States,283\r\nOPay,Lagos,Finance,NULL,0.7,5/6/2020,Series B,Nigeria,170\r\nThoughtSpot,SF Bay Area,Data,NULL,NULL,5/6/2020,Series E,United States,743\r\nAirbnb,SF Bay Area,Travel,1900,0.25,5/5/2020,Private Equity,United States,5400\r\nJuul,SF Bay Area,Consumer,900,0.3,5/5/2020,Unknown,United States,1500\r\nAndela,New York City,Recruiting,135,0.1,5/5/2020,Series D,United States,181\r\nStack Overflow,New York City,Recruiting,40,0.15,5/5/2020,Series D,United States,68\r\nPipedrive,Tallinn,Sales,31,NULL,5/5/2020,Series C,Estonia,90\r\nWorkable,Boston,Recruiting,25,0.1,5/5/2020,Series C,United States,84\r\nCloudera,SF Bay Area,Infrastructure,NULL,NULL,5/5/2020,Post-IPO,United States,1000\r\nHandshake,SF Bay Area,Recruiting,NULL,NULL,5/5/2020,Series C,United States,74\r\nLeague,Toronto,Healthcare,NULL,NULL,5/5/2020,Unknown,Canada,76\r\nCureFit,Bengaluru,Fitness,800,0.16,5/4/2020,Series D,India,404\r\nCareem,Dubai,Transportation,536,0.31,5/4/2020,Acquired,United Arab Emirates,771\r\nOriente,Hong Kong,Finance,400,0.2,5/4/2020,Series B,Hong Kong,175\r\nVeriff,Tallinn,Security,63,0.21,5/4/2020,Series A,Estonia,7\r\nElement AI,Montreal,Other,62,0.15,5/4/2020,Series B,Canada,257\r\nDeputy,Sydney,HR,60,0.3,5/4/2020,Series B,Australia,106\r\nLoopio,Toronto,Sales,11,0.08,5/4/2020,Series A,Canada,11\r\nCastlight Health,SF Bay Area,Healthcare,NULL,0.13,5/4/2020,Post-IPO,United States,184\r\nTrivago,Dusseldorf,Travel,NULL,NULL,5/4/2020,Acquired,Germany,55\r\nTrax,Tel Aviv,Retail,120,0.1,5/3/2020,Series D,Israel,386\r\nLiveTiles,New York City,Other,50,NULL,5/3/2020,Post-IPO,United States,46\r\nOYO,London,Travel,150,NULL,5/1/2020,Series F,United Kingdom,2400\r\nNamely,New York City,HR,110,0.4,5/1/2020,Series E,United States,217\r\nCulture Trip,London,Media,95,0.32,5/1/2020,Series B,United Kingdom,102\r\nSandbox VR,SF Bay Area,Consumer,80,0.8,5/1/2020,Series A,United States,82\r\nVirtudent,Boston,Healthcare,70,NULL,5/1/2020,Series A,United States,10\r\nMonese,Lisbon,Finance,35,NULL,5/1/2020,Unknown,Portugal,80\r\nTheSkimm,New York City,Media,26,0.2,5/1/2020,Series C,United States,28\r\nAutomatic,SF Bay Area,Transportation,NULL,1,5/1/2020,Acquired,United States,24\r\nFlynote,Bengaluru,Travel,NULL,NULL,5/1/2020,Seed,India,1\r\nBullhorn,Boston,Sales,100,NULL,4/30/2020,Acquired,United States,32\r\nCare.com,Boston,Consumer,81,NULL,4/30/2020,Acquired,United States,156\r\nAirMap,Los Angeles,Aerospace,NULL,NULL,4/30/2020,Unknown,United States,75\r\nCohesity,SF Bay Area,Data,NULL,NULL,4/30/2020,Series E,United States,660\r\nFandom,SF Bay Area,Media,NULL,0.14,4/30/2020,Series E,United States,145\r\nPicoBrew,Seattle,Food,NULL,1,4/30/2020,Series A,United States,15\r\nLyft,SF Bay Area,Transportation,982,0.17,4/29/2020,Post-IPO,United States,4900\r\nWeWork,SF Bay Area,Real Estate,300,NULL,4/29/2020,Series H,United States,2250\r\nKayak / OpenTable,Stamford,Travel,160,0.08,4/29/2020,Acquired,United States,229\r\nKitopi,New York City,Food,124,NULL,4/29/2020,Series B,United States,89\r\nLime,SF Bay Area,Transportation,80,0.13,4/29/2020,Series D,United States,765\r\nTransfix,New York City,Logistics,24,0.1,4/29/2020,Series D,United States,128\r\nYoco,Cape Town,Finance,NULL,NULL,4/29/2020,Series B,South Africa,23\r\nTripAdvisor,Boston,Travel,900,0.25,4/28/2020,Post-IPO,United States,3\r\nRenmoney,Lagos,Finance,391,0.5,4/28/2020,Unknown,Nigeria,NULL\r\nDeliveroo,London,Food,367,0.15,4/28/2020,Series G,United Kingdom,1500\r\nApp Annie,SF Bay Area,Data,80,0.18,4/28/2020,Unknown,United States,156\r\nOpenX,Los Angeles,Marketing,35,0.15,4/28/2020,Unknown,United States,70\r\nPayJoy,SF Bay Area,Finance,27,0.25,4/28/2020,Series B,United States,71\r\nShipsi,Los Angeles,Retail,20,0.5,4/28/2020,Seed,United States,2\r\nDesktop Metal,Boston,Other,NULL,NULL,4/28/2020,Series E,United States,436\r\nMigo,SF Bay Area,Finance,NULL,0.25,4/28/2020,Series B,United States,37\r\nAutomation Anywhere,SF Bay Area,Other,260,0.1,4/27/2020,Series B,United States,840\r\nJetClosing,Seattle,Real Estate,20,0.2,4/27/2020,Series A,United States,24\r\nQwick,Phoenix,Recruiting,NULL,0.7,4/27/2020,Seed,United States,4\r\nOYO,Sao Paulo,Travel,500,NULL,4/25/2020,Series F,Brazil,2400\r\nStoqo,Jakarta,Food,250,1,4/25/2020,Series A,Indonesia,NULL\r\nSubmittable,Missoula,Other,30,0.2,4/25/2020,Series B,United States,17\r\nDivergent 3D,Los Angeles,Transportation,57,0.36,4/24/2020,Series B,United States,88\r\nAda Support,Toronto,Support,36,0.23,4/24/2020,Series B,Canada,60\r\nUPshow,Chicago,Marketing,19,0.3,4/24/2020,Series A,United States,7\r\nLighter Capital,Seattle,Finance,18,0.22,4/24/2020,Series B,United States,15\r\nHorizn Studios,Berlin,Travel,15,0.25,4/24/2020,Series B,Germany,30\r\nWelkin Health,SF Bay Area,Healthcare,10,0.33,4/24/2020,Series B,United States,29\r\nJiobit,Chicago,Consumer,6,0.21,4/24/2020,Unknown,United States,12\r\nTutorMundi,Sao Paulo,Education,4,1,4/24/2020,Series A,Brazil,2\r\nCheddar,New York City,Media,NULL,NULL,4/24/2020,Acquired,United States,54\r\nGoCardless,London,Finance,NULL,NULL,4/24/2020,Series E,United Kingdom,122\r\nStockX,Detroit,Retail,100,0.12,4/23/2020,Series C,United States,160\r\nZenefits,SF Bay Area,HR,87,0.15,4/23/2020,Series C,United States,583\r\nSisense,New York City,Data,80,0.09,4/23/2020,Series F,United States,274\r\nOscar Health,New York City,Healthcare,70,0.05,4/23/2020,Unknown,United States,1300\r\nSimon Data,New York City,Marketing,6,NULL,4/23/2020,Series C,United States,68\r\nConvoy,Seattle,Logistics,NULL,0.01,4/23/2020,Series D,United States,665\r\nPowerReviews,Chicago,Retail,NULL,NULL,4/23/2020,Acquired,United States,78\r\nSingular,SF Bay Area,Marketing,NULL,NULL,4/23/2020,Series B,United States,50\r\nMagic Leap,Miami,Consumer,1000,0.5,4/22/2020,Series E,United States,2600\r\nWhen I Work,Minneapolis,HR,55,0.35,4/22/2020,Series B,United States,24\r\nIke,SF Bay Area,Transportation,10,0.14,4/22/2020,Series A,United States,52\r\nAiry Rooms,Jakarta,Travel,NULL,0.7,4/22/2020,Unknown,Indonesia,NULL\r\nClearbit,SF Bay Area,Data,NULL,NULL,4/22/2020,Series A,United States,17\r\nExtraHop,Seattle,Security,NULL,NULL,4/22/2020,Series C,United States,61\r\nNearmap,Sydney,Construction,NULL,0.1,4/22/2020,Post-IPO,Australia,15\r\nSwiggy,Bengaluru,Food,800,NULL,4/21/2020,Series I,India,1600\r\nPaytm,New Delhi,Finance,500,NULL,4/21/2020,Unknown,India,2200\r\nLending Club,SF Bay Area,Finance,460,0.3,4/21/2020,Post-IPO,United States,392\r\nHouzz,SF Bay Area,Consumer,155,0.1,4/21/2020,Series E,United States,613\r\nCasper,New York City,Retail,78,0.21,4/21/2020,Post-IPO,United States,339\r\nRealSelf,Seattle,Healthcare,40,0.13,4/21/2020,Series B,United States,42\r\nFreshbooks,Toronto,Finance,38,0.09,4/21/2020,Series B,Canada,75\r\nPatreon,SF Bay Area,Media,30,0.13,4/21/2020,Series D,United States,165\r\nLambda School,SF Bay Area,Education,19,NULL,4/21/2020,Series B,United States,48\r\nPolitico / Protocol,Washington D.C.,Media,13,NULL,4/21/2020,Unknown,United States,NULL\r\nBringg,Tel Aviv,Logistics,10,0.1,4/21/2020,Series D,Israel,84\r\nKlook,Hong Kong,Travel,300,0.15,4/20/2020,Series D,Hong Kong,521\r\nConsenSys,New York City,Crypto,91,0.14,4/20/2020,Unknown,United States,10\r\nGumGum,Los Angeles,Marketing,90,0.25,4/20/2020,Series D,United States,58\r\nKueski,Guadalajara,Finance,90,0.3,4/20/2020,Series B,Mexico,38\r\nMovidesk,Blumenau,Support,33,0.25,4/20/2020,Seed,Brazil,1\r\nZum,SF Bay Area,Transportation,28,NULL,4/20/2020,Series C,United States,71\r\nKomodo Health,SF Bay Area,Healthcare,23,0.09,4/20/2020,Series C,United States,50\r\nForward,SF Bay Area,Healthcare,10,0.03,4/20/2020,Series C,United States,100\r\nHipcamp,SF Bay Area,Travel,NULL,0.6,4/20/2020,Series B,United States,40.5\r\nMotif Investing,SF Bay Area,Finance,NULL,1,4/18/2020,Series E,United States,126\r\nBlackBuck,Bengaluru,Logistics,200,NULL,4/17/2020,Series D,India,293\r\nContaAzul,Joinville,Education,140,0.35,4/17/2020,Series D,Brazil,37\r\nGreenhouse Software,New York City,Recruiting,120,0.28,4/17/2020,Series D,United States,110\r\nQuintoAndar,Sao Paulo,Real Estate,88,0.08,4/17/2020,Series D,Brazil,335\r\nLoft,Sao Paulo,Real Estate,47,0.1,4/17/2020,Series C,Brazil,263\r\nZilingo,Singapore,Retail,44,0.05,4/17/2020,Series D,Singapore,307\r\nLabster,Copenhagen,Education,40,NULL,4/17/2020,Series B,Denmark,34\r\nSweetgreen,Los Angeles,Food,35,0.1,4/17/2020,Series I,United States,478\r\nPeople.ai,SF Bay Area,Marketing,30,0.18,4/17/2020,Series C,United States,100\r\nTor,Boston,Security,13,0.37,4/17/2020,Unknown,United States,1\r\nBitGo,SF Bay Area,Crypto,NULL,0.12,4/17/2020,Series B,United States,69\r\nDispatch,Boston,Other,NULL,0.38,4/17/2020,Series A,United States,18\r\nFood52,New York City,Food,NULL,NULL,4/17/2020,Acquired,United States,96\r\nInfluitive,Toronto,Marketing,NULL,NULL,4/17/2020,Unknown,Canada,59\r\nCarGurus,Boston,Transportation,130,0.13,4/16/2020,Post-IPO,United States,1\r\nFunding Societies,Singapore,Finance,65,0.18,4/16/2020,Series B,Singapore,42\r\nCleverTap,Mumbai,Marketing,60,0.2,4/16/2020,Series C,India,76\r\nCrowdRiff,Toronto,Marketing,NULL,NULL,4/16/2020,Series A,Canada,11\r\nFullStory,Atlanta,Marketing,NULL,NULL,4/16/2020,Series C,United States,59\r\nGrailed,New York City,Retail,NULL,NULL,4/16/2020,Series A,United States,16\r\nLumenAd,Missoula,Marketing,NULL,NULL,4/16/2020,Unknown,United States,NULL\r\nPurse,SF Bay Area,Crypto,NULL,1,4/16/2020,Seed,United States,1\r\nSquadVoice,Columbus,Real Estate,NULL,NULL,4/16/2020,Series A,United States,2\r\nOpendoor,SF Bay Area,Real Estate,600,0.35,4/15/2020,Series E,United States,1500\r\nGoPro,SF Bay Area,Consumer,200,0.2,4/15/2020,Post-IPO,United States,288\r\nShop101,Mumbai,Retail,200,0.4,4/15/2020,Series C,India,19\r\nZume,SF Bay Area,Food,200,0.67,4/15/2020,Unknown,United States,423\r\nCarta,SF Bay Area,Finance,161,0.16,4/15/2020,Series E,United States,447\r\nAkulaku,Jakarta,Finance,100,NULL,4/15/2020,Series D,Indonesia,160\r\nParsable,SF Bay Area,HR,40,0.25,4/15/2020,Series C,United States,72\r\nKodiak Robotics,SF Bay Area,Transportation,15,0.2,4/15/2020,Series A,United States,40\r\nTulip Retail,Toronto,Retail,14,NULL,4/15/2020,Series B,Canada,51\r\nTrove Recommerce,SF Bay Area,Retail,13,NULL,4/15/2020,Series C,United States,34\r\nDude Solutions,Raleigh,Other,NULL,0.2,4/15/2020,Acquired,United States,100\r\nSweetEscape,Jakarta,Consumer,NULL,0.3,4/15/2020,Series A,Indonesia,7\r\nView,SF Bay Area,Energy,NULL,NULL,4/15/2020,Series H,United States,1800\r\nThe RealReal,SF Bay Area,Retail,235,0.1,4/14/2020,Post-IPO,United States,358\r\nTouchBistro,Toronto,Food,131,0.23,4/14/2020,Series E,Canada,224\r\nEnvoy,SF Bay Area,HR,58,0.3,4/14/2020,Series B,United States,59\r\nVSCO,SF Bay Area,Consumer,45,0.35,4/14/2020,Series B,United States,90\r\nSkillz,SF Bay Area,Consumer,21,NULL,4/14/2020,Series D,United States,132\r\nDataStax,SF Bay Area,Data,15,NULL,4/14/2020,Series E,United States,190\r\nXerpa,Sao Paulo,HR,10,NULL,4/14/2020,Unknown,Brazil,5\r\nAura Financial,SF Bay Area,Finance,NULL,NULL,4/14/2020,Unknown,United States,584\r\nRedDoorz,Singapore,Travel,NULL,NULL,4/14/2020,Series C,Singapore,134\r\nGroupon,Chicago,Retail,2800,0.44,4/13/2020,Post-IPO,United States,1400\r\nZoox,SF Bay Area,Transportation,100,0.1,4/13/2020,Series B,United States,955\r\nNeon,Sao Paulo,Finance,70,0.1,4/13/2020,Series B,Brazil,120\r\nEasyPost,SF Bay Area,Logistics,50,0.25,4/13/2020,Series A,United States,12\r\nClearbanc,Toronto,Finance,17,0.08,4/13/2020,Series B,Canada,119\r\nMeow Wolf,Santa Fe,Media,201,NULL,4/10/2020,Unknown,United States,185\r\nFrontdesk,Milwaukee,Travel,35,0.16,4/10/2020,Unknown,United States,3\r\nBeeTech,Sao Paulo,Finance,30,NULL,4/10/2020,Series B,Brazil,14\r\nBuilt In,Chicago,Recruiting,28,NULL,4/10/2020,Series C,United States,29\r\nNuoDB,Boston,Data,20,0.29,4/10/2020,Unknown,United States,85\r\nRhumbix,SF Bay Area,Construction,16,0.27,4/10/2020,Series B,United States,35\r\nAtsu,Seattle,Infrastructure,6,1,4/10/2020,Unknown,United States,1\r\nGeekwire,Seattle,Media,5,0.31,4/10/2020,Unknown,United States,NULL\r\nFloQast,Los Angeles,Finance,NULL,NULL,4/10/2020,Series C,United States,119\r\nYelp,SF Bay Area,Consumer,1000,0.17,4/9/2020,Post-IPO,United States,56\r\nMonzo,Las Vegas,Finance,165,NULL,4/9/2020,Series F,United States,324\r\nOneTrust,Atlanta,Legal,150,0.1,4/9/2020,Series B,United States,410\r\nOmie,Sao Paulo,Finance,136,0.31,4/9/2020,Series B,Brazil,26\r\nDomo,Salt Lake City,Data,90,0.1,4/9/2020,Post-IPO,United States,689\r\nMatterport,SF Bay Area,Data,90,0.34,4/9/2020,Series D,United States,114\r\nClinc,Ann Arbor,Support,40,0.32,4/9/2020,Series B,United States,59\r\nMejuri,Toronto,Retail,36,0.15,4/9/2020,Series B,Canada,28\r\nCode42,Minneapolis,Data,25,0.05,4/9/2020,Series B,United States,137.5\r\nLighthouse Labs,Toronto,Education,14,0.07,4/9/2020,Unknown,Canada,NULL\r\nLoopMe,London,Marketing,8,0.04,4/9/2020,Unknown,United Kingdom,32\r\nCipherTrace,SF Bay Area,Crypto,NULL,NULL,4/9/2020,Unknown,United States,18\r\nElliptic,London,Crypto,NULL,0.3,4/9/2020,Series B,United Kingdom,40\r\nZest AI,Los Angeles,Finance,NULL,NULL,4/9/2020,Unknown,United States,217\r\nEventbrite,SF Bay Area,Consumer,500,0.45,4/8/2020,Post-IPO,United States,332\r\nMeesho,Bengaluru,Retail,200,0.28,4/8/2020,Series D,India,215\r\nScoop,SF Bay Area,Transportation,92,0.33,4/8/2020,Series C,United States,95\r\nUnison,SF Bay Area,Finance,89,0.45,4/8/2020,Series B,United States,40\r\nLever,SF Bay Area,Recruiting,86,0.4,4/8/2020,Series C,United States,72\r\nUnbabel,Lisbon,Support,80,0.35,4/8/2020,Series C,Portugal,91\r\nButton,New York City,Marketing,48,0.35,4/8/2020,Series C,United States,64\r\nEden / Managed By Q,New York City,Real Estate,40,0.4,4/8/2020,Series B,United States,40\r\nQuantcast,SF Bay Area,Marketing,30,0.05,4/8/2020,Series C,United States,65\r\nBVAccel,San Diego,Marketing,25,0.25,4/8/2020,Unknown,United States,NULL\r\nVideoAmp,Los Angeles,Marketing,21,0.1,4/8/2020,Series C,United States,106\r\nKenoby,Sao Paulo,Recruiting,18,0.16,4/8/2020,Unknown,Brazil,23\r\nConnected,Toronto,Product,17,0.1,4/8/2020,Unknown,Canada,NULL\r\nGetNinjas,Sao Paulo,Consumer,11,0.1,4/8/2020,Series B,Brazil,16\r\nSpyce,Boston,Food,4,0.12,4/8/2020,Series A,United States,26\r\nCreditas,Sao Paulo,Finance,NULL,0.06,4/8/2020,Series D,Brazil,314\r\nLytics,Portland,Marketing,NULL,NULL,4/8/2020,Series C,United States,58\r\nRedDoorz,Singapore,Travel,NULL,0.1,4/8/2020,Series C,Singapore,134\r\nSlice Labs,New York City,Finance,NULL,NULL,4/8/2020,Series A,United States,35\r\nTechAdvance,Lagos,Finance,NULL,NULL,4/8/2020,Unknown,Nigeria,1\r\nZola,New York City,Retail,NULL,0.2,4/8/2020,Series D,United States,140\r\nToast,Boston,Food,1300,0.5,4/7/2020,Series F,United States,902\r\nezCater,Boston,Food,400,0.44,4/7/2020,Series D,United States,319\r\nSage Therapeutics,Boston,Healthcare,340,0.53,4/7/2020,Post-IPO,United States,438\r\nRedfin,Seattle,Real Estate,236,0.07,4/7/2020,Post-IPO,United States,319\r\nBranch Metrics,SF Bay Area,Marketing,100,0.2,4/7/2020,Series E,United States,367\r\nNewfront Insurance,SF Bay Area,Finance,94,NULL,4/7/2020,Unknown,United States,NULL\r\nIbotta,Denver,Retail,87,0.15,4/7/2020,Series D,United States,85\r\nVirta Health,SF Bay Area,Healthcare,65,NULL,4/7/2020,Series C,United States,175\r\nAway,New York City,Retail,60,0.1,4/7/2020,Series D,United States,181\r\nMediaMath,New York City,Marketing,53,0.08,4/7/2020,Private Equity,United States,607\r\nGroup Nine Media,New York City,Media,50,0.07,4/7/2020,Unknown,United States,190\r\nPayfactors,Boston,HR,46,NULL,4/7/2020,Unknown,United States,NULL\r\nNav,Salt Lake City,Finance,30,NULL,4/7/2020,Series C,United States,99\r\nAskNicely,Portland,Support,NULL,NULL,4/7/2020,Series A,United States,15\r\nRainFocus,Salt Lake City,Marketing,NULL,NULL,4/7/2020,Private Equity,United States,41\r\nMetromile,SF Bay Area,Finance,100,0.33,4/6/2020,Series E,United States,293\r\nRock Content,Belo Horizonte,Marketing,100,0.2,4/6/2020,Series A,Brazil,0.7\r\nBounceX,New York City,Marketing,77,0.2,4/6/2020,Series B,United States,75\r\nC6 Bank,Sao Paulo,Finance,60,0.06,4/6/2020,Unknown,Brazil,NULL\r\nWordstream,Boston,Marketing,26,0.1,4/6/2020,Acquired,United States,28\r\nCogito,Boston,Support,24,0.14,4/6/2020,Series C,United States,92\r\nBusBud,Montreal,Transportation,23,0.32,4/6/2020,Series B,Canada,21\r\nBorrowell,Toronto,Finance,15,0.2,4/6/2020,Series B,Canada,72\r\nPerkSpot,Chicago,HR,10,0.1,4/6/2020,Private Equity,United States,50\r\nBitfarms,Quebec,Crypto,NULL,NULL,4/6/2020,Post-IPO,Canada,25\r\nHopper,Boston,Travel,NULL,NULL,4/6/2020,Series D,United States,183\r\nMapbox,Washington D.C.,Data,NULL,NULL,4/6/2020,Series C,United States,227\r\nAstra,SF Bay Area,Aerospace,40,0.25,4/5/2020,Unknown,United States,100\r\nIflix,Kuala Lumpur,Consumer,50,0.12,4/4/2020,Unknown,Malaysia,348\r\nGympass,Sao Paulo,Fitness,467,0.33,4/3/2020,Series D,Brazil,300\r\nSojern,SF Bay Area,Marketing,300,0.5,4/3/2020,Series D,United States,162\r\nMaxMilhas,Belo Horizonte,Travel,167,0.42,4/3/2020,Unknown,Brazil,NULL\r\nMinted,SF Bay Area,Retail,147,0.37,4/3/2020,Series E,United States,297\r\nVelodyne Lidar,SF Bay Area,Transportation,140,NULL,4/3/2020,Unknown,United States,225\r\nZoox,SF Bay Area,Transportation,120,NULL,4/3/2020,Series B,United States,955\r\nTraveloka,Jakarta,Travel,100,0.1,4/3/2020,Unknown,Indonesia,NULL\r\nArrive Logistics,Austin,Logistics,75,0.07,4/3/2020,Series B,United States,35\r\nSalsify,Boston,Retail,60,0.13,4/3/2020,Series D,United States,97\r\nJetty,New York City,Finance,35,0.4,4/3/2020,Series B,United States,40\r\nD2iQ,SF Bay Area,Infrastructure,34,0.13,4/3/2020,Series D,United States,247\r\nBustle Digital Group,New York City,Media,24,0.08,4/3/2020,Series E,United States,80\r\nBustle Digital Group,New York City,Media,19,NULL,4/3/2020,Series E,United States,80\r\nOpencare,Toronto,Healthcare,18,0.25,4/3/2020,Series A,Canada,24\r\nAnagram,Los Angeles,Healthcare,17,NULL,4/3/2020,Series A,United States,14\r\nG/O Media Group,New York City,Media,14,0.05,4/3/2020,Unknown,United States,NULL\r\nDSCO,Salt Lake City,Retail,12,NULL,4/3/2020,Series A,United States,4\r\nTripbam,Dallas,Travel,10,0.25,4/3/2020,Unknown,United States,NULL\r\nAvantage Entertainment,Minneapolis,Media,5,0.2,4/3/2020,Unknown,United States,NULL\r\nAlegion,Austin,Data,NULL,NULL,4/3/2020,Series A,United States,16\r\nAllyO,SF Bay Area,HR,NULL,NULL,4/3/2020,Series B,United States,64\r\nMews,Prague,Travel,NULL,NULL,4/3/2020,Series B,Czech Republic,41\r\nPeopleGrove,SF Bay Area,HR,NULL,NULL,4/3/2020,Series A,United States,7\r\nThe Muse,New York City,Recruiting,NULL,NULL,4/3/2020,Series B,United States,28\r\nThe Wing,New York City,Real Estate,NULL,0.5,4/3/2020,Series C,United States,117\r\nMindBody,San Luis Obispo,Fitness,700,0.35,4/2/2020,Post-IPO,United States,114\r\nKaterra,SF Bay Area,Construction,240,0.03,4/2/2020,Series D,United States,1200\r\nRitual,Toronto,Food,196,0.54,4/2/2020,Series C,Canada,112\r\nClassPass,New York City,Fitness,154,0.22,4/2/2020,Series E,United States,549\r\neGym,Munich,Fitness,100,0.25,4/2/2020,Series D,Germany,109\r\nVoi,Stockholm,Transportation,100,NULL,4/2/2020,Series B,Sweden,167\r\nIndustrious,New York City,Real Estate,90,0.2,4/2/2020,Series D,United States,222\r\n1stdibs,New York City,Retail,70,0.17,4/2/2020,Series D,United States,253\r\nThirdLove,SF Bay Area,Retail,65,0.3,4/2/2020,Series B,United States,68\r\nLatch,New York City,Security,60,NULL,4/2/2020,Series B,United States,152\r\nThe Predictive Index,Boston,HR,59,0.25,4/2/2020,Acquired,United States,65\r\nShuttl,New Delhi,Transportation,40,NULL,4/2/2020,Series C,India,122\r\nJobcase,Boston,Recruiting,39,0.2,4/2/2020,Private Equity,United States,118\r\nCopper,SF Bay Area,Marketing,35,NULL,4/2/2020,Series C,United States,102\r\nDynamic Signal,SF Bay Area,HR,35,0.19,4/2/2020,Series E,United States,114\r\nFiscalNote,Washington D.C.,Media,30,NULL,4/2/2020,Series D,United States,50\r\nSauce Labs,SF Bay Area,Infrastructure,30,0.15,4/2/2020,Unknown,United States,151\r\nHumu,SF Bay Area,HR,26,NULL,4/2/2020,Series B,United States,40\r\nTripleLift,New York City,Marketing,23,0.07,4/2/2020,Series B,United States,16\r\nCoding Dojo,Seattle,Education,7,0.07,4/2/2020,Unknown,United States,2\r\nInstamojo,Bengaluru,Finance,6,0.06,4/2/2020,Unknown,India,8\r\nSynergysuite,Salt Lake City,Food,5,0.07,4/2/2020,Series A,United States,6\r\nAtlanta Tech Village,Atlanta,Real Estate,NULL,0.5,4/2/2020,Unknown,United States,NULL\r\nCapillary,Bengaluru,Retail,NULL,NULL,4/2/2020,Private Equity,India,102\r\nModsy,SF Bay Area,Retail,NULL,NULL,4/2/2020,Series C,United States,70\r\nThe Modist,Dubai,Retail,NULL,1,4/2/2020,Unknown,United Arab Emirates,NULL\r\nWonder,New York City,Other,NULL,NULL,4/2/2020,Unknown,United States,NULL\r\nBooksy,SF Bay Area,Consumer,200,NULL,4/1/2020,Series B,United States,48\r\nFlymya,Yangon,Travel,200,0.33,4/1/2020,Unknown,Myanmar,NULL\r\nPatientPop,Los Angeles,Healthcare,100,0.2,4/1/2020,Series B,United States,75\r\nShowpad,Chicago,Marketing,52,0.12,4/1/2020,Series D,United States,159\r\nHighsnobiety,Berlin,Media,51,0.25,4/1/2020,Series A,Germany,8.5\r\nEarnin,SF Bay Area,Finance,50,0.2,4/1/2020,Series C,United States,190\r\nWonolo,SF Bay Area,Recruiting,46,0.13,4/1/2020,Series C,United States,52\r\nAcko,Mumbai,Finance,45,0.09,4/1/2020,Unknown,India,143\r\nMoovel,Portland,Transportation,28,0.37,4/1/2020,Unknown,United States,NULL\r\nAqua Security,Tel Aviv,Security,24,0.09,4/1/2020,Series C,Israel,100\r\nCrayon,Boston,Marketing,20,0.2,4/1/2020,Series A,United States,16\r\nPana,Denver,Travel,18,NULL,4/1/2020,Series A,United States,11\r\nSensibill,Toronto,Finance,17,0.2,4/1/2020,Series B,Canada,50\r\nUsermind,Seattle,Marketing,15,0.25,4/1/2020,Series C,United States,46\r\nIncredible Health,SF Bay Area,Healthcare,9,0.4,4/1/2020,Series A,United States,15\r\nCurrency,Los Angeles,Finance,NULL,NULL,4/1/2020,Unknown,United States,NULL\r\nGOAT Group,Los Angeles,Retail,NULL,NULL,4/1/2020,Series D,United States,197\r\nLe Tote,SF Bay Area,Retail,NULL,0.5,4/1/2020,Series C,United States,62\r\nLevelset,New Orleans,Construction,NULL,NULL,4/1/2020,Series C,United States,46\r\nPebblepost,New York City,Marketing,NULL,0.6,4/1/2020,Series C,United States,81\r\nWhyHotel,Washington D.C.,Travel,NULL,0.5,4/1/2020,Series B,United States,33\r\nKeepTruckin,SF Bay Area,Logistics,349,0.18,3/31/2020,Series D,United States,227\r\nAdRoll,Salt Lake City,Marketing,210,0.3,3/31/2020,Series C,United States,89\r\nRover,Seattle,Consumer,194,0.41,3/31/2020,Series G,United States,310\r\nTuro,SF Bay Area,Transportation,108,0.3,3/31/2020,Series E,United States,467\r\nuShip,Austin,Logistics,65,0.37,3/31/2020,Series D,United States,69\r\nSkySlope,Sacramento,Real Estate,50,0.25,3/31/2020,Acquired,United States,NULL\r\nSiteimprove,Minneapolis,Marketing,40,NULL,3/31/2020,Unknown,United States,55\r\nAngelList,SF Bay Area,Recruiting,20,NULL,3/31/2020,Series B,United States,26\r\nZenoti,Seattle,Fitness,17,0.04,3/31/2020,Series C,United States,91\r\nDialSource,Sacramento,Marketing,5,0.14,3/31/2020,Series B,United States,26\r\nAdara,SF Bay Area,Travel,NULL,NULL,3/31/2020,Series C,United States,67\r\nClaravine,Salt Lake City,Marketing,NULL,NULL,3/31/2020,Seed,United States,4\r\nDomio,New York City,Travel,NULL,0.3,3/31/2020,Series B,United States,116\r\nKazoo,Austin,HR,NULL,0.35,3/31/2020,Series A,United States,8\r\nSnap Finance,Salt Lake City,Finance,NULL,NULL,3/31/2020,Unknown,United States,NULL\r\nZerto,Boston,Infrastructure,NULL,NULL,3/31/2020,Series E,United States,130\r\nThumbtack,SF Bay Area,Consumer,250,0.3,3/30/2020,Series F,United States,423\r\nRigUp,Austin,Energy,120,0.25,3/30/2020,Series D,United States,423\r\nFabHotels,New Delhi,Travel,80,0.2,3/30/2020,Series B,India,48\r\nHibob,Tel Aviv,HR,70,0.3,3/30/2020,Series A,Israel,45\r\nPeerStreet,Los Angeles,Finance,51,0.3,3/30/2020,Series C,United States,110\r\nMaven,Seattle,Media,31,0.09,3/30/2020,Post-IPO,United States,77\r\nBlume Global,SF Bay Area,Logistics,30,0.1,3/30/2020,Unknown,United States,NULL\r\nCatalant,Boston,Other,30,NULL,3/30/2020,Series E,United States,110\r\nStarship Technologies,Tallinn,Transportation,30,NULL,3/30/2020,Series A,Estonia,82\r\nLoftsmart,New York City,Real Estate,25,0.75,3/30/2020,Series A,United States,18\r\nCaliva,SF Bay Area,Retail,20,NULL,3/30/2020,Series A,United States,75\r\nIris Nova,New York City,Food,9,0.5,3/30/2020,Seed,United States,15\r\nCuyana,SF Bay Area,Retail,NULL,NULL,3/30/2020,Series C,United States,31\r\nZipRecruiter,Los Angeles,Recruiting,400,0.39,3/29/2020,Series B,United States,219\r\nAmplero,Seattle,Marketing,17,1,3/29/2020,Series B,United States,25\r\nPolarr,SF Bay Area,Consumer,10,NULL,3/29/2020,Series A,United States,13\r\nTravelTriangle,Gurugram,Travel,250,0.5,3/28/2020,Series D,India,47\r\nWeWork,New York City,Real Estate,250,NULL,3/28/2020,Series H,United States,2250\r\nRent the Runway,New York City,Retail,NULL,NULL,3/28/2020,Series F,United States,541\r\nOneWeb,London,Aerospace,451,0.85,3/27/2020,Unknown,United Kingdom,3000\r\nBird,Los Angeles,Transportation,406,0.3,3/27/2020,Series D,United States,623\r\nHOOQ,Singapore,Consumer,250,1,3/27/2020,Unknown,Singapore,95\r\nEverlane,SF Bay Area,Retail,227,NULL,3/27/2020,Unknown,United States,18\r\nDataRobot,Boston,Data,200,NULL,3/27/2020,Series E,United States,430\r\nRestaurant365,Los Angeles,Food,175,NULL,3/27/2020,Series C,United States,127\r\nBlueground,New York City,Real Estate,130,0.25,3/27/2020,Series B,United States,77\r\nKnotel,New York City,Real Estate,127,0.3,3/27/2020,Series C,United States,560\r\nGetaround,SF Bay Area,Transportation,100,0.25,3/27/2020,Series D,United States,403\r\nZipcar,Boston,Transportation,100,0.2,3/27/2020,Acquired,United States,107\r\nMogo,Vancouver,Finance,78,0.3,3/27/2020,Post-IPO,Canada,201\r\nDISCO,Austin,Legal,75,NULL,3/27/2020,Series E,United States,133\r\nRaken,San Diego,Construction,60,NULL,3/27/2020,Series A,United States,12\r\nBench,Vancouver,Finance,47,0.1,3/27/2020,Series B,Canada,49\r\nLoft,Sao Paulo,Real Estate,47,NULL,3/27/2020,Series C,Brazil,263\r\nOh My Green,SF Bay Area,Food,40,NULL,3/27/2020,Seed,United States,20\r\nBevi,Boston,Food,30,0.2,3/27/2020,Series C,United States,60\r\nTextio,Seattle,Recruiting,30,0.2,3/27/2020,Unknown,United States,41\r\nOpal,Portland,Marketing,20,NULL,3/27/2020,Series B,United States,25\r\nThirdLove,New York City,Retail,10,NULL,3/27/2020,Series B,United States,68\r\nBcredi,Curitiba,Finance,NULL,NULL,3/27/2020,Series A,Brazil,NULL\r\nMake School,SF Bay Area,Education,NULL,NULL,3/27/2020,Unknown,United States,NULL\r\nPivot3,Austin,Infrastructure,NULL,NULL,3/27/2020,Series H,United States,273\r\nB8ta,SF Bay Area,Retail,250,0.5,3/26/2020,Series C,United States,88\r\nFareportal,Gurugram,Travel,200,NULL,3/26/2020,Unknown,India,NULL\r\nPuppet,Portland,Infrastructure,50,0.1,3/26/2020,Series F,United States,149\r\nEcobee,Toronto,Energy,47,0.1,3/26/2020,Series C,Canada,149\r\nPassport,Charlotte,Transportation,44,NULL,3/26/2020,Series D,United States,123\r\nPeerspace,SF Bay Area,Real Estate,41,0.75,3/26/2020,Series B,United States,34\r\nGoSpotCheck,Denver,Retail,23,0.2,3/26/2020,Series B,United States,47\r\nConsider.co,SF Bay Area,Other,13,1,3/26/2020,Seed,United States,5\r\nNativo,Los Angeles,Marketing,NULL,0.4,3/26/2020,Series B,United States,35\r\nTripActions,SF Bay Area,Travel,300,0.25,3/25/2020,Series D,United States,981\r\nLyric,SF Bay Area,Real Estate,100,NULL,3/25/2020,Series B,United States,179.1\r\nRangle,Toronto,Product,78,0.3,3/25/2020,Unknown,Canada,NULL\r\nO'Reilly Media,SF Bay Area,Media,75,0.15,3/25/2020,Unknown,United States,NULL\r\nWanderJaunt,SF Bay Area,Travel,56,0.23,3/25/2020,Series B,United States,26\r\nOutboundEngine,Austin,Marketing,52,0.28,3/25/2020,Series C,United States,48\r\nWonderschool,SF Bay Area,Education,50,0.75,3/25/2020,Series A,United States,24\r\nOvertime,New York City,Media,30,0.23,3/25/2020,Series B,United States,35\r\nJama,Portland,Product,12,0.05,3/25/2020,Unknown,United States,233\r\nElement Analytics,SF Bay Area,Data,10,NULL,3/25/2020,Series A,United States,22\r\nClever Real Estate,St. Louis,Real Estate,NULL,NULL,3/25/2020,Series A,United States,5\r\nClutter,Los Angeles,Consumer,NULL,0.3,3/25/2020,Series D,United States,296\r\nDivvy Homes,SF Bay Area,Real Estate,NULL,0.08,3/25/2020,Series B,United States,180\r\nUniversal Standard,New York City,Retail,NULL,NULL,3/25/2020,Series A,United States,8\r\nSonder,SF Bay Area,Travel,400,0.33,3/24/2020,Series D,United States,359\r\nTakl,Nashville,Consumer,130,NULL,3/24/2020,Unknown,United States,NULL\r\nFoodsby,Minneapolis,Food,87,0.67,3/24/2020,Series B,United States,20\r\nZeus Living,SF Bay Area,Real Estate,80,0.3,3/24/2020,Series B,United States,79\r\nTravelBank,SF Bay Area,Travel,20,NULL,3/24/2020,Series B,United States,35\r\nFlowr,Toronto,Retail,NULL,0.25,3/24/2020,Unknown,Canada,NULL\r\nPeerfit,Tampa Bay,HR,NULL,0.4,3/24/2020,Unknown,United States,47\r\nSpotHero,Chicago,Transportation,NULL,NULL,3/24/2020,Series D,United States,117\r\nCompass,New York City,Real Estate,375,0.15,3/23/2020,Series G,United States,1600\r\nConvene,New York City,Real Estate,150,0.18,3/23/2020,Series D,United States,280\r\nLeafly,Seattle,Retail,91,0.5,3/23/2020,Acquired,United States,2\r\nThe Guild,Austin,Travel,38,0.22,3/23/2020,Series B,United States,36\r\nDrip,Salt Lake City,Marketing,20,NULL,3/23/2020,Acquired,United States,0\r\nGrayMeta,Los Angeles,Data,20,0.4,3/23/2020,Unknown,United States,7\r\nTriplebyte,SF Bay Area,Recruiting,15,0.17,3/23/2020,Series B,United States,48\r\nLadder Life,SF Bay Area,Finance,13,0.25,3/23/2020,Series C,United States,94\r\nCabin,SF Bay Area,Travel,NULL,0.2,3/23/2020,Seed,United States,3\r\nEight Sleep,New York City,Retail,NULL,0.2,3/23/2020,Series C,United States,70\r\nZwift,Los Angeles,Fitness,NULL,NULL,3/23/2020,Series B,United States,164\r\nFlywheel Sports,New York City,Fitness,784,0.98,3/20/2020,Acquired,United States,120\r\nPeek,Salt Lake City,Travel,45,NULL,3/20/2020,Series B,United States,39\r\nCTO.ai,Vancouver,Infrastructure,30,0.5,3/20/2020,Seed,Canada,7\r\nYonder,Austin,Media,18,NULL,3/20/2020,Series A,United States,16\r\nService,Los Angeles,Travel,NULL,1,3/20/2020,Seed,United States,5\r\nVacasa,Portland,Travel,NULL,NULL,3/20/2020,Series C,United States,526\r\nBounce,Bengaluru,Transportation,120,NULL,3/19/2020,Series D,India,214\r\nEjento,SF Bay Area,Recruiting,84,1,3/19/2020,Unknown,United States,NULL\r\nRemote Year,Chicago,Travel,50,0.5,3/19/2020,Series B,United States,17\r\nLola,Boston,Travel,34,NULL,3/19/2020,Series C,United States,81\r\nAnyvision,Tel Aviv,Security,NULL,NULL,3/19/2020,Series A,Israel,74\r\nPopin,New York City,Fitness,NULL,1,3/19/2020,Unknown,United States,13\r\nTuft & Needle,Phoenix,Retail,NULL,NULL,3/19/2020,Acquired,United States,0\r\nFlytedesk,Boulder,Marketing,4,0.2,3/18/2020,Seed,United States,4\r\nInspirato,Denver,Travel,130,0.22,3/16/2020,Series C,United States,79\r\nHelp.com,Austin,Support,16,1,3/16/2020,Seed,United States,6\r\nService,Los Angeles,Travel,NULL,1,3/16/2020,Seed,United States,5.1\r\nHopSkipDrive,Los Angeles,Transportation,8,0.1,3/13/2020,Unknown,United States,45\r\nPanda Squad,SF Bay Area,Consumer,6,0.75,3/13/2020,Seed,United States,1\r\nTamara Mellon,Los Angeles,Retail,20,0.4,3/12/2020,Series C,United States,90\r\nEasyPost,Salt Lake City,Logistics,75,NULL,3/11/2020,Series A,United States,12\r\nBlackbaud,Charleston,Other,500,0.14,NULL,Post-IPO,United States,NULL\r\nYahoo,SF Bay Area,Consumer,1600,0.2,2/9/2023,Acquired,United States,6\r\nHibob,Tel Aviv,HR,70,0.3,3/30/2020,Series A,Israel,45\r\nCasper,New York City,Retail,NULL,NULL,9/14/2021,Post-IPO,United States,339\r\nWildlife Studios,Sao Paulo,Consumer,300,0.2,11/28/2022,Unknown,Brazil,260\r\n" } ]