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gitextract_arvppbev/
├── Advanced - CTEs.sql
├── Advanced - Stored Procedures.sql
├── Advanced - Temp Tables.sql
├── Advanced - Triggers and Events.sql
├── Beginner - Group By + Order By.sql
├── Beginner - Having vs Where.sql
├── Beginner - Limit and Aliasing.sql
├── Beginner - Parks_and_Rec_Create_db.sql
├── Beginner - Select Statement.sql
├── Beginner - Where Statement.sql
├── Intermediate - Case Statements.sql
├── Intermediate - Joins.sql
├── Intermediate - String Functions.sql
├── Intermediate - Subqueries.sql
├── Intermediate - Unions.sql
├── Intermediate - Window Functions.sql
├── Portfolio Project - Data Cleaning.sql
├── Portfolio Project - EDA.sql
├── README.md
└── layoffs.csv
================================================
FILE CONTENTS
================================================
================================================
FILE: Advanced - CTEs.sql
================================================
-- Using Common Table Expressions (CTE)
-- A CTE allows you to define a subquery block that can be referenced within the main query.
-- It is particularly useful for recursive queries or queries that require referencing a higher level
-- this is something we will look at in the next lesson/
-- Let's take a look at the basics of writing a CTE:
-- First, CTEs start using a "With" Keyword. Now we get to name this CTE anything we want
-- Then we say as and within the parenthesis we build our subquery/table we want
WITH CTE_Example AS
(
SELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary), AVG(salary)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
GROUP BY gender
)
-- directly after using it we can query the CTE
SELECT *
FROM CTE_Example;
-- Now if I come down here, it won't work because it's not using the same syntax
SELECT *
FROM CTE_Example;
-- Now we can use the columns within this CTE to do calculations on this data that
-- we couldn't have done without it.
WITH CTE_Example AS
(
SELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
GROUP BY gender
)
-- notice here I have to use back ticks to specify the table names - without them it doesn't work
SELECT gender, ROUND(AVG(`SUM(salary)`/`COUNT(salary)`),2)
FROM CTE_Example
GROUP BY gender;
-- we also have the ability to create multiple CTEs with just one With Expression
WITH CTE_Example AS
(
SELECT employee_id, gender, birth_date
FROM employee_demographics dem
WHERE birth_date > '1985-01-01'
), -- just have to separate by using a comma
CTE_Example2 AS
(
SELECT employee_id, salary
FROM parks_and_recreation.employee_salary
WHERE salary >= 50000
)
-- Now if we change this a bit, we can join these two CTEs together
SELECT *
FROM CTE_Example cte1
LEFT JOIN CTE_Example2 cte2
ON cte1. employee_id = cte2. employee_id;
-- the last thing I wanted to show you is that we can actually make our life easier by renaming the columns in the CTE
-- let's take our very first CTE we made. We had to use tick marks because of the column names
-- we can rename them like this
WITH CTE_Example (gender, sum_salary, min_salary, max_salary, count_salary) AS
(
SELECT gender, SUM(salary), MIN(salary), MAX(salary), COUNT(salary)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
GROUP BY gender
)
-- notice here I have to use back ticks to specify the table names - without them it doesn't work
SELECT gender, ROUND(AVG(sum_salary/count_salary),2)
FROM CTE_Example
GROUP BY gender;
================================================
FILE: Advanced - Stored Procedures.sql
================================================
-- So let's look at how we can create a stored procedure
-- First let's just write a super simple query
SELECT *
FROM employee_salary
WHERE salary >= 60000;
-- Now let's put this into a stored procedure.
CREATE PROCEDURE large_salaries()
SELECT *
FROM employee_salary
WHERE salary >= 60000;
-- Now if we run this it will work and create the stored procedure
-- we can click refresh and see that it is there
-- notice it did not give us an output, that's because we
-- If we want to call it and use it we can call it by saying:
CALL large_salaries();
-- as you can see it ran the query inside the stored procedure we created
-- Now how we have written is not actually best practice.alter
-- Usually when writing a stored procedure you don't have a simple query like that. It's usually more complex
-- if we tried to add another query to this stored procedure it wouldn't work. It's a separate query:
CREATE PROCEDURE large_salaries2()
SELECT *
FROM employee_salary
WHERE salary >= 60000;
SELECT *
FROM employee_salary
WHERE salary >= 50000;
-- Best practice is to use a delimiter and a Begin and End to really control what's in the stored procedure
-- let's see how we can do this.
-- the delimiter is what separates the queries by default, we can change this to something like two $$
-- in my career this is what I've seen a lot of people who work in SQL use so I've picked it up as well
-- When we change this delimiter it now reads in everything as one whole unit or query instead of stopping
-- after the first semi colon
DELIMITER $$
CREATE PROCEDURE large_salaries2()
BEGIN
SELECT *
FROM employee_salary
WHERE salary >= 60000;
SELECT *
FROM employee_salary
WHERE salary >= 50000;
END $$
-- now we change the delimiter back after we use it to make it default again
DELIMITER ;
-- let's refresh to see the SP
-- now we can run this stored procedure
CALL large_salaries2();
-- as you can see we have 2 outputs which are the 2 queries we had in our stored procedure
-- we can also create a stored procedure by right clicking on Stored Procedures and creating one:
-- it's going to drop the procedure if it already exists.
USE `parks_and_recreation`;
DROP procedure IF EXISTS `large_salaries3`;
-- it automatically adds the dilimiter for us
DELIMITER $$
CREATE PROCEDURE large_salaries3()
BEGIN
SELECT *
FROM employee_salary
WHERE salary >= 60000;
SELECT *
FROM employee_salary
WHERE salary >= 50000;
END $$
DELIMITER ;
-- and changes it back at the end
-- this can be a genuinely good option to help you write your Stored Procedures faster, although either way
-- works
-- if we click finish you can see it is created the same and if we run it
CALL large_order_totals3();
-- we get our results
-- -------------------------------------------------------------------------
-- we can also add parameters
USE `parks_and_recreation`;
DROP procedure IF EXISTS `large_salaries3`;
-- it automatically adds the dilimiter for us
DELIMITER $$
CREATE PROCEDURE large_salaries3(employee_id_param INT)
BEGIN
SELECT *
FROM employee_salary
WHERE salary >= 60000
AND employee_id_param = employee_id;
END $$
DELIMITER ;
CALL large_salaries3(1);
================================================
FILE: Advanced - Temp Tables.sql
================================================
-- Using Temporary Tables
-- Temporary tables are tables that are only visible to the session that created them.
-- They can be used to store intermediate results for complex queries or to manipulate data before inserting it into a permanent table.
-- There's 2 ways to create temp tables:
-- 1. This is the less commonly used way - which is to build it exactly like a real table and insert data into it
CREATE TEMPORARY TABLE temp_table
(first_name varchar(50),
last_name varchar(50),
favorite_movie varchar(100)
);
-- if we execute this it gets created and we can actualyl query it.
SELECT *
FROM temp_table;
-- notice that if we refresh out tables it isn't there. It isn't an actual table. It's just a table in memory.
-- now obviously it's balnk so we would need to insert data into it like this:
INSERT INTO temp_table
VALUES ('Alex','Freberg','Lord of the Rings: The Twin Towers');
-- now when we run it and execute it again we have our data
SELECT *
FROM temp_table;
-- the second way is much faster and my preferred method
-- 2. Build it by inserting data into it - easier and faster
CREATE TEMPORARY TABLE salary_over_50k
SELECT *
FROM employee_salary
WHERE salary > 50000;
-- if we run this query we get our output
SELECT *
FROM temp_table_2;
-- 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
-- it helps me kind of categorize and separate it out
-- In the next lesson we will look at the Temp Tables vs CTEs
================================================
FILE: Advanced - Triggers and Events.sql
================================================
-- Triggers
-- a Trigger is a block of code that executes automatically executes when an event takes place in a table.
-- 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"
-- to reflect that the client has indeed paid their invoice
SELECT * FROM employee_salary;
SELECT * FROM employee_demographics;
-- 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
-- with the amount that was paid
-- so let's write this out
USE parks_and_recreation;
DELIMITER $$
CREATE TRIGGER employee_insert2
-- we can also do BEFORE, but for this lesson we have to do after
AFTER INSERT ON employee_salary
-- 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
-- only trigger once, but MySQL doesn't have this functionality unfortunately
FOR EACH ROW
-- now we can write our block of code that we want to run when this is triggered
BEGIN
-- we want to update our client invoices table
-- and set the total paid = total_paid (if they had already made some payments) + NEW.amount_paid
-- 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
INSERT INTO employee_demographics (employee_id, first_name, last_name) VALUES (NEW.employee_id,NEW.first_name,NEW.last_name);
END $$
DELIMITER ;
-- Now let's run it and create it
-- Now that it's created let's test it out.
-- Let's insert a payment into the payments table and see if it updates in the Invoice table.
-- so let's put the values that we want to insert - let's pay off this invoice 3 in full
INSERT INTO employee_salary (employee_id, first_name, last_name, occupation, salary, dept_id)
VALUES(13, 'Jean-Ralphio', 'Saperstein', 'Entertainment 720 CEO', 1000000, NULL);
-- now it was updated in the payments table and the trigger was triggered and update the corresponding values in the invoice table
DELETE FROM employee_salary
WHERE employee_id = 13;
-- -------------------------------------------------------------------------
-- now let's look at Events
-- Now I usually call these "Jobs" because I called them that for years in MSSQL, but in MySQL they're called Events
-- Events are task or block of code that gets executed according to a schedule. These are fantastic for so many reasons. Importing data on a schedule.
-- Scheduling reports to be exported to files and so many other things
-- you can schedule all of this to happen every day, every monday, every first of the month at 10am. Really whenever you want
-- This really helps with automation in MySQL
-- let's say Parks and Rec has a policy that anyone over the age of 60 is immediately retired with lifetime pay
-- All we have to do is delete them from the demographics table
SELECT *
FROM parks_and_recreation.employee_demographics;
SHOW EVENTS;
-- we can drop or alter these events like this:
DROP EVENT IF EXISTS delete_retirees;
DELIMITER $$
CREATE EVENT delete_retirees
ON SCHEDULE EVERY 30 SECOND
DO BEGIN
DELETE
FROM parks_and_recreation.employee_demographics
WHERE age >= 60;
END $$
-- if we run it again you can see Jerry is now fired -- or I mean retired
SELECT *
FROM parks_and_recreation.employee_demographics;
================================================
FILE: Beginner - Group By + Order By.sql
================================================
-- Group By
-- 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.
-- GROUP BY is going to allow us to group rows that have the same data and run aggregate functions on them
SELECT *
FROM employee_demographics;
-- when you use group by you have to have the same columns you're grouping on in the group by statement
SELECT gender
FROM employee_demographics
GROUP BY gender
;
SELECT first_name
FROM employee_demographics
GROUP BY gender
;
SELECT occupation
FROM employee_salary
GROUP BY occupation
;
-- notice there is only one office manager row
-- when we group by 2 columns we now have a row for both occupation and salary because salary is different
SELECT occupation, salary
FROM employee_salary
GROUP BY occupation, salary
;
-- now the most useful reason we use group by is so we can perform out aggregate functions on them
SELECT gender, AVG(age)
FROM employee_demographics
GROUP BY gender
;
SELECT gender, MIN(age), MAX(age), COUNT(age),AVG(age)
FROM employee_demographics
GROUP BY gender
;
#10 - The ORDER BY clause:
-------------------------
#The ORDER BY keyword is used to sort the result-set in ascending or descending order.
#The ORDER BY keyword sorts the records in ascending order by default. To sort the records in descending order, use the DESC keyword.
#So let's try it out with our customer table
#First let's start simple with just ordering by one column
SELECT *
FROM customers
ORDER BY first_name;
#You can see that first name is ordered from a - z or Ascending.
#We can change that by specifying DESC after it
SELECT *
FROM employee_demographics;
-- if we use order by it goes a to z by default (ascending order)
SELECT *
FROM employee_demographics
ORDER BY first_name;
-- we can manually change the order by saying desc
SELECT *
FROM employee_demographics
ORDER BY first_name DESC;
#Now we can also do multiple columns like this:
SELECT *
FROM employee_demographics
ORDER BY gender, age;
SELECT *
FROM employee_demographics
ORDER BY gender DESC, age DESC;
#now we don't actually have to spell out the column names. We can actually just use their column position
#State is in position 8 and money is in 9, we can use those as well.
SELECT *
FROM employee_demographics
ORDER BY 5 DESC, 4 DESC;
#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.
#So that's all there is to order by - fairly straight forward, but something I use for most queries I use in SQL
================================================
FILE: Beginner - Having vs Where.sql
================================================
-- Having vs Where
-- Both were created to filter rows of data, but they filter 2 separate things
-- Where is going to filters rows based off columns of data
-- Having is going to filter rows based off aggregated columns when grouped
SELECT gender, AVG(age)
FROM employee_demographics
GROUP BY gender
;
-- let's try to filter on the avg age using where
SELECT gender, AVG(age)
FROM employee_demographics
WHERE AVG(age) > 40
GROUP BY gender
;
-- 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
-- this is why Having was created
SELECT gender, AVG(age)
FROM employee_demographics
GROUP BY gender
HAVING AVG(age) > 40
;
SELECT gender, AVG(age) as AVG_age
FROM employee_demographics
GROUP BY gender
HAVING AVG_age > 40
;
================================================
FILE: Beginner - Limit and Aliasing.sql
================================================
-- LIMIT and ALIASING
-- Limit is just going to specify how many rows you want in the output
SELECT *
FROM employee_demographics
LIMIT 3;
-- if we change something like the order or use a group by it would change the output
SELECT *
FROM employee_demographics
ORDER BY first_name
LIMIT 3;
-- now there is an additional paramater in limit which we can access using a comma that specifies the starting place
SELECT *
FROM employee_demographics
ORDER BY first_name;
SELECT *
FROM employee_demographics
ORDER BY first_name
LIMIT 3,2;
-- this now says start at position 3 and take 2 rows after that
-- this is not used a lot in my opinion
-- you could us it if you wanted to select the third oldest person by doing this:
SELECT *
FROM employee_demographics
ORDER BY age desc;
-- we can see it's Donna - let's try to select her
SELECT *
FROM employee_demographics
ORDER BY age desc
LIMIT 2,1;
-- ALIASING
-- aliasing is just a way to change the name of the column (for the most part)
-- it can also be used in joins, but we will look at that in the intermediate series
SELECT gender, AVG(age)
FROM employee_demographics
GROUP BY gender
;
-- we can use the keyword AS to specify we are using an Alias
SELECT gender, AVG(age) AS Avg_age
FROM employee_demographics
GROUP BY gender
;
-- although we don't actually need it, but it's more explicit which I usually like
SELECT gender, AVG(age) Avg_age
FROM employee_demographics
GROUP BY gender
;
================================================
FILE: Beginner - Parks_and_Rec_Create_db.sql
================================================
DROP DATABASE IF EXISTS `Parks_and_Recreation`;
CREATE DATABASE `Parks_and_Recreation`;
USE `Parks_and_Recreation`;
CREATE TABLE employee_demographics (
employee_id INT NOT NULL,
first_name VARCHAR(50),
last_name VARCHAR(50),
age INT,
gender VARCHAR(10),
birth_date DATE,
PRIMARY KEY (employee_id)
);
CREATE TABLE employee_salary (
employee_id INT NOT NULL,
first_name VARCHAR(50) NOT NULL,
last_name VARCHAR(50) NOT NULL,
occupation VARCHAR(50),
salary INT,
dept_id INT
);
INSERT INTO employee_demographics (employee_id, first_name, last_name, age, gender, birth_date)
VALUES
(1,'Leslie', 'Knope', 44, 'Female','1979-09-25'),
(3,'Tom', 'Haverford', 36, 'Male', '1987-03-04'),
(4, 'April', 'Ludgate', 29, 'Female', '1994-03-27'),
(5, 'Jerry', 'Gergich', 61, 'Male', '1962-08-28'),
(6, 'Donna', 'Meagle', 46, 'Female', '1977-07-30'),
(7, 'Ann', 'Perkins', 35, 'Female', '1988-12-01'),
(8, 'Chris', 'Traeger', 43, 'Male', '1980-11-11'),
(9, 'Ben', 'Wyatt', 38, 'Male', '1985-07-26'),
(10, 'Andy', 'Dwyer', 34, 'Male', '1989-03-25'),
(11, 'Mark', 'Brendanawicz', 40, 'Male', '1983-06-14'),
(12, 'Craig', 'Middlebrooks', 37, 'Male', '1986-07-27');
INSERT INTO employee_salary (employee_id, first_name, last_name, occupation, salary, dept_id)
VALUES
(1, 'Leslie', 'Knope', 'Deputy Director of Parks and Recreation', 75000,1),
(2, 'Ron', 'Swanson', 'Director of Parks and Recreation', 70000,1),
(3, 'Tom', 'Haverford', 'Entrepreneur', 50000,1),
(4, 'April', 'Ludgate', 'Assistant to the Director of Parks and Recreation', 25000,1),
(5, 'Jerry', 'Gergich', 'Office Manager', 50000,1),
(6, 'Donna', 'Meagle', 'Office Manager', 60000,1),
(7, 'Ann', 'Perkins', 'Nurse', 55000,4),
(8, 'Chris', 'Traeger', 'City Manager', 90000,3),
(9, 'Ben', 'Wyatt', 'State Auditor', 70000,6),
(10, 'Andy', 'Dwyer', 'Shoe Shiner and Musician', 20000, NULL),
(11, 'Mark', 'Brendanawicz', 'City Planner', 57000, 3),
(12, 'Craig', 'Middlebrooks', 'Parks Director', 65000,1);
CREATE TABLE parks_departments (
department_id INT NOT NULL AUTO_INCREMENT,
department_name varchar(50) NOT NULL,
PRIMARY KEY (department_id)
);
INSERT INTO parks_departments (department_name)
VALUES
('Parks and Recreation'),
('Animal Control'),
('Public Works'),
('Healthcare'),
('Library'),
('Finance');
================================================
FILE: Beginner - Select Statement.sql
================================================
-- SELECT STATEMENET
-- 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
-- we will discuss throughout this video
#We can also select a specefic number of column based on our requirement.
#Now remember we can just select everything by saying:
SELECT *
FROM parks_and_recreation.employee_demographics;
#Let's try selecting a specific column
SELECT first_name
FROM employee_demographics;
#As you can see from the output, we only have the one column here now and don't see the others
#Now let's add some more columns, we just need to separate the columns with columns
SELECT first_name, last_name
FROM employee_demographics;
#Now the order doesn't normall matter when selecting your columns.
#There are some use cases we will look at in later modules where the order of the column
#Names in the select statement will matter, but for this you can put them in any order
SELECT last_name, first_name, gender, age
FROM employee_demographics;
#You'll also often see SQL queries formatted like this.
SELECT last_name,
first_name,
gender,
age
FROM employee_demographics;
#The query still runs the exact same, but it is easier to read and pick out the columns
#being selected and what you're doing with them.
#For example let's take a look at using a calculation in the select statement
#You can see here we have the total_money_spent - we can perform calculations on this
SELECT first_name,
last_name,
total_money_spent,
total_money_spent + 100
FROM customers;
#See how it's pretty easy to read and to see which columns we are using.
#Math in SQL does follow PEMDAS which stands for Parenthesis, Exponent, Multiplication,
#Division, Addition, subtraction - it's the order of operation for math
#For example - What will the output be?:
SELECT first_name,
last_name,
salary,
salary + 100
FROM employee_salary;
#This is going to do 10* 100 which is 1000 and then adds the original 540
#Now what will the output be when we do this?
SELECT first_name,
last_name,
salary,
(salary + 100) * 10
FROM employee_salary;
# Pemdas
#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
#The output - and you won't have any duplicates
SELECT department_id
FROM employee_salary;
SELECT DISTINCT department_id
FROM employee_salary;
#Now a lot happens in the select statement. We have an entire module dedicated to just the
#select statement so this is kind of just an introduction to the select statement.
================================================
FILE: Beginner - Where Statement.sql
================================================
#WHERE Clause:
#-------------
#The WHERE clause is used to filter records (rows of data)
#It's going to extract only those records that fulfill a specified condition.
# So basically if we say "Where name is = 'Alex' - only rows were the name = 'Alex' will return
# So this is only effecting the rows, not the columns
#Let's take a look at how this looks
SELECT *
FROM employee_salary
WHERE salary > 50000;
SELECT *
FROM employee_salary
WHERE salary >= 50000;
SELECT *
FROM employee_demographics
WHERE gender = 'Female';
#We can also return rows that do have not "Scranton"
SELECT *
FROM employee_demographics
WHERE gender != 'Female';
#We can use WHERE clause with date value also
SELECT *
FROM employee_demographics
WHERE birth_date > '1985-01-01';
-- Here '1990-01-01' is the default data formate in MySQL.
-- There are other date formats as well that we will talk about in a later lesson.
# LIKE STATEMENT
-- two special characters a % and a _
-- % means anything
SELECT *
FROM employee_demographics
WHERE first_name LIKE 'a%';
-- _ means a specific value
SELECT *
FROM employee_demographics
WHERE first_name LIKE 'a__';
SELECT *
FROM employee_demographics
WHERE first_name LIKE 'a___%';
================================================
FILE: Intermediate - Case Statements.sql
================================================
-- Case Statements
-- 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
SELECT *
FROM employee_demographics;
SELECT first_name,
last_name,
CASE
WHEN age <= 30 THEN 'Young'
END
FROM employee_demographics;
--
SELECT first_name,
last_name,
CASE
WHEN age <= 30 THEN 'Young'
WHEN age BETWEEN 31 AND 50 THEN 'Old'
WHEN age >= 50 THEN "On Death's Door"
END
FROM employee_demographics;
-- Poor Jerry
-- Now we don't just have to do simple labels like we did, we can also perform calculations
-- Let's look at giving bonuses to employees
SELECT *
FROM employee_salary;
-- Pawnee Council sent out a memo of their bonus and pay increase structure so we need to follow it
-- Basically if they make less than 45k then they get a 5% raise - very generous
-- if they make more than 45k they get a 7% raise
-- they get a bonus of 10% if they work for the Finance Department
SELECT first_name, last_name, salary,
CASE
WHEN salary > 45000 THEN salary + (salary * 0.05)
WHEN salary < 45000 THEN salary + (salary * 0.07)
END AS new_salary
FROM employee_salary;
-- Unfortunately Pawnee Council was extremely specific in their wording and Jerry was not included in the pay increases. Maybe Next Year.
-- Now we need to also account for Bonuses, let's make a new column
SELECT first_name, last_name, salary,
CASE
WHEN salary > 45000 THEN salary + (salary * 0.05)
WHEN salary < 45000 THEN salary + (salary * 0.07)
END AS new_salary,
CASE
WHEN dept_id = 6 THEN salary * .10
END AS Bonus
FROM employee_salary;
-- as you can see Ben is the only one who get's a bonus
================================================
FILE: Intermediate - Joins.sql
================================================
-- Joins
-- joins allow you to combine 2 tables together (or more) if they have a common column.
-- 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
-- there are several joins we will look at today, inner joins, outer joins, and self joins
-- 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
SELECT *
FROM employee_demographics;
SELECT *
FROM employee_salary;
-- let's start with an inner join -- inner joins return rows that are the same in both columns
-- since we have the same columns we need to specify which table they're coming from
SELECT *
FROM employee_demographics
JOIN employee_salary
ON employee_demographics.employee_id = employee_salary.employee_id;
-- 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
-- use aliasing!
SELECT *
FROM employee_demographics dem
INNER JOIN employee_salary sal
ON dem.employee_id = sal.employee_id;
-- OUTER JOINS
-- for outer joins we have a left and a right join
-- 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
-- the exact opposite is true for a right join
SELECT *
FROM employee_salary sal
LEFT JOIN employee_demographics dem
ON dem.employee_id = sal.employee_id;
-- so you'll notice we have everything from the left table or the salary table. Even though there is no match to ron swanson.
-- Since there is not match on the right table it's just all Nulls
-- if we just switch this to a right join it basically just looks like an inner join
-- 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
-- it looks kind of like an inner join
SELECT *
FROM employee_salary sal
RIGHT JOIN employee_demographics dem
ON dem.employee_id = sal.employee_id;
-- Self Join
-- a self join is where you tie a table to itself
SELECT *
FROM employee_salary;
-- what we could do is a secret santa so the person with the higher ID is the person's secret santa
SELECT *
FROM employee_salary emp1
JOIN employee_salary emp2
ON emp1.employee_id = emp2.employee_id
;
-- now let's change it to give them their secret santa
SELECT *
FROM employee_salary emp1
JOIN employee_salary emp2
ON emp1.employee_id + 1 = emp2.employee_id
;
SELECT 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
FROM employee_salary emp1
JOIN employee_salary emp2
ON emp1.employee_id + 1 = emp2.employee_id
;
-- 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
-- Joining multiple tables
-- now we have on other table we can join - let's take a look at it
SELECT *
FROM parks_and_recreation.parks_departments;
SELECT *
FROM employee_demographics dem
INNER JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
JOIN parks_departments dept
ON dept.department_id = sal.dept_id;
-- 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
-- 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
SELECT *
FROM employee_demographics dem
INNER JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
LEFT JOIN parks_departments dept
ON dept.department_id = sal.dept_id;
================================================
FILE: Intermediate - String Functions.sql
================================================
#Now let's look at string functions. These help us change and look at strings differently.
SELECT *
FROM bakery.customers;
#Length will give us the length of each value
SELECT LENGTH('sky');
#Now we can see the length of each name
SELECT first_name, LENGTH(first_name)
FROM employee_demographics;
#Upper will change all the string characters to upper case
SELECT UPPER('sky');
SELECT first_name, UPPER(first_name)
FROM employee_demographics;
#lower will change all the string characters to lower case
SELECT LOWER('sky');
SELECT first_name, LOWER(first_name)
FROM employee_demographics;
#Now if you have values that have white space on the front or end, we can get rid of that white space using TRIM
SELECT TRIM('sky' );
#Now if we have white space in the middle it doesn't work
SELECT LTRIM(' I love SQL');
#There's also L trim for trimming just the left side
SELECT LTRIM(' I love SQL');
#There's also R trim for trimming just the Right side
SELECT RTRIM('I love SQL ');
#Now we have Left. Left is going to allow us to take a certain amount of strings from the left hand side.
SELECT LEFT('Alexander', 4);
SELECT first_name, LEFT(first_name,4)
FROM employee_demographics;
#Right is basically the opposite - taking it starting from the right side
SELECT RIGHT('Alexander', 6);
SELECT first_name, RIGHT(first_name,4)
FROM employee_demographics;
#Now let's look at substring, this one I personally love and use a lot.
#Substring allows you to specify a starting point and how many characters you want so you can take characters from anywhere in the string.
SELECT SUBSTRING('Alexander', 2, 3);
#We could use this on phones to get the area code at the beginning.
SELECT birth_date, SUBSTRING(birth_date,1,4) as birth_year
FROM employee_demographics;
#We can also use replace
SELECT REPLACE(first_name,'a','z')
FROM employee_demographics;
#Next we have locate - we have 2 arguments we can use here: we can specify what we are searching for and where to search
#It will return the position of that character in the string.
SELECT LOCATE('x', 'Alexander');
#Now Alexander has 2 e's - what will happen if we try to locate it
SELECT LOCATE('e', 'Alexander');
#It will return the location of just the first position.
#Let's try it on our first name
SELECT first_name, LOCATE('a',first_name)
FROM employee_demographics;
#You can also locate longer strings
SELECT first_name, LOCATE('Mic',first_name)
FROM employee_demographics;
#Now let's look at concatenate - it will combine the strings together
SELECT CONCAT('Alex', 'Freberg');
#Here we can combine the first and the last name columns together
SELECT CONCAT(first_name, ' ', last_name) AS full_name
FROM employee_demographics;
================================================
FILE: Intermediate - Subqueries.sql
================================================
# Subqueries
#So subqueries are queries within queries. Let's see how this looks.
SELECT *
FROM employee_demographics;
#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
#We can do that like this:
SELECT *
FROM employee_demographics
WHERE employee_id IN
(SELECT employee_id
FROM employee_salary
WHERE dept_id = 1);
#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
SELECT *
FROM employee_demographics
WHERE employee_id IN
(SELECT employee_id, salary
FROM employee_salary
WHERE dept_id = 1);
# 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
#We can also use subqueries in the select and the from statements - let's see how we can do this
-- Let's say we want to look at the salaries and compare them to the average salary
SELECT first_name, salary, AVG(salary)
FROM employee_salary;
-- 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
-- if we do that though we don't exactly get what we want
SELECT first_name, salary, AVG(salary)
FROM employee_salary
GROUP BY first_name, salary;
-- it's giving us the average PER GROUP which we don't want
-- here's a good use for a subquery
SELECT first_name,
salary,
(SELECT AVG(salary)
FROM employee_salary)
FROM employee_salary;
-- We can also use it in the FROM Statement
-- when we use it here it's almost like we are creating a small table we are querying off of
SELECT *
FROM (SELECT gender, MIN(age), MAX(age), COUNT(age),AVG(age)
FROM employee_demographics
GROUP BY gender)
;
-- now this doesn't work because we get an error saying we have to name it
SELECT gender, AVG(Min_age)
FROM (SELECT gender, MIN(age) Min_age, MAX(age) Max_age, COUNT(age) Count_age ,AVG(age) Avg_age
FROM employee_demographics
GROUP BY gender) AS Agg_Table
GROUP BY gender
;
================================================
FILE: Intermediate - Unions.sql
================================================
#UNIONS
#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
#Joins allow you to combine the rows of data
#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.
#Let's try it out and use Union to bring together some random data, then we will look at an actual use case
SELECT first_name, last_name
FROM employee_demographics
UNION
SELECT occupation, salary
FROM employee_salary;
#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
#This obviously is not good since you're mixing data, but it can be done if you want.
SELECT first_name, last_name
FROM employee_demographics
UNION
SELECT first_name, last_name
FROM employee_salary;
-- notice it gets rid of duplicates? Union is actually shorthand for Union Distinct
SELECT first_name, last_name
FROM employee_demographics
UNION DISTINCT
SELECT first_name, last_name
FROM employee_salary;
-- we can use UNION ALL to show all values
SELECT first_name, last_name
FROM employee_demographics
UNION ALL
SELECT first_name, last_name
FROM employee_salary;
#Now Let's actually try to use UNION
# 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
-- let's create some queries to help with this
SELECT first_name, last_name, 'Old'
FROM employee_demographics
WHERE age > 50;
SELECT first_name, last_name, 'Old Lady' as Label
FROM employee_demographics
WHERE age > 40 AND gender = 'Female'
UNION
SELECT first_name, last_name, 'Old Man'
FROM employee_demographics
WHERE age > 40 AND gender = 'Male'
UNION
SELECT first_name, last_name, 'Highly Paid Employee'
FROM employee_salary
WHERE salary >= 70000
ORDER BY first_name
;
================================================
FILE: Intermediate - Window Functions.sql
================================================
-- Window Functions
-- windows functions are really powerful and are somewhat like a group by - except they don't roll everything up into 1 row when grouping.
-- windows functions allow us to look at a partition or a group, but they each keep their own unique rows in the output
-- we will also look at things like Row Numbers, rank, and dense rank
SELECT *
FROM employee_demographics;
-- first let's look at group by
SELECT gender, ROUND(AVG(salary),1)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
GROUP BY gender
;
-- now let's try doing something similar with a window function
SELECT dem.employee_id, dem.first_name, gender, salary,
AVG(salary) OVER()
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- now we can add any columns and it works. We could get this exact same output with a subquery in the select statement,
-- but window functions have a lot more functionality, let's take a look
-- 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
SELECT dem.employee_id, dem.first_name, gender, salary,
AVG(salary) OVER(PARTITION BY gender)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- 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
SELECT dem.employee_id, dem.first_name, gender, salary,
SUM(salary) OVER(PARTITION BY gender ORDER BY employee_id)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- Let's look at row_number rank and dense rank now
SELECT dem.employee_id, dem.first_name, gender, salary,
ROW_NUMBER() OVER(PARTITION BY gender)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- let's try ordering by salary so we can see the order of highest paid employees by gender
SELECT dem.employee_id, dem.first_name, gender, salary,
ROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc)
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- let's compare this to rank
SELECT dem.employee_id, dem.first_name, gender, salary,
ROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc) row_num,
Rank() OVER(PARTITION BY gender ORDER BY salary desc) rank_1
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
-- notice rank repeats on tom ad jerry at 5, but then skips 6 to go to 7 -- this goes based off positional rank
-- let's compare this to dense rank
SELECT dem.employee_id, dem.first_name, gender, salary,
ROW_NUMBER() OVER(PARTITION BY gender ORDER BY salary desc) row_num,
Rank() OVER(PARTITION BY gender ORDER BY salary desc) rank_1,
dense_rank() OVER(PARTITION BY gender ORDER BY salary desc) dense_rank_2 -- this is numerically ordered instead of positional like rank
FROM employee_demographics dem
JOIN employee_salary sal
ON dem.employee_id = sal.employee_id
;
================================================
FILE: Portfolio Project - Data Cleaning.sql
================================================
-- SQL Project - Data Cleaning
-- https://www.kaggle.com/datasets/swaptr/layoffs-2022
SELECT *
FROM world_layoffs.layoffs;
-- 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
CREATE TABLE world_layoffs.layoffs_staging
LIKE world_layoffs.layoffs;
INSERT layoffs_staging
SELECT * FROM world_layoffs.layoffs;
-- now when we are data cleaning we usually follow a few steps
-- 1. check for duplicates and remove any
-- 2. standardize data and fix errors
-- 3. Look at null values and see what
-- 4. remove any columns and rows that are not necessary - few ways
-- 1. Remove Duplicates
# First let's check for duplicates
SELECT *
FROM world_layoffs.layoffs_staging
;
SELECT company, industry, total_laid_off,`date`,
ROW_NUMBER() OVER (
PARTITION BY company, industry, total_laid_off,`date`) AS row_num
FROM
world_layoffs.layoffs_staging;
SELECT *
FROM (
SELECT company, industry, total_laid_off,`date`,
ROW_NUMBER() OVER (
PARTITION BY company, industry, total_laid_off,`date`
) AS row_num
FROM
world_layoffs.layoffs_staging
) duplicates
WHERE
row_num > 1;
-- let's just look at oda to confirm
SELECT *
FROM world_layoffs.layoffs_staging
WHERE company = 'Oda'
;
-- 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
-- these are our real duplicates
SELECT *
FROM (
SELECT company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions,
ROW_NUMBER() OVER (
PARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions
) AS row_num
FROM
world_layoffs.layoffs_staging
) duplicates
WHERE
row_num > 1;
-- these are the ones we want to delete where the row number is > 1 or 2or greater essentially
-- now you may want to write it like this:
WITH DELETE_CTE AS
(
SELECT *
FROM (
SELECT company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions,
ROW_NUMBER() OVER (
PARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions
) AS row_num
FROM
world_layoffs.layoffs_staging
) duplicates
WHERE
row_num > 1
)
DELETE
FROM DELETE_CTE
;
WITH DELETE_CTE AS (
SELECT company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions,
ROW_NUMBER() OVER (PARTITION BY company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions) AS row_num
FROM world_layoffs.layoffs_staging
)
DELETE FROM world_layoffs.layoffs_staging
WHERE (company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions, row_num) IN (
SELECT company, location, industry, total_laid_off, percentage_laid_off, `date`, stage, country, funds_raised_millions, row_num
FROM DELETE_CTE
) AND row_num > 1;
-- 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
-- so let's do it!!
ALTER TABLE world_layoffs.layoffs_staging ADD row_num INT;
SELECT *
FROM world_layoffs.layoffs_staging
;
CREATE TABLE `world_layoffs`.`layoffs_staging2` (
`company` text,
`location`text,
`industry`text,
`total_laid_off` INT,
`percentage_laid_off` text,
`date` text,
`stage`text,
`country` text,
`funds_raised_millions` int,
row_num INT
);
INSERT INTO `world_layoffs`.`layoffs_staging2`
(`company`,
`location`,
`industry`,
`total_laid_off`,
`percentage_laid_off`,
`date`,
`stage`,
`country`,
`funds_raised_millions`,
`row_num`)
SELECT `company`,
`location`,
`industry`,
`total_laid_off`,
`percentage_laid_off`,
`date`,
`stage`,
`country`,
`funds_raised_millions`,
ROW_NUMBER() OVER (
PARTITION BY company, location, industry, total_laid_off,percentage_laid_off,`date`, stage, country, funds_raised_millions
) AS row_num
FROM
world_layoffs.layoffs_staging;
-- now that we have this we can delete rows were row_num is greater than 2
DELETE FROM world_layoffs.layoffs_staging2
WHERE row_num >= 2;
-- 2. Standardize Data
SELECT *
FROM world_layoffs.layoffs_staging2;
-- if we look at industry it looks like we have some null and empty rows, let's take a look at these
SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL
OR industry = ''
ORDER BY industry;
-- let's take a look at these
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE company LIKE 'Bally%';
-- nothing wrong here
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE company LIKE 'airbnb%';
-- it looks like airbnb is a travel, but this one just isn't populated.
-- I'm sure it's the same for the others. What we can do is
-- write a query that if there is another row with the same company name, it will update it to the non-null industry values
-- makes it easy so if there were thousands we wouldn't have to manually check them all
-- we should set the blanks to nulls since those are typically easier to work with
UPDATE world_layoffs.layoffs_staging2
SET industry = NULL
WHERE industry = '';
-- now if we check those are all null
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL
OR industry = ''
ORDER BY industry;
-- now we need to populate those nulls if possible
UPDATE layoffs_staging2 t1
JOIN layoffs_staging2 t2
ON t1.company = t2.company
SET t1.industry = t2.industry
WHERE t1.industry IS NULL
AND t2.industry IS NOT NULL;
-- and if we check it looks like Bally's was the only one without a populated row to populate this null values
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL
OR industry = ''
ORDER BY industry;
-- ---------------------------------------------------
-- I also noticed the Crypto has multiple different variations. We need to standardize that - let's say all to Crypto
SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;
UPDATE layoffs_staging2
SET industry = 'Crypto'
WHERE industry IN ('Crypto Currency', 'CryptoCurrency');
-- now that's taken care of:
SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;
-- --------------------------------------------------
-- we also need to look at
SELECT *
FROM world_layoffs.layoffs_staging2;
-- everything looks good except apparently we have some "United States" and some "United States." with a period at the end. Let's standardize this.
SELECT DISTINCT country
FROM world_layoffs.layoffs_staging2
ORDER BY country;
UPDATE layoffs_staging2
SET country = TRIM(TRAILING '.' FROM country);
-- now if we run this again it is fixed
SELECT DISTINCT country
FROM world_layoffs.layoffs_staging2
ORDER BY country;
-- Let's also fix the date columns:
SELECT *
FROM world_layoffs.layoffs_staging2;
-- we can use str to date to update this field
UPDATE layoffs_staging2
SET `date` = STR_TO_DATE(`date`, '%m/%d/%Y');
-- now we can convert the data type properly
ALTER TABLE layoffs_staging2
MODIFY COLUMN `date` DATE;
SELECT *
FROM world_layoffs.layoffs_staging2;
-- 3. Look at Null Values
-- 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
-- I like having them null because it makes it easier for calculations during the EDA phase
-- so there isn't anything I want to change with the null values
-- 4. remove any columns and rows we need to
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL;
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL
AND percentage_laid_off IS NULL;
-- Delete Useless data we can't really use
DELETE FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL
AND percentage_laid_off IS NULL;
SELECT *
FROM world_layoffs.layoffs_staging2;
ALTER TABLE layoffs_staging2
DROP COLUMN row_num;
SELECT *
FROM world_layoffs.layoffs_staging2;
================================================
FILE: Portfolio Project - EDA.sql
================================================
-- EDA
-- Here we are jsut going to explore the data and find trends or patterns or anything interesting like outliers
-- normally when you start the EDA process you have some idea of what you're looking for
-- with this info we are just going to look around and see what we find!
SELECT *
FROM world_layoffs.layoffs_staging2;
-- EASIER QUERIES
SELECT MAX(total_laid_off)
FROM world_layoffs.layoffs_staging2;
-- Looking at Percentage to see how big these layoffs were
SELECT MAX(percentage_laid_off), MIN(percentage_laid_off)
FROM world_layoffs.layoffs_staging2
WHERE percentage_laid_off IS NOT NULL;
-- Which companies had 1 which is basically 100 percent of they company laid off
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE percentage_laid_off = 1;
-- these are mostly startups it looks like who all went out of business during this time
-- if we order by funcs_raised_millions we can see how big some of these companies were
SELECT *
FROM world_layoffs.layoffs_staging2
WHERE percentage_laid_off = 1
ORDER BY funds_raised_millions DESC;
-- BritishVolt looks like an EV company, Quibi! I recognize that company - wow raised like 2 billion dollars and went under - ouch
-- SOMEWHAT TOUGHER AND MOSTLY USING GROUP BY--------------------------------------------------------------------------------------------------
-- Companies with the biggest single Layoff
SELECT company, total_laid_off
FROM world_layoffs.layoffs_staging
ORDER BY 2 DESC
LIMIT 5;
-- now that's just on a single day
-- Companies with the most Total Layoffs
SELECT company, SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY company
ORDER BY 2 DESC
LIMIT 10;
-- by location
SELECT location, SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY location
ORDER BY 2 DESC
LIMIT 10;
-- this it total in the past 3 years or in the dataset
SELECT country, SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY country
ORDER BY 2 DESC;
SELECT YEAR(date), SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY YEAR(date)
ORDER BY 1 ASC;
SELECT industry, SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY industry
ORDER BY 2 DESC;
SELECT stage, SUM(total_laid_off)
FROM world_layoffs.layoffs_staging2
GROUP BY stage
ORDER BY 2 DESC;
-- TOUGHER QUERIES------------------------------------------------------------------------------------------------------------------------------------
-- Earlier we looked at Companies with the most Layoffs. Now let's look at that per year. It's a little more difficult.
-- I want to look at
WITH Company_Year AS
(
SELECT company, YEAR(date) AS years, SUM(total_laid_off) AS total_laid_off
FROM layoffs_staging2
GROUP BY company, YEAR(date)
)
, Company_Year_Rank AS (
SELECT company, years, total_laid_off, DENSE_RANK() OVER (PARTITION BY years ORDER BY total_laid_off DESC) AS ranking
FROM Company_Year
)
SELECT company, years, total_laid_off, ranking
FROM Company_Year_Rank
WHERE ranking <= 3
AND years IS NOT NULL
ORDER BY years ASC, total_laid_off DESC;
-- Rolling Total of Layoffs Per Month
SELECT SUBSTRING(date,1,7) as dates, SUM(total_laid_off) AS total_laid_off
FROM layoffs_staging2
GROUP BY dates
ORDER BY dates ASC;
-- now use it in a CTE so we can query off of it
WITH DATE_CTE AS
(
SELECT SUBSTRING(date,1,7) as dates, SUM(total_laid_off) AS total_laid_off
FROM layoffs_staging2
GROUP BY dates
ORDER BY dates ASC
)
SELECT dates, SUM(total_laid_off) OVER (ORDER BY dates ASC) as rolling_total_layoffs
FROM DATE_CTE
ORDER BY dates ASC;
================================================
FILE: README.md
================================================
# MySQL-YouTube-Series
================================================
FILE: layoffs.csv
================================================
company,location,industry,total_laid_off,percentage_laid_off,date,stage,country,funds_raised_millions
Atlassian,Sydney,Other,500,0.05,3/6/2023,Post-IPO,Australia,210
SiriusXM,New York City,Media,475,0.08,3/6/2023,Post-IPO,United States,525
Alerzo,Ibadan,Retail,400,NULL,3/6/2023,Series B,Nigeria,16
UpGrad,Mumbai,Education,120,NULL,3/6/2023,Unknown,India,631
Loft,Sao Paulo,Real Estate,340,0.15,3/3/2023,Unknown,Brazil,788
Embark Trucks,SF Bay Area,Transportation,230,0.7,3/3/2023,Post-IPO,United States,317
Lendi,Sydney,Real Estate,100,NULL,3/3/2023,Unknown,Australia,59
UserTesting,SF Bay Area,Marketing,63,NULL,3/3/2023,Acquired,United States,152
Airbnb,SF Bay Area,,30,NULL,3/3/2023,Post-IPO,United States,6400
Accolade,Seattle,Healthcare,NULL,NULL,3/3/2023,Post-IPO,United States,458
Indigo,Boston,Other,NULL,NULL,3/3/2023,Series F,United States.,1200
Zscaler,SF Bay Area,Security,177,0.03,3/2/2023,Post-IPO,United States,148
MasterClass,SF Bay Area,Education,79,NULL,3/2/2023,Series E,United States,461
Ambev Tech,Blumenau,Food,50,NULL,3/2/2023,Acquired,Brazil,NULL
Fittr,Pune,Fitness,30,0.11,3/2/2023,Series A,India,13
CNET,SF Bay Area,Media,12,0.1,3/2/2023,Acquired,United States,20
Flipkart,Bengaluru,Retail,NULL,NULL,3/2/2023,Acquired,India,12900
Kandela,Los Angeles,Consumer,NULL,1,3/2/2023,Acquired,United States,NULL
Truckstop.com,Boise,Logistics,NULL,NULL,3/2/2023,Acquired,United States,NULL
Thoughtworks,Chicago,Other,500,0.04,3/1/2023,Post-IPO,United States,748
iFood,Sao Paulo,Food,355,0.06,3/1/2023,Subsidiary,Brazil,2100
Color Health,SF Bay Area,Healthcare,300,NULL,3/1/2023,Series E,United States,482
Waymo,SF Bay Area,Transportation,209,0.08,3/1/2023,Subsidiary,United States,5500
PayFit,Paris,HR,200,0.2,3/1/2023,Series E,France,495
Yellow.ai,SF Bay Area,Support,200,NULL,3/1/2023,Series C,United States,102
Sonder,SF Bay Area,Travel,100,0.14,3/1/2023,Post-IPO,United States,839
Protego Trust Bank,Seattle,Crypto,NULL,0.5,3/1/2023,Series A,United States,70
Electronic Arts,Baton Rouge,Consumer,200,NULL,2/28/2023,Post-IPO,United States,2
Eventbrite,SF Bay Area,Consumer,80,0.08,2/28/2023,Post-IPO,United States,557
DUX Education,Bengaluru,Education,NULL,1,2/28/2023,Unknown,India,NULL
MeridianLink,Los Angeles,Finance,NULL,0.09,2/28/2023,Post-IPO,United States,485
Sono Motors,Munich,Transportation,300,NULL,2/27/2023,Post-IPO,Germany,126
Cerebral,SF Bay Area,Healthcare,285,0.15,2/27/2023,Series C,United States,462
Amount,Chicago,Finance,130,0.25,2/27/2023,Unknown,United States,283
Palantir,Denver,Data,75,0.02,2/27/2023,Post-IPO,United States.,3000
Outreach,Seattle,Sales,70,0.07,2/27/2023,Series G,United States,489
Stytch,SF Bay Area,Security,19,0.25,2/27/2023,Series B,United States,126
BitSight,Tel Aviv,Security,40,NULL,2/26/2023,Unknown,Israel,401
Twitter,SF Bay Area,Consumer,200,0.1,2/25/2023,Post-IPO,United States,12900
Ericsson,Stockholm,Other,8500,0.08,2/24/2023,Post-IPO,Sweden,663
SAP Labs,Bengaluru,Other,300,NULL,2/24/2023,Subsidiary,India,1300
Velodyne Lidar,SF Bay Area,Transportation,220,NULL,2/24/2023,Acquired,United States,575
Medallia,SF Bay Area,Support,59,NULL,2/24/2023,Acquired,United States,325
Eat Just,SF Bay Area,Food,40,NULL,2/24/2023,Unknown,United States,465
Lucira Health,SF Bay Area,Healthcare,26,NULL,2/24/2023,Post-IPO,United States,132
Stax,Orlando,Finance,24,NULL,2/24/2023,Series D,United States,263
Poshmark,SF Bay Area,Retail,NULL,0.02,2/24/2023,Acquired,United States,153
Merative,Ann Arbor,Healthcare,200,0.1,2/23/2023,Acquired,United States,NULL
OneFootball,Berlin,Marketing,150,0.32,2/23/2023,Series D,Germany,442
The Iconic,Sydney,Retail,69,0.06,2/23/2023,Unknown,Australia,NULL
EVgo,Los Angeles,Transportation,40,NULL,2/23/2023,Post-IPO,United States,400
StrongDM,SF Bay Area,Infrastructure,40,NULL,2/23/2023,Series B,United States,76
Dapper Labs,Vancouver,Crypto,NULL,0.2,2/23/2023,Series D,United States,607
Messari,New York City,Crypto,NULL,0.15,2/23/2023,Series B,United States,61
Vibrent Health,Washington D.C.,Healthcare,NULL,0.13,2/23/2023,Unknown,United States,NULL
Synamedia,London,Media,200,0.12,2/22/2023,Unknown,United Kingdom,NULL
TaskUs,San Antonio,Support,186,NULL,2/22/2023,Post-IPO,United States,279
Arch Oncology,St. Louis,Healthcare,NULL,NULL,2/22/2023,Series C,United States,155
Immutable,Sydney,Crypto,NULL,0.11,2/22/2023,Series C,Australia,279
Jounce Therapeutics,Boston,Healthcare,NULL,0.57,2/22/2023,Acquired,United States,194
Locomation,Pittsburgh,Transportation,NULL,1,2/22/2023,Seed,United States,57
Polygon,Bengaluru,Crypto,100,0.2,2/21/2023,Unknown,India,451
Crunchyroll,Tokyo,Media,85,NULL,2/21/2023,Unknown,Japan,26
Ethos Life,SF Bay Area,Finance,50,NULL,2/21/2023,Series D,United States,406
Bolt,Lagos,Transportation,17,NULL,2/21/2023,Series F,Nigeria,NULL
Criteo,Paris,Marketing,NULL,NULL,2/21/2023,Post-IPO,France,61
Green Labs,Seoul,Food,NULL,NULL,2/21/2023,Series C,South Korea,214
PeerStreet,Los Angeles,Real Estate,NULL,NULL,2/21/2023,Series C,United States,121
Zalando,Berlin,Retail,NULL,NULL,2/21/2023,Post-IPO,Germany,467
MyGate,Bengaluru,Other,200,0.3,2/20/2023,Series B,India,79
Fireblocks,New York City,Crypto,30,0.05,2/20/2023,Series E,United States,1000
Kinde,Sydney,Other,8,0.28,2/20/2023,Seed,Australia,10
Fipola,Chennai,Food,NULL,1,2/20/2023,Series A,United States,9
HP,Tel Aviv,Hardware,100,NULL,2/19/2023,Post-IPO,Israel,4200
Tencent,Shenzen,Consumer,300,NULL,2/17/2023,Post-IPO,China,12600
Evernote,SF Bay Area,Consumer,129,NULL,2/17/2023,Acquired,United States,290
Chipper Cash,SF Bay Area,Finance,100,0.33,2/17/2023,Series C,United States,302
Digimarc,Portland,Other,NULL,NULL,2/17/2023,Post-IPO,United States,105
Reserve,SF Bay Area,Crypto,NULL,NULL,2/17/2023,Unknown,United States,NULL
DocuSign,SF Bay Area,Sales,680,0.1,2/16/2023,Post-IPO,United States,536
Pico Interactive,SF Bay Area,Other,400,0.2,2/16/2023,Acquired,United States,62
The RealReal,SF Bay Area,Retail,230,0.07,2/16/2023,Post-IPO,United States,356
Smartsheet,Seattle,Other,85,0.03,2/16/2023,Post-IPO,United States,152
Convoy,Atlanta,Logistics,NULL,NULL,2/16/2023,Series E,United States,1100
Wix,Tel Aviv,Marketing,370,0.06,2/15/2023,Post-IPO,Israel,58
ServiceTitan,Los Angeles,Sales,221,0.08,2/15/2023,Series G,United States,1100
Neon,Sao Paulo,Finance,210,0.09,2/15/2023,Series D,Brazil,720
Jellysmack,Paris,Media,208,NULL,2/15/2023,Series C,France,22
DigitalOcean,New York City,Infrastructure,200,0.11,2/15/2023,Post-IPO,United States,491
Sprinklr,New York City,Support,100,0.04,2/15/2023,Post-IPO,United States,429
Betterment,New York City,Finance,28,NULL,2/15/2023,Series F,United States,435
Divvy Homes,SF Bay Area,Real Estate,NULL,NULL,2/15/2023,Series B,United States,180
Milkrun,Sydney,Food,NULL,0.2,2/15/2023,Series A,Australia,86
Momentive,SF Bay Area,Marketing,NULL,0.14,2/15/2023,Post-IPO,United States,1100
Observe.AI,SF Bay Area,Support,NULL,NULL,2/15/2023,Series C,United States,214
Religion of Sports,Los Angeles,Media,NULL,NULL,2/15/2023,Series B,United States,63
Tackle,Boise,Other,NULL,0.15,2/15/2023,Series C,United States,148
Vicarious Surgical,Boston,Healthcare,NULL,0.14,2/15/2023,Post-IPO,United States,185
CommerceHub,Albany,Retail,371,0.31,2/14/2023,Acquired,United States,NULL
HackerEarth,SF Bay Area,HR,NULL,0.08,2/14/2023,Series B,United States,11
PhableCare,Bengaluru,Healthcare,NULL,0.7,2/14/2023,Series B,India,40
Udemy,SF Bay Area,Education,NULL,0.1,2/14/2023,Post-IPO,United States,311
Twilio,SF Bay Area,Other,1500,0.17,2/13/2023,Post-IPO,United States.,614
Electric,New York City,Other,141,0.25,2/13/2023,Series D,United States,212
EMX Digital,New York City,Marketing,100,1,2/13/2023,Unknown,United States,NULL
PetLove,Sao Paulo,Retail,94,NULL,2/13/2023,Series C,Brazil,225
iRobot,Boston,Consumer,85,0.07,2/13/2023,Acquired,United States,30
Collective Health,SF Bay Area,Healthcare,54,NULL,2/13/2023,Series F,United States,719
Magic Eden,SF Bay Area,Crypto,22,NULL,2/13/2023,Series B,United States,170
Casavo,Milan,Real Estate,NULL,0.3,2/13/2023,Unknown,Italy,708
Foodpanda,Singapore,Food,NULL,NULL,2/13/2023,Acquired,Singapore,749
Getir,London,Food,NULL,NULL,2/13/2023,Series E,United Kingdom,1800
LinkedIn,SF Bay Area,HR,NULL,NULL,2/13/2023,Acquired,United States,154
Moladin,Jakarta,Transportation,360,0.11,2/12/2023,Series B,Indonesia,138
TripleLift,New York City,Marketing,100,0.2,2/10/2023,Acquired,United States,16
TikTok India,Mumbai,Consumer,40,NULL,2/10/2023,Acquired,India,9400
Open Co,Sao Paulo,Finance,NULL,NULL,2/10/2023,Series D,Brazil,140
Rigetti Computing,SF Bay Area,Hardware,NULL,0.28,2/10/2023,Post-IPO,United States,298
Yahoo,SF Bay Area,Consumer,1600,0.2,2/9/2023,Acquired,United States,6
Misfits Market,Philadelphia,Food,649,0.33,2/9/2023,Series C,United States,526
Deliveroo,London,Food,350,0.09,2/9/2023,Post-IPO,United Kingdom,1700
Olive AI,Columbus,Healthcare,215,0.35,2/9/2023,Series H,United States,856
Oportun,SF Bay Area,Finance,155,0.1,2/9/2023,Post-IPO,United States,566
GitLab,SF Bay Area,Product,130,0.07,2/9/2023,Post-IPO,United States,413
Bark,New York City,Retail,126,0.12,2/9/2023,Post-IPO,United States,NULL
Nomad Health,New York City,Healthcare,119,0.17,2/9/2023,Unknown,United States,218
Veriff,Tallinn,Security,66,0.12,2/9/2023,Series C,Estonia,192
REE Automotive,Tel Aviv,Transportation,31,0.11,2/9/2023,Post-IPO,Israel,317
GitHub,SF Bay Area,Product,NULL,0.1,2/9/2023,Acquired,United States,350
Quillt,St. Louis,Media,NULL,NULL,2/9/2023,Unknown,United States,NULL
WeTrade,Bengaluru,Crypto,NULL,1,2/9/2023,Unknown,India,NULL
GoDaddy,Phoenix,Marketing,530,0.08,2/8/2023,Post-IPO,United States,800
Affirm,SF Bay Area,Finance,500,0.19,2/8/2023,Post-IPO,United States,1500
Gusto,SF Bay Area,HR,126,0.05,2/8/2023,Series E,United States,746
Gong,SF Bay Area,Sales,80,0.07,2/8/2023,Series E,United States,583
Beam Benefits,Columbus,Healthcare,31,0.08,2/8/2023,Series E,United States,168
Equitybee,SF Bay Area,Finance,24,0.25,2/8/2023,Series B,United States,85
Baraja,Sydney,Transportation,NULL,0.75,2/8/2023,Unknown,Australia,63
Koho,Toronto,Finance,NULL,0.14,2/8/2023,Series D,Canada,278
Medly,New York City,Healthcare,NULL,1,2/8/2023,Series C,United States,100
Nearmap,Sydney,Construction,NULL,0.2,2/8/2023,Acquired,Australia,15
Zoom,SF Bay Area,Other,1300,0.15,2/7/2023,Post-IPO,United States,276
eBay,SF Bay Area,Retail,500,0.04,2/7/2023,Post-IPO,United States,1200
SecureWorks,Atlanta,Security,212,0.09,2/7/2023,Post-IPO,United States,83
Salesloft,Atlanta,Sales,100,0.1,2/7/2023,Acquired,United States,245
Openpay,Melbourne,Finance,83,1,2/7/2023,Post-IPO,Australia,299
LearnUpon,Dublin,Education,27,0.09,2/7/2023,Private Equity,Ireland,56
Sana Benefits,Austin,HR,NULL,0.19,2/7/2023,Series B,United States,106
Dell,Austin,Hardware,6650,0.05,2/6/2023,Post-IPO,United States,NULL
Loggi,Sao Paulo,Logistics,300,0.07,2/6/2023,Series F,Brazil,507
Catch.com.au,Melbourne,Retail,100,NULL,2/6/2023,Acquired,Australia,80
VinFast US,Los Angeles,Transportation,80,NULL,2/6/2023,Subsidiary,United States,NULL
Drift,Boston,Marketing,59,NULL,2/6/2023,Acquired,United States,107
Pocket Aces,Mumbai,Media,50,0.25,2/6/2023,Unknown,India,19
Clari,SF Bay Area,Sales,20,NULL,2/6/2023,Series F,United States,496
C6 Bank,Sao Paulo,Finance,NULL,NULL,2/6/2023,Unknown,Brazil,2300
Daraz,Singapore,Retail,NULL,0.11,2/6/2023,Unknown,Singapore,NULL
TenureX,Tel Aviv,Finance,NULL,NULL,2/6/2023,Seed,Israel,6
Kyruus,Boston,Healthcare,70,NULL,2/5/2023,Unknown,United States,183
Lightico,Tel Aviv,Finance,20,0.25,2/5/2023,Series B,Israel,42
FarEye,New Delhi,Logistics,90,NULL,2/3/2023,Series E,India,150
Protocol Labs,SF Bay Area,Crypto,89,0.2,2/3/2023,Unknown,United States,10
Finder,Sydney,Retail,NULL,0.15,2/3/2023,Unknown,Australia,30
Byju's,Bengaluru,Education,1500,NULL,2/2/2023,Private Equity,India,5500
Okta,SF Bay Area,Security,300,0.05,2/2/2023,Post-IPO,United States,1200
Autodesk,SF Bay Area,Other,250,0.02,2/2/2023,Post-IPO,United States,NULL
Mindstrong,SF Bay Area,Healthcare,127,NULL,2/2/2023,Series C,United States,160
NCC Group,Manchester,Security,125,0.07,2/2/2023,Post-IPO,United Kingdom,NULL
Miro,SF Bay Area,Other,119,0.07,2/2/2023,Series C,United States,476
Getir,New York City,Food,100,NULL,2/2/2023,Series E,United States,1800
Highspot,Seattle,Sales,100,0.1,2/2/2023,Series F,United States,644
Bittrex,Seattle,Crypto,80,NULL,2/2/2023,Unknown,United States,NULL
Snowplow,London,Data,40,NULL,2/2/2023,Series B,United States,55
Articulate,New York City,Education,38,NULL,2/2/2023,Series A,United States,1500
Desktop Metal,Boston,Other,NULL,NULL,2/2/2023,Post-IPO,United States,811
Getaround,SF Bay Area,Transportation,NULL,0.1,2/2/2023,Post-IPO,United States,948
NCSoft,Seoul,Consumer,NULL,0.2,2/2/2023,Post-IPO,South Korea,240
Talkdesk,SF Bay Area,Support,NULL,NULL,2/2/2023,Series D,United States,497
Splunk,SF Bay Area,Data,325,0.04,2/1/2023,Post-IPO,United States,2400
Pinterest,SF Bay Area,Consumer,150,NULL,2/1/2023,Post-IPO,United States,1500
DraftKings,Boston,Consumer,140,0.04,2/1/2023,Post-IPO,United States,719
Cyren,Washington D.C.,Security,121,NULL,2/1/2023,Post-IPO,United States,161
Workato,SF Bay Area,Other,90,0.1,2/1/2023,Series E,United States,415
VerticalScope,Toronto,Media,60,0.22,2/1/2023,Post-IPO,Canada,NULL
Wheel,Austin,Healthcare,56,0.28,2/1/2023,Series C,United States,215
Chainalysis,New York City,Crypto,44,0.05,2/1/2023,Series F,United States,536
Appgate,Miami,Security,34,0.08,2/1/2023,Post-IPO,United States,NULL
Exterro,Portland,Legal,24,0.03,2/1/2023,Private Equity,United States,100
TheSkimm,New York City,Media,17,0.1,2/1/2023,Series C,United States,28
Ada,Toronto,Support,NULL,NULL,2/1/2023,Series C,Canada,190
Bustle Digital Group,New York City,Media,NULL,0.08,2/1/2023,Series E,United States,80
Frequency Therapeutics,Boston,Healthcare,NULL,0.5,2/1/2023,Post-IPO,United States,76
MariaDB,Helsinki,Data,NULL,0.08,2/1/2023,Post-IPO,Finland,245
Match Group,New York City,Consumer,NULL,0.08,2/1/2023,Post-IPO,United States,NULL
Omnipresent,London,HR,NULL,NULL,2/1/2023,Series B,United States,137
Picnic,Seattle,Food,NULL,NULL,2/1/2023,Unknown,United States,52
Rivian,Detroit,Transportation,NULL,0.06,2/1/2023,Post-IPO,United States,10700
PayPal,SF Bay Area,Finance,2000,0.07,1/31/2023,Post-IPO,United States,216
NetApp,SF Bay Area,Data,960,0.08,1/31/2023,Post-IPO,United States,NULL
Workday,SF Bay Area,HR,525,0.03,1/31/2023,Post-IPO,United States,230
HubSpot,Boston,Marketing,500,0.07,1/31/2023,Post-IPO,United States,100
Upstart,SF Bay Area,Finance,365,0.2,1/31/2023,Post-IPO,United States,144
Software AG,Frankfurt,Data,200,0.04,1/31/2023,Post-IPO,Germany,344
Wish,SF Bay Area,Retail,150,0.17,1/31/2023,Post-IPO,United States,1600
Wefox,Berlin,Finance,100,NULL,1/31/2023,Series D,Germany,1300
Tilting Point,New York City,Consumer,60,0.14,1/31/2023,Unknown,United States,235
Gokada,Lagos,Transportation,54,NULL,1/31/2023,Unknown,Nigeria,12
AU10TIX,Tel Aviv,Security,19,0.09,1/31/2023,Unknown,Israel,80
National Instruments,Austin,Hardware,NULL,0.04,1/31/2023,Post-IPO,United States,NULL
OpenText,Waterloo,Data,NULL,0.08,1/31/2023,Post-IPO,Canada,1100
Philips,Amsterdam,Healthcare,6000,0.13,1/30/2023,Post-IPO,Netherlands,NULL
OLX Group,Amsterdam,Marketing,1500,0.15,1/30/2023,Acquired,Netherlands,NULL
Arrival,London,Transportation,800,0.5,1/30/2023,Post-IPO,United Kingdom,629
Groupon,Chicago,Retail,500,NULL,1/30/2023,Post-IPO,United States,1400
Intel,SF Bay Area,Hardware,343,NULL,1/30/2023,Post-IPO,United States,12
Glovo,Barcelona,Food,250,0.06,1/30/2023,Acquired,Spain,1200
Delivery Hero,Berlin,Food,156,0.04,1/30/2023,Post-IPO,Germany,9900
Impossible Foods copy,SF Bay Area,Food,140,0.2,1/30/2023,Series H,United States,1900
Chrono24,Karlsruhe,Retail,65,0.13,1/30/2023,Series C,Germany,205
BM Technologies,Philadelphia,Finance,NULL,0.25,1/30/2023,Post-IPO,United States,NULL
Olist,Curitiba,Retail,NULL,NULL,1/30/2023,Series E,Brazil,322
Oyster,Charlotte,HR,NULL,NULL,1/30/2023,Series C,United States,224
Prime Trust,Las Vegas,Crypto,NULL,0.33,1/30/2023,Series B,United States,176
Quantum SI,New Haven,Healthcare,NULL,0.12,1/30/2023,Post-IPO,United States,425
SoFi,SF Bay Area,Finance,NULL,NULL,1/30/2023,Post-IPO,United States,3000
Hoxhunt,Helsinki,Security,NULL,NULL,1/29/2023,Series B,Finland,43
Me Poupe,Sao Paulo,Finance,60,0.5,1/28/2023,Unknown,Brazil,NULL
CoinTracker,SF Bay Area,Crypto,19,NULL,1/28/2023,Series A,United States,101
SSense,Montreal,Retail,138,0.07,1/27/2023,Series A,Canada,NULL
DealShare,Bengaluru,Retail,100,0.06,1/27/2023,Series E,India,390
Ruggable,Los Angeles,Retail,100,NULL,1/27/2023,Seed,United States,NULL
Synopsys,SF Bay Area,Other,100,NULL,1/27/2023,Post-IPO,United States,NULL
Heycar,Berlin,Transportation,73,NULL,1/27/2023,Unknown,Germany,NULL
Matrixport,Singapore,Crypto,29,0.1,1/27/2023,Series C,Singapore,100
Shakepay,Montreal,Crypto,21,0.25,1/27/2023,Series A,Canada,45
#Paid,Toronto,Marketing,19,0.17,1/27/2023,Series B,Canada,21
Decent,SF Bay Area,Healthcare,NULL,NULL,1/27/2023,Series A,United States,18
Feedzai,Coimbra,Finance,NULL,NULL,1/27/2023,Unknown,Portugal,273
Nate,New York City,Retail,NULL,NULL,1/27/2023,Series A,United States,47
SAP,Walldorf,Other,3000,0.03,1/26/2023,Post-IPO,Germany,1300
Confluent,SF Bay Area,Data,221,0.08,1/26/2023,Post-IPO,United States,455
DriveWealth,Jersey City,Finance,NULL,0.2,1/26/2023,Series D,United States,550
Mode Global,London,Finance,NULL,1,1/26/2023,Post-IPO,United Kingdom,NULL
Plus One Robotics,San Antonio,Other,NULL,0.1,1/26/2023,Series B,United States,43
Quora,SF Bay Area,Consumer,NULL,NULL,1/26/2023,Series D,United States,226
IBM,New York City,Hardware,3900,0.02,1/25/2023,Post-IPO,United States,NULL
Lam Research,SF Bay Area,Hardware,1300,0.07,1/25/2023,Post-IPO,United States,NULL
Shutterfly,SF Bay Area,Retail,360,NULL,1/25/2023,Acquired,United States,50
Luno,London,Crypto,330,0.35,1/25/2023,Acquired,United Kingdom,13
Clear Capital,Reno,Real Estate,250,0.25,1/25/2023,Unknown,United States,NULL
Guardant Health,SF Bay Area,Healthcare,130,0.07,1/25/2023,Post-IPO,United States,550
SirionLabs,Seattle,Legal,130,0.15,1/25/2023,Series D,United States,171
Tier Mobility,Berlin,Transportation,80,0.07,1/25/2023,Series D,Germany,646
CareRev,Los Angeles,Retail,NULL,NULL,1/25/2023,Series A,United States,51
Finastra,Tel Aviv,Finance,NULL,NULL,1/25/2023,Unknown,Israel,NULL
Noom,New York City,Fitness,NULL,NULL,1/25/2023,Series F,United States,657
PagSeguro,Sao Paulo,Finance,NULL,0.07,1/25/2023,Post-IPO,Brazil,NULL
Prosus,Amsterdam,Other,NULL,0.3,1/25/2023,Unknown,Netherlands,NULL
Vacasa,Portland,Travel,1300,0.17,1/24/2023,Post-IPO,United States,834
Innovaccer,SF Bay Area,Healthcare,245,0.15,1/24/2023,Series E,United States,379
Bolt,SF Bay Area,Finance,50,0.1,1/24/2023,Series E,United States,1300
PartnerStack,Toronto,Sales,40,0.2,1/24/2023,Series B,Canada,36
Gitpod,Kiel,Product,21,0.28,1/24/2023,Series A,Germany,41
OFFOR Health,Columbus,Healthcare,16,NULL,1/24/2023,Series A,United States,14
Venngage,Toronto,Marketing,11,0.2,1/24/2023,Unknown,Canada,NULL
CoachHub,Berlin,HR,NULL,0.1,1/24/2023,Series C,Germany,332
Corvus Insurance,Boston,Finance,NULL,0.14,1/24/2023,Series C,United States,160
Icon,Austin,Construction,NULL,NULL,1/24/2023,Series B,United States,451
PagerDuty,SF Bay Area,Product,NULL,0.07,1/24/2023,Post-IPO,United States,173
Scoro,London,HR,NULL,0.09,1/24/2023,Series B,United Kingdom,23
Spotify,Stockholm,Media,600,0.06,1/23/2023,Post-IPO,Sweden,2100
Uber Freight,SF Bay Area,Logistics,150,0.03,1/23/2023,Subsidiary,United States,2700
Inmobi,Bengaluru,Marketing,50,NULL,1/23/2023,Unknown,India,320
Innovid,New York City,Marketing,40,0.1,1/23/2023,Post-IPO,United States,295
Booktopia,Sydney,Retail,30,NULL,1/23/2023,Series A,Australia,23
Ermetic,SF Bay Area,Security,30,0.17,1/23/2023,Series B,United States,97
Namogoo,Tel Aviv,Marketing,20,0.15,1/23/2023,Series C,United States,69
Camp K12,Gurugram,Education,NULL,0.7,1/23/2023,Series A,India,16
Gemini,New York City,Crypto,NULL,0.1,1/23/2023,Unknown,United States,423
Yext,New York City,Marketing,NULL,0.08,1/23/2023,Post-IPO,United States,117
BUX,Amsterdam,Finance,NULL,NULL,1/22/2023,Series C,Netherlands,115
Google,SF Bay Area,Consumer,12000,0.06,1/20/2023,Post-IPO,United States,26
Wayfair,Boston,Retail,1750,0.1,1/20/2023,Post-IPO,United States,1700
Swiggy,Bengaluru,Food,380,0.06,1/20/2023,Unknown,India,3600
MediBuddy,Bengaluru,Healthcare,200,NULL,1/20/2023,Acquired,India,192
Vox Media,Washington D.C.,Media,130,0.07,1/20/2023,Series F,United States,307
BitTorrent,SF Bay Area,Infrastructure,92,NULL,1/20/2023,Acquired,United States,NULL
Karat,Seattle,HR,47,NULL,1/20/2023,Unknown,United States,169
Enjoei,Sao Paulo,Retail,31,0.1,1/20/2023,Unknown,Brazil,14
Edifecs,Seattle,Healthcare,30,NULL,1/20/2023,Unknown,United States,1
Citrine Informatics,SF Bay Area,Data,22,0.27,1/20/2023,Series C,United States,64
Avalara,Seattle,Finance,NULL,NULL,1/20/2023,Acquired,United States,341
Cyteir Therapeutics,Boston,Healthcare,NULL,0.7,1/20/2023,Series C,United States,156
Morning Consult,Washington D.C.,Data,NULL,NULL,1/20/2023,Series B,United States,91
TikTok,Los Angeles,Consumer,NULL,NULL,1/20/2023,Acquired,United States,NULL
Zappos,Las Vegas,Retail,NULL,NULL,1/20/2023,Acquired,United States,62
Capital One,Washington D.C.,Finance,1100,NULL,1/19/2023,Post-IPO,United States,NULL
Proterra,SF Bay Area,Transportation,300,NULL,1/19/2023,Post-IPO,United States,1200
WeWork ,New York City,Real Estate,300,NULL,1/19/2023,Post-IPO,United States,22200
Hubilo,SF Bay Area,Other,115,0.35,1/19/2023,Series B,United States,153
Saks.com,New York City,Retail,100,0.05,1/19/2023,Unknown,United States,965
CS Disco,Austin,Legal,62,0.09,1/19/2023,Post-IPO,United States,233
Riot Games,Los Angeles,Consumer,46,NULL,1/19/2023,Acquired,United States,21
Hydrow,Boston,Fitness,30,NULL,1/19/2023,Series D,United States,269
Earth Rides,Nashville,Transportation,NULL,1,1/19/2023,Unknown,United States,2
Fandom,SF Bay Area,Media,NULL,NULL,1/19/2023,Series E,United States,145
IAM Robotics,Pittsburgh,Hardware,NULL,NULL,1/19/2023,Unknown,United States,21
Icertis,Seattle,Legal,NULL,NULL,1/19/2023,Unknown,United States,521
Magnite,Los Angeles,Marketing,NULL,0.06,1/19/2023,Post-IPO,United States,400
Mudafy,Mexico City,Real Estate,NULL,0.7,1/19/2023,Series A,United States,13
Personalis,SF Bay Area,Healthcare,NULL,0.3,1/19/2023,Post-IPO,United States,225
Prisma,SF Bay Area,Data,NULL,0.28,1/19/2023,Series B,United States,56
Spaceship,Sydney,Finance,NULL,NULL,1/19/2023,Series A,Australia,41
Wallbox,Barcelona,Energy,NULL,0.15,1/19/2023,Post-IPO,Spain,167
Microsoft,Seattle,Other,10000,0.05,1/18/2023,Post-IPO,United States,1
Sophos,Oxford,Security,450,0.1,1/18/2023,Acquired,United States,125
Teladoc Health,New York City,Healthcare,300,0.06,1/18/2023,Post-IPO,United States,172
Vroom,New York City,Transportation,275,0.2,1/18/2023,Post-IPO,United States,1300
8x8,SF Bay Area,Support,155,0.07,1/18/2023,Post-IPO,United States,253
Pagaya,New York City,Finance,140,0.2,1/18/2023,Post-IPO,United States,571
Benevity,Calgary,Other,137,0.14,1/18/2023,Unknown,Canada,69
Inspirato,Denver,Travel,109,0.12,1/18/2023,Post-IPO,United States,179
Jumpcloud,Boulder,Security,100,0.12,1/18/2023,Series F,United States,416
nCino,Wilmington,Finance,100,0.07,1/18/2023,Post-IPO,United States,1100
Starry,Boston,Other,100,0.24,1/18/2023,Post-IPO,United States,260
Hootsuite,Vancouver,Marketing,70,0.07,1/18/2023,Series C,Canada,300
Clue,Berlin,Healthcare,31,0.31,1/18/2023,Unknown,Germany,47
Addepar,SF Bay Area,Finance,20,0.03,1/18/2023,Series F,United States,491
80 Acres Farms,Cincinnati,Food,NULL,0.1,1/18/2023,Unknown,United States,275
Aiven,Helsinki,Infrastructure,NULL,0.2,1/18/2023,Series D,Finland,420
Bally's Interactive,Providence,NULL,NULL,0.15,1/18/2023,Post-IPO,United States,946
Betterfly,Santiago,Healthcare,NULL,0.3,1/18/2023,Series C,Chile,204
Cazoo,London,Transportation,NULL,NULL,1/18/2023,Post-IPO,United Kingdom,2000
Coda,SF Bay Area,Other,NULL,NULL,1/18/2023,Series D,United States,240
Cypress.io,Atlanta,Product,NULL,NULL,1/18/2023,Series B,United States,54
Lucid Diagnostics,New York City,Healthcare,NULL,0.2,1/18/2023,Post-IPO,United States,NULL
Mavenir,Dallas,Infrastructure,NULL,NULL,1/18/2023,Acquired,United States,854
Redbubble,Melbourne,Retail,NULL,0.14,1/18/2023,Post-IPO,Australia,55
Lightspeed Commerce,Montreal,Retail,300,0.1,1/17/2023,Post-IPO,Canada,1200
Unity,SF Bay Area,Other,284,0.03,1/17/2023,Post-IPO,United States,1300
Britishvolt,London,Transportation,206,1,1/17/2023,Unknown,United Kingdom,2400
Clutch,Toronto,Transportation,150,NULL,1/17/2023,Unknown,Canada,253
Exotel,Bengaluru,Support,142,0.15,1/17/2023,Series D,India,87
Unico,Sao Paulo,Other,110,0.1,1/17/2023,Series D,Brazil,336
Tul,Bogota,Construction,100,NULL,1/17/2023,Series B,Colombia,218
American Robotics,Boston,Other,50,0.65,1/17/2023,Acquired,United States,92
Luxury Presence,Los Angeles,Real Estate,44,NULL,1/17/2023,Series B,United States,31
RingCentral,SF Bay Area,Other,30,NULL,1/17/2023,Post-IPO,United States,44
Fishbrain,Stockholm,Consumer,NULL,NULL,1/17/2023,Unknown,United States,59
GoMechanic,Gurugram,Transportation,NULL,0.7,1/17/2023,Series C,India,54
LiveVox,SF Bay Area,Support,NULL,0.16,1/17/2023,Post-IPO,United States,12
Oracle,SF Bay Area,Other,NULL,NULL,1/17/2023,Post-IPO,United States,NULL
Rappi,Buenos Aires,Food,NULL,NULL,1/17/2023,Unknown,Argentina,2300
RateGenius,Austin,Finance,NULL,NULL,1/17/2023,Acquired,United States,2
XP,Sao Paulo,Finance,NULL,NULL,1/17/2023,Post-IPO,Brazil,NULL
PagBank,Sao Paulo,Finance,500,0.07,1/16/2023,Post-IPO,Brazil,NULL
ShareChat,Bengaluru,Consumer,500,0.2,1/16/2023,Series H,India,1700
Gramophone,Indore,Food,75,NULL,1/16/2023,Series B,India,17
ClearCo,Toronto,Finance,50,0.3,1/16/2023,Series C,Canada,698
Dunzo,Bengaluru,Food,NULL,0.03,1/16/2023,Unknown,India,382
Ignition,Sydney,Finance,NULL,0.1,1/16/2023,Series C,Australia,74
Rebel Foods,Mumbai,Food,NULL,0.02,1/16/2023,Unknown,India,548
Captain Fresh ,Bengaluru,Food,120,NULL,1/15/2023,Series C,India,126
Snappy,New York City,Marketing,100,0.3,1/15/2023,Series C,United States,104
BharatAgri,Mumbai,Food,40,0.43,1/15/2023,Series A,India,21
DeHaat,Patna,Food,NULL,0.05,1/15/2023,Series E,India,254
Black Shark,Shenzen,Hardware,900,NULL,1/13/2023,Unknown,China,NULL
Ola,Bengaluru,Transportation,200,NULL,1/13/2023,Series J,India,5000
Bonterra ,Austin,Other,140,0.1,1/13/2023,Unknown,United States,NULL
Vial,SF Bay Area,Healthcare,40,NULL,1/13/2023,Series B,United States,101
Arch Oncology,Brisbane,Healthcare,NULL,1,1/13/2023,Series C,United States,155
Carvana,Phoenix,Transportation,NULL,NULL,1/13/2023,Post-IPO,United States,1600
CoSchedule,Bismarck,Marketing,NULL,NULL,1/13/2023,Unknown,United States,2
GoCanvas,Washington D.C.,Other,NULL,NULL,1/13/2023,Acquired,United States,21
Jellyfish,Boston,Product,NULL,0.09,1/13/2023,Series C,United States,114
Lending Club,SF Bay Area,Finance,225,0.14,1/12/2023,Post-IPO,United States,392
SmartNews,Tokyo,Media,120,0.4,1/12/2023,Series F,United States,410
Skit.ai,Bengaluru,Support,115,NULL,1/12/2023,Series B,India,28
Pier,Sao Paulo,Finance,111,0.39,1/12/2023,Series B,Brazil,42
Blockchain.com,London,Crypto,110,0.28,1/12/2023,Series D,United Kingdom,490
Greenlight,Atlanta,Finance,104,0.21,1/12/2023,Series D,United States,556
Cashfree Payments,Bengaluru,Finance,100,NULL,1/12/2023,Series B,India,41
Mapbox,Washington D.C.,Data,64,NULL,1/12/2023,Unknown,United States,334
Definitive Healthcare,Boston,Healthcare,55,0.06,1/12/2023,Post-IPO,United States,NULL
Akili Labs,Baltimore,Healthcare,46,0.3,1/12/2023,Seed,United States,NULL
Career Karma,SF Bay Area,Education,22,NULL,1/12/2023,Series B,United States,51
Crypto.com,Singapore,Crypto,NULL,0.2,1/12/2023,Unknown,United States,NULL
Lattice,SF Bay Area,HR,NULL,0.15,1/12/2023,Series F,United States,328
Life360,SF Bay Area,Consumer,NULL,0.14,1/12/2023,Post-IPO,United States,158
Rock Content,Miami,Marketing,NULL,0.15,1/12/2023,Series B,United States,34
Flexport,SF Bay Area,Logistics,640,0.2,1/11/2023,Series E,United States,2400
Qualtrics,Salt Lake City,Other,270,0.05,1/11/2023,Post-IPO,United States,400
Verily,SF Bay Area,Healthcare,250,0.15,1/11/2023,NULL,United States,3500
Tipalti,SF Bay Area,Finance,123,0.11,1/11/2023,Series F,United States,565
Jumio,SF Bay Area,Security,100,0.06,1/11/2023,Private Equity,United States,205
CoinDCX,Mumbai,Crypto,80,NULL,1/11/2023,Series D,India,244
HashiCorp,SF Bay Area,Security,69,NULL,1/11/2023,Post-IPO,United States,349
Embark,Boston,Healthcare,41,NULL,1/11/2023,Series B,United States,94
Intrinsic,SF Bay Area,Other,40,0.2,1/11/2023,Acquired,United States,NULL
Citizen,New York City,Consumer,33,NULL,1/11/2023,Series C,United States,133
Carta,SF Bay Area,HR,NULL,0.1,1/11/2023,Series G,United States,1100
Limeade,Seattle,HR,NULL,0.15,1/11/2023,Series C,United States,33
Oyster,Charlotte,HR,NULL,NULL,1/11/2023,Series C,United States,224
Paddle,London,Finance,NULL,0.08,1/11/2023,Series D,United Kingdom,293
Coinbase,SF Bay Area,Crypto,950,0.2,1/10/2023,Post-IPO,United States,549
Informatica,SF Bay Area,Data,450,0.07,1/10/2023,Post-IPO,United States,NULL
Blend,SF Bay Area,Finance,340,0.28,1/10/2023,Post-IPO,United States,665
Till Payments,Sydney,Finance,120,NULL,1/10/2023,Series C,Australia,95
ConsenSys,New York City,Crypto,100,0.11,1/10/2023,Series D,United States,726
ForeScout,SF Bay Area,Security,100,0.1,1/10/2023,Post-IPO,United States,125
Thinkific,Vancouver,Education,76,0.19,1/10/2023,Post-IPO,Canada,22
LEAD,Mumbai,Education,60,NULL,1/10/2023,Series E,India,190
Parler,Nashville,Consumer,60,0.75,1/10/2023,Series B,United States,36
GoBolt,Toronto,Logistics,55,0.05,1/10/2023,Series C,Canada,178
Relevel,Bengaluru,HR,40,0.2,1/10/2023,NULL,India,NULL
StreamElements,Tel Aviv,Media,40,0.2,1/10/2023,Series B,Israel,111
100 Thieves,Los Angeles,Retail,NULL,NULL,1/10/2023,Series C,United States,120
Beamery,London,HR,NULL,0.12,1/10/2023,Series D,United States,223
Cart.com,Austin,Retail,NULL,NULL,1/10/2023,Unknown,United States,383
Citrix,Miami,Infrastructure,NULL,0.15,1/10/2023,Acquired,United States,20
Esper,Seattle,Other,NULL,0.21,1/10/2023,Series A,United States,10
WHOOP,Boston,Fitness,NULL,0.04,1/10/2023,Series F,United States,404
Fate Therapeutics,San Diego,Healthcare,315,0.57,1/9/2023,Post-IPO,United States,1200
Century Therapeutics,Philadelphia,Healthcare,NULL,NULL,1/9/2023,Post-IPO,United States,560
Editas Medicine,Boston,Healthcare,NULL,0.2,1/9/2023,Post-IPO,United States,656
Scale AI,SF Bay Area,Data,NULL,0.2,1/9/2023,Series E,United States,602
Minute Media,London,Media,50,0.1,1/8/2023,Series I,United Kingdom,160
WalkMe,SF Bay Area,Other,43,0.03,1/8/2023,Post-IPO,United States,307
Huobi,Beijing,Crypto,275,0.2,1/6/2023,Unknown,China,2
Carbon Health,SF Bay Area,Healthcare,200,NULL,1/6/2023,Series D,United States,522
Bounce,Bengaluru,Transportation,40,0.05,1/6/2023,Series D,India,214
Aware,Columbus,Security,NULL,NULL,1/6/2023,Series C,United States,88
CareerArc,Los Angeles,HR,NULL,NULL,1/6/2023,Private Equity,United States,30
CreateMe,SF Bay Area,Manufacturing,NULL,NULL,1/6/2023,Unknown,United States,NULL
Lantern,Grand Rapids,Retail,NULL,1,1/6/2023,Seed,United States,40
Mojo Vision,SF Bay Area,Hardware,NULL,0.75,1/6/2023,Series B,United States,204
SuperRare,Wilmington,Crypto,NULL,0.3,1/6/2023,Series A,United States,9
Cue,San Diego,Healthcare,388,NULL,1/5/2023,Post-IPO,United States,999
SoundHound,SF Bay Area,Other,200,0.5,1/5/2023,Post-IPO,United States,326
Socure,Reno,Finance,104,0.19,1/5/2023,Series E,United States,646
Genesis,New York City,Crypto,60,0.3,1/5/2023,Series A,United States,NULL
Moglix,Singapore,Retail,40,0.02,1/5/2023,Series F,Singapore,472
Twitter,SF Bay Area,Consumer,40,NULL,1/5/2023,Post-IPO,United States,12900
Everlane,SF Bay Area,Retail,30,0.17,1/5/2023,Unknown,United States,176
Pecan AI,Tel Aviv,Data,30,0.25,1/5/2023,Series C,Israel,116
Personetics,New York City,Support,30,0.08,1/5/2023,Private Equity,United States,178
Twine Solutions ,Tel Aviv,Hardware,30,0.33,1/5/2023,Unknown,Israel,50
UpScalio,Gurugram,Retail,25,0.15,1/5/2023,Series B,India,62
Attentive,New York City,Marketing,NULL,0.15,1/5/2023,Series E,United States,863
Compass,New York City,Real Estate,NULL,NULL,1/5/2023,Post-IPO,United States,1600
Megaport,Brisbane,Infrastructure,NULL,NULL,1/5/2023,Post-IPO,Australia,98
Stitch Fix,SF Bay Area,Retail,NULL,0.2,1/5/2023,Post-IPO,United States,79
TCR2,Boston,Healthcare,NULL,0.4,1/5/2023,Post-IPO,United States,173
Amazon,Seattle,Retail,8000,0.02,1/4/2023,Post-IPO,United States,108
Salesforce,SF Bay Area,Sales,8000,0.1,1/4/2023,Post-IPO,United States,65
Astronomer,Cincinnati,Data,76,0.2,1/4/2023,Series C,United States,282
Kaltura,New York City,Media,75,0.11,1/4/2023,Post-IPO,United States,166
Augury,New York City,Manufacturing,20,0.05,1/4/2023,Series E,United States,274
Butterfly Network,Boston,Healthcare,NULL,0.25,1/4/2023,Post-IPO,United States,530
Vimeo,New York City,Consumer,NULL,0.11,1/4/2023,Post-IPO,United States,450
Wyre,SF Bay Area,Crypto,NULL,1,1/4/2023,Unknown,United States,29
Pegasystems,Boston,HR,245,0.04,1/3/2023,Post-IPO,United States,NULL
Uniphore,SF Bay Area,Support,76,0.1,1/3/2023,Series E,United States,620
Harappa,New Delhi,Education,60,0.3,1/3/2023,Acquired,India,NULL
ByteDance,Shanghai,Consumer,NULL,0.1,1/3/2023,Unknown,China,9400
Amdocs,St. Louis,Support,700,0.03,1/2/2023,Post-IPO,United States,NULL
Bilibili,Shanghai,Media,NULL,0.3,12/27/2022,Post-IPO,China,3700
Octopus Network,Beau Vallon,Crypto,NULL,0.4,12/27/2022,Series A,Seychelles,8
PayU,Amsterdam,Finance,150,0.06,12/26/2022,Acquired,Netherlands,NULL
Element,London,Other,NULL,0.15,12/25/2022,Series B,United Kingdom,96
Willow,Sydney,Real Estate,99,0.22,12/23/2022,Unknown,Australia,NULL
Back Market,Paris,Retail,93,0.13,12/23/2022,Series E,France,1000
Zoopla,London,Real Estate,50,NULL,12/23/2022,Series C,United Kingdom,25
Qualcomm,San Diego,Hardware,153,NULL,12/22/2022,Post-IPO,United States,NULL
TuSimple,San Diego,Transportation,350,0.25,12/21/2022,Post-IPO,United States,648
Lendis,Berlin,Other,NULL,0.5,12/21/2022,Series A,Germany,90
Chope,Singapore,Food,65,0.24,12/20/2022,Series E,Singapore,50
Briza,Toronto,Finance,26,0.4,12/20/2022,Series A,Canada,10
StreetBees,London,Data,NULL,NULL,12/20/2022,Unknown,United Kingdom,63
Zhihu,Beijing,Consumer,NULL,0.1,12/20/2022,Series F,China,892
Homebot,Denver,Real Estate,18,0.13,12/19/2022,Acquired,United States,4
Health IQ,SF Bay Area,Healthcare,NULL,NULL,12/19/2022,Series D,United States,136
Xiaomi,Beijing,Consumer,NULL,NULL,12/19/2022,Post-IPO,United States,7400
YourGrocer,Melbourne,Food,NULL,1,12/19/2022,Unknown,Australia,2
Tomorrow,Hamburg,Finance,30,0.25,12/16/2022,Unknown,Germany,29
Revelate,Montreal,Data,24,0.3,12/16/2022,Series A,Canada,26
E Inc.,Toronto,Transportation,NULL,NULL,12/16/2022,Post-IPO,Canada,NULL
Autograph,Los Angeles,Crypto,NULL,NULL,12/16/2022,Series B,United States,205
Improbable,London,Other,NULL,0.1,12/16/2022,Unknown,United Kingdom,704
Modern Treasury,SF Bay Area,Finance,NULL,0.18,12/16/2022,Series C,United States,183
Reach,Calgary,Retail,NULL,0.12,12/16/2022,Series A,Canada,30
SonderMind,Denver,Healthcare,NULL,0.15,12/16/2022,Series C,United States,183
BigCommerce,Austin,Retail,180,0.13,12/15/2022,Post-IPO,United States,224
Freshworks,SF Bay Area,Support,90,0.02,12/15/2022,Post-IPO,United States,484
LeafLink,New York City,Other,80,0.31,12/15/2022,Series C,United States,379
Workmotion,Berlin,HR,60,0.2,12/15/2022,Series B,United States,76
Apollo,SF Bay Area,Product,NULL,0.15,12/15/2022,Series D,United States,183
JD.ID,Jakarta,Retail,200,0.3,12/14/2022,Post-IPO,Indonesia,5100
GoStudent,Vienna,Education,100,NULL,12/14/2022,Series D,Austria,686
Quanergy Systems,SF Bay Area,Transportation,72,NULL,12/14/2022,Post-IPO,United States,175
Headspace,Los Angeles,Healthcare,50,0.04,12/14/2022,Unknown,United States,215
ChowNow,Los Angeles,Food,40,0.1,12/14/2022,Series C,United States,64
Landing,Birmingham,Real Estate,NULL,NULL,12/14/2022,Series C,United States,347
Thumbtack,SF Bay Area,Consumer,160,0.14,12/13/2022,Series I,United States,698
Edgio,Phoenix,Infrastructure,95,0.1,12/13/2022,Post-IPO,United States,462
Komodo Health,SF Bay Area,Healthcare,78,0.09,12/13/2022,Series E,United States,514
Viant,Los Angeles,Marketing,46,0.13,12/13/2022,Post-IPO,United States,NULL
TaxBit,Salt Lake City,Crypto,NULL,NULL,12/13/2022,Series B,United States,235
Pluralsight,Salt Lake City,Education,400,0.2,12/12/2022,Acquired,United States,192
Freshly,Phoenix,Food,329,NULL,12/12/2022,Acquired,United States,107
Balto,St. Louis,Sales,35,NULL,12/12/2022,Series B,United States,51
Caribou,Washington D.C.,Finance,NULL,NULL,12/12/2022,Series C,United States,189
Outschool,SF Bay Area,Education,43,0.25,12/10/2022,Series D,United States,240
Xentral,Munich,Product,20,0.1,12/10/2022,Series B,Germany,94
Autobooks,Detroit,Finance,NULL,NULL,12/10/2022,Series C,United States,97
Convene,New York City,Real Estate,NULL,NULL,12/10/2022,Unknown,United States,281
PharmEasy,Mumbai,Healthcare,NULL,NULL,12/10/2022,Unknown,India,1600
Playtika,Tel Aviv,Consumer,600,0.15,12/9/2022,Post-IPO,Israel,NULL
Share Now,Berlin,Transportation,150,0.36,12/9/2022,Acquired,Germany,NULL
Alice,Sao Paulo,Healthcare,113,0.16,12/9/2022,Series C,Brazil,174
Primer,London,Finance,85,0.33,12/9/2022,Series B,United Kingdom,73
OneFootball,Berlin,Marketing,62,0.115,12/9/2022,Series D,Germany,442
C2FO,Kansas City,Finance,20,0.02,12/9/2022,Series H,United States,537
Brodmann17,Tel Aviv,Other,NULL,1,12/9/2022,Series A,Israel,25
Digital Surge,Brisbane,Crypto,NULL,1,12/9/2022,Unknown,Australia,NULL
N-able Technologies,Raleigh,Other,NULL,NULL,12/9/2022,Post-IPO,United States,225
ZenLedger,Seattle,Crypto,NULL,0.1,12/9/2022,Series B,United States,25
Airtable,SF Bay Area,Product,254,0.2,12/8/2022,Series F,United States,1400
Swiggy,Bengaluru,Food,250,0.03,12/8/2022,Unknown,India,3600
Glints,Singapore,HR,198,0.18,12/8/2022,Series D,Singapore,82
Buser,Sao Paulo,Transportation,160,0.3,12/8/2022,Series C,Brazil,138
BlackLine,Los Angeles,Finance,95,0.05,12/8/2022,Private Equity,United States,220
Chrono24,Karlsruhe,Retail,80,NULL,12/8/2022,Series C,Germany,205
Otonomo,Tel Aviv,Transportation,80,0.5,12/8/2022,Post-IPO,Israel,231
TechTarget,Boston,Marketing,60,0.05,12/8/2022,Post-IPO,United States,115
Inscripta,Boulder,Healthcare,43,NULL,12/8/2022,Series E,United States,459
CyCognito,SF Bay Area,Security,30,0.15,12/8/2022,Series C,United States,153
Armis,SF Bay Area,Security,25,0.04,12/8/2022,Private Equity,United States,537
Bakkt,Atlanta,Crypto,NULL,0.15,12/8/2022,Post-IPO,United States,932
Blue Apron,New York City,Food,NULL,0.1,12/8/2022,Post-IPO,United States,352
FireHydrant,New York City,Infrastructure,NULL,NULL,12/8/2022,Series B,United States,32
Lenovo,Raleigh,Hardware,NULL,NULL,12/8/2022,Post-IPO,United States,850
Nerdy,St. Louis,Education,NULL,0.17,12/8/2022,Post-IPO,United States,150
Vanta,SF Bay Area,Security,NULL,0.14,12/8/2022,Series B,United States,203
Vedantu,Bengaluru,Education,385,NULL,12/7/2022,Series E,India,292
Loft,Sao Paulo,Real Estate,312,0.12,12/7/2022,Unknown,Brazil,788
Plaid,SF Bay Area,Finance,260,0.2,12/7/2022,Series D,United States,734
Motive,SF Bay Area,Transportation,237,0.06,12/7/2022,Series F,United States,567
Recur Forever,Miami,Crypto,235,NULL,12/7/2022,Series A,United States,55
Relativity,Chicago,Legal,150,0.1,12/7/2022,Private Equity,United States,125
Voi,Stockholm,Transportation,130,0.13,12/7/2022,Series D,United States,515
Integral Ad Science,New York City,Marketing,120,0.13,12/7/2022,Acquired,United States,116
Houzz,SF Bay Area,Consumer,95,0.08,12/7/2022,Series E,United States,613
Grover,Berlin,Retail,40,0.1,12/7/2022,Unknown,United States,2300
Lev,New York City,Real Estate,30,0.3,12/7/2022,Series B,United States,114
Lithic,New York City,Finance,27,0.18,12/7/2022,Series C,United States,115
CircleCI,SF Bay Area,Product,NULL,0.17,12/7/2022,Series F,United States,315
Sayurbox,Jakarta,Food,NULL,0.05,12/7/2022,Series C,Indonesia,139
Zywave,Milwaukee,Finance,NULL,NULL,12/7/2022,Acquired,United States,NULL
Doma,SF Bay Area,Finance,515,0.4,12/6/2022,Post-IPO,United States,679
Intel,SF Bay Area,Hardware,201,NULL,12/6/2022,Post-IPO,United States,12
BuzzFeed,New York City,Media,180,0.12,12/6/2022,Post-IPO,United States,696
Weedmaps,Los Angeles,Other,175,0.25,12/6/2022,Acquired,United States,NULL
Adobe,SF Bay Area,Marketing,100,NULL,12/6/2022,Post-IPO,United States,2
Chipper Cash,SF Bay Area,Finance,50,0.125,12/6/2022,Series C,United States,302
Stash,New York City,Finance,32,0.08,12/6/2022,Unknown,United States,480
Perimeter 81,Tel Aviv,Security,20,0.08,12/6/2022,Series C,Israel,165
Koinly,London,Crypto,16,0.14,12/6/2022,Unknown,United Kingdom,NULL
Bridgit,Waterloo,Construction,13,0.13,12/6/2022,Series B,Canada,36
Filevine,Salt Lake City,Legal,NULL,NULL,12/6/2022,Series D,United States,226
Moove,Lagos,Transportation,NULL,NULL,12/6/2022,Unknown,Nigeria,630
Nextiva,Phoenix,Other,NULL,0.17,12/6/2022,Private Equity,United States,200
OneStudyTeam,Boston,Healthcare,NULL,0.25,12/6/2022,Series D,United States,479
Zuora,SF Bay Area,Finance,NULL,0.11,12/6/2022,Post-IPO,United States,647
Swyftx,Brisbane,Crypto,90,0.4,12/5/2022,Unknown,Australia,NULL
Aqua Security,Boston,Security,65,0.1,12/5/2022,Series E,United States,265
DataRails,Tel Aviv,Finance,30,0.18,12/5/2022,Series B,Israel,103
Elemy,SF Bay Area,Healthcare,NULL,NULL,12/5/2022,Series B,United States,323
Route,Lehi,Retail,NULL,NULL,12/5/2022,Unknown,United States,481
Thinkific,Vancouver,Education,NULL,NULL,12/5/2022,Post-IPO,Canada,22
OYO,Gurugram,Travel,600,NULL,12/3/2022,Series F,India,4000
HealthifyMe,Bengaluru,Fitness,150,NULL,12/3/2022,Series C,India,100
Bybit,Singapore,Crypto,NULL,0.3,12/3/2022,Unknown,Singapore,NULL
Cognyte,Tel Aviv,Security,100,0.05,12/2/2022,Unknown,Israel,NULL
ShareChat,Bengaluru,Consumer,100,NULL,12/2/2022,Unknown,India,1700
Polly,Burlington,Finance,47,0.15,12/2/2022,Series C,United States,184
Homebound,SF Bay Area,Real Estate,NULL,NULL,12/2/2022,Unknown,United States,128
Lora DiCarlo,Bend,Consumer,NULL,1,12/2/2022,Unknown,United States,9
Carousell,Singapore,Retail,110,0.1,12/1/2022,Private Equity,Singapore,372
Bizzabo,New York City,Marketing,100,0.37,12/1/2022,Series E,United States,194
BloomTech,SF Bay Area,Education,88,0.5,12/1/2022,Unknown,United States,NULL
Netlify,SF Bay Area,Product,48,0.16,12/1/2022,Series D,United States,212
Springbig,Miami,Sales,37,0.23,12/1/2022,Post-IPO,United States,32
Podium,Lehi,Support,NULL,0.12,12/1/2022,Series D,United States,419
SQZ Biotech,Boston,Healthcare,NULL,0.6,12/1/2022,Post-IPO,United States,229
Strava,SF Bay Area,Fitness,NULL,0.14,12/1/2022,Series F,United States,151
Synlogic,Boston,Healthcare,NULL,0.25,12/1/2022,Post-IPO,United States,321
Yapily,London,Finance,NULL,NULL,12/1/2022,Series A,United Kingdom,69
DoorDash,SF Bay Area,Food,1250,0.06,11/30/2022,Post-IPO,United States,2500
Kraken,SF Bay Area,Crypto,1100,0.3,11/30/2022,Unknown,United States,134
Happy Money,Los Angeles,Finance,158,0.34,11/30/2022,Series D,United States,191
Ula,Jakarta,Retail,134,0.23,11/30/2022,Series B,Indonesia,140
Wonder,New York City,Food,130,0.07,11/30/2022,Series B,United States,850
StudySmarter,Berlin,Education,70,NULL,11/30/2022,Series A,Germany,64
Grin,Sacramento,Marketing,60,0.13,11/30/2022,Series B,United States,145
Ualá,Buenos Aires,Finance,53,0.03,11/30/2022,Series D,Argentina,544
Teachmint,Bengaluru,Education,45,0.05,11/30/2022,Series B,India,118
Etermax,Buenos Aires,Other,40,NULL,11/30/2022,Unknown,Argentina,NULL
Thread,London,Retail,30,0.5,11/30/2022,Acquired,United Kingdom,40
Elastic,SF Bay Area,Data,NULL,0.13,11/30/2022,Post-IPO,United States,162
Motional,Boston,Transportation,NULL,NULL,11/30/2022,Unknown,United States,NULL
Pinterest,SF Bay Area,Consumer,NULL,NULL,11/30/2022,Post-IPO,United States,1500
Sana,Seattle,Healthcare,NULL,0.15,11/30/2022,Series A,United States,700
Venafi,Salt Lake City,Security,NULL,NULL,11/30/2022,Acquired,United States,167
Bitso,Mexico City,Crypto,100,NULL,11/29/2022,Series C,Mexico,378
Lyst,London,Retail,50,0.25,11/29/2022,Unknown,United Kingdom,144
CoinJar,Melbourne,Crypto,10,0.2,11/29/2022,Unknown,Australia,1
Bitfront,SF Bay Area,Crypto,NULL,1,11/29/2022,Unknown,United States,NULL
Codexis,SF Bay Area,Healthcare,NULL,0.18,11/29/2022,Post-IPO,United States,162
Firework,SF Bay Area,Retail,NULL,0.1,11/29/2022,Series B,United States,269
Lazerpay,Lagos,Crypto,NULL,NULL,11/29/2022,Unknown,Nigeria,NULL
MessageBird,Amsterdam,Other,NULL,0.31,11/29/2022,Series C,Netherlands,1100
Plerk,Guadalajara,Finance,NULL,0.4,11/29/2022,Series A,Mexico,13
Proton.ai,Boston,Sales,NULL,NULL,11/29/2022,Series A,United States,20
Infarm,Berlin,Other,500,0.5,11/28/2022,Series D,Germany,604
Wildlife Studios,Sao Paulo,Consumer,300,0.2,11/28/2022,Unknown,Brazil,260
Hirect,Bengaluru,Recruiting,200,0.4,11/28/2022,Series A,India,NULL
ApplyBoard,Waterloo,Education,90,0.06,11/28/2022,Series D,Canada,483
Ajaib,Jakarta,Finance,67,0.08,11/28/2022,Unknown,Indonesia,245
Candy Digital,New York City,Crypto,33,0.33,11/28/2022,Series A,United States,100
ResearchGate,Berlin,Other,25,0.1,11/28/2022,Series D,Germany,87
BlockFi,New York City,Crypto,NULL,1,11/28/2022,Series E,United States,1000
FutureLearn,London,Education,NULL,NULL,11/28/2022,Unknown,United Kingdom,50
Inspectify,Seattle,Real Estate,NULL,NULL,11/28/2022,Series A,United States,11
Ledn,Toronto,Crypto,NULL,NULL,11/28/2022,Series B,Canada,103
NCX,SF Bay Area,Energy,NULL,0.4,11/28/2022,Series B,United States,78
Change Invest,Amsterdam,Finance,NULL,0.24,11/27/2022,Unknown,Netherlands,22
Zilch,London,Finance,NULL,NULL,11/26/2022,Unknown,United Kingdom,389
VerSe Innovation,Bengaluru,Media,150,0.05,11/25/2022,Series J,India,1700
Carwow,London,Transportation,70,0.2,11/25/2022,Unknown,United Kingdom,157
Vendease,Lagos,Food,27,0.09,11/25/2022,Series A,Nigeria,43
Lemon,Buenos Aires,Crypto,100,0.38,11/24/2022,Series A,Argentina,17
Quidax,Lagos,Crypto,20,0.2,11/24/2022,Unknown,Nigeria,3
Menulog,Sydney,Food,NULL,NULL,11/24/2022,Acquired,Australia,NULL
Utopia Music,Zug,Media,NULL,NULL,11/24/2022,Series B,Switzerland,NULL
Assure,Salt Lake City,Finance,NULL,1,11/23/2022,Seed,United States,2
GoodGood,Toronto,Retail,NULL,1,11/23/2022,Seed,Canada,6
SWVL,Cairo,Transportation,NULL,0.5,11/23/2022,Post-IPO,Egypt,264
Western Digital,SF Bay Area,Hardware,251,NULL,11/22/2022,Post-IPO,United States,900
SIRCLO,Jakarta,Retail,160,0.08,11/22/2022,Series B,Indonesia,92
Trax,Singapore,Retail,80,0.08,11/22/2022,Series E,Singapore,1000
Flash Coffee,Singapore,Food,NULL,NULL,11/22/2022,Series B,Singapore,57
Natera,SF Bay Area,Healthcare,NULL,NULL,11/22/2022,Post-IPO,United States,809
Rapyd,Tel Aviv,Finance,NULL,NULL,11/22/2022,Unknown,Israel,770
Jumia,Lagos,Retail,900,0.2,11/21/2022,Post-IPO,Nigeria,1200
Kitopi,Dubai,Food,93,0.1,11/21/2022,Series C,United States,804
Devo,Boston,Security,NULL,0.15,11/21/2022,Series F,United States,481
GloriFi,Dallas,Finance,NULL,1,11/21/2022,Unknown,United States,NULL
Zomato,Gurugram,Food,100,0.04,11/19/2022,Series J,India,914
Carvana,Phoenix,Transportation,1500,0.08,11/18/2022,Post-IPO,United States,1600
Nuro,SF Bay Area,Transportation,300,0.2,11/18/2022,Series D,United States,2100
Synthego,SF Bay Area,Healthcare,105,0.2,11/18/2022,Series E,United States,459
Splyt,London,Transportation,57,NULL,11/18/2022,Series B,United Kingdom,34
Capitolis,New York City,Finance,NULL,0.25,11/18/2022,Series D,United States,281
Kavak,Sao Paulo,Transportation,NULL,NULL,11/18/2022,Series E,Brazil,1600
Metaplex,Chicago,Crypto,NULL,NULL,11/18/2022,Unknown,United States,NULL
Ruangguru,Jakarta,Education,NULL,NULL,11/18/2022,Unknown,Indonesia,205
StoryBlocks,Washington D.C.,Media,NULL,0.25,11/18/2022,Acquired,United States,18
Unchained Capital,Austin,Crypto,NULL,0.15,11/18/2022,Series A,United States,33
Roku,SF Bay Area,Media,200,0.07,11/17/2022,Post-IPO,United States,208
Orchard,New York City,Real Estate,180,NULL,11/17/2022,Series D,United States,472
Homepoint,Phoenix,Real Estate,113,NULL,11/17/2022,Post-IPO,United States,NULL
Juni,Gothenburg,Finance,72,0.33,11/17/2022,Unknown,Sweden,281
Chili Piper,New York City,Sales,58,NULL,11/17/2022,Series B,United States,54
Capitolis,New York City,Finance,37,0.25,11/17/2022,Series D,United States,281
TealBook,Toronto,Other,34,0.19,11/17/2022,Series B,Canada,73
Koho,Toronto,Finance,15,0.04,11/17/2022,Series D,Canada,278
&Open,Dublin,Marketing,9,0.09,11/17/2022,Series A,Ireland,35
Kandji,San Diego,Other,NULL,0.17,11/17/2022,Series C,United States,188
Morning Brew,New York City,Media,NULL,0.14,11/17/2022,Acquired,United States,NULL
Symend,Calgary,Other,NULL,0.13,11/17/2022,Series C,Canada,148
Amazon,Seattle,Retail,10000,0.03,11/16/2022,Post-IPO,United States,108
Cisco,SF Bay Area,Infrastructure,4100,0.05,11/16/2022,Post-IPO,United States,2
Twiga,Nairobi,Food,211,0.21,11/16/2022,Series C,Kenya,157
Wayflyer,Dublin,Marketing,200,0.4,11/16/2022,Unknown,Ireland,889
SimilarWeb,New York City,Other,120,0.1,11/16/2022,Post-IPO,United States,235
Salsify,Boston,Retail,90,0.11,11/16/2022,Series F,United States,452
Lokalise,Dover,Other,76,0.23,11/16/2022,Series B,United States,56
Yotpo,New York City,Marketing,70,0.09,11/16/2022,Unknown,United States,436
Pear Therapeutics,Boston,Healthcare,59,0.22,11/16/2022,Post-IPO,United States,409
D2L,Waterloo,Education,NULL,0.05,11/16/2022,Post-IPO,Canada,168
Dance,Berlin,Transportation,NULL,0.16,11/16/2022,Unknown,Germany,63
Homeward,Austin,Real Estate,NULL,0.25,11/16/2022,Unknown,United States,501
Hopin,London,Other,NULL,0.17,11/16/2022,Series D,United Kingdom,1000
Infogrid,London,Other,NULL,NULL,11/16/2022,Series A,United Kingdom,15
Kite,SF Bay Area,Product,NULL,1,11/16/2022,Series A,United States,21
UiPath,New York City,Data,241,0.06,11/15/2022,Post-IPO,United States,2000
Asana,SF Bay Area,Other,180,0.09,11/15/2022,Post-IPO,United States,453
OwnBackup,New York City,Security,170,0.17,11/15/2022,Series E,United States,507
Deliveroo Australia,Melbourne,Food,120,1,11/15/2022,Post-IPO,Australia,1700
Productboard,SF Bay Area,Product,100,0.2,11/15/2022,Series D,United States,NULL
Properly,Toronto,Real Estate,71,NULL,11/15/2022,Series B,Canada,154
Protocol,SF Bay Area,Media,60,1,11/15/2022,Acquired,United States,NULL
Jimdo,Hamburg,Other,50,0.16,11/15/2022,Unknown,Germany,28
The Zebra,Austin,Finance,50,NULL,11/15/2022,Series D,United States,256
Viber,Luxembourg,Consumer,45,0.08,11/15/2022,Acquired,Luxembourg,NULL
CaptivateIQ,SF Bay Area,Sales,31,0.1,11/15/2022,Series C,United States,164
Apollo Insurance,Vancouver,Finance,NULL,0.25,11/15/2022,Series B,Canada,11
Nirvana Money,Miami,Finance,NULL,1,11/15/2022,Unknown,United States,NULL
Oatly,Malmö,Food,NULL,NULL,11/15/2022,Post-IPO,Sweden,441
OfferUp,Seattle,Retail,NULL,0.19,11/15/2022,Unknown,United States,381
Outside,Boulder,Media,NULL,0.12,11/15/2022,Series B,United States,174
Rubicon Technologies,Lexington,Other,NULL,0.11,11/15/2022,Post-IPO,United States,382
Tencent,Shenzen,Consumer,NULL,NULL,11/15/2022,Post-IPO,China,12600
Typeform,Barcelona,Marketing,NULL,NULL,11/15/2022,Series C,Spain,187
Whispir,Melbourne,Other,NULL,0.3,11/15/2022,Post-IPO,Australia,68
Illumina,San Diego,Healthcare,500,0.05,11/14/2022,Post-IPO,United States,28
Sema4,Stamford,Healthcare,500,NULL,11/14/2022,Post-IPO,United States,791
iFit,Logan,Fitness,300,0.2,11/14/2022,Private Equity,United States,200
Ribbon,New York City,Real Estate,170,0.85,11/14/2022,Series C,United States,405
Pipedrive,Tallinn,Sales,143,0.15,11/14/2022,Private Equity,Estonia,90
Intercom,SF Bay Area,Support,124,0.13,11/14/2022,Series D,United States,240
Science 37 ,Los Angeles,Healthcare,90,NULL,11/14/2022,Post-IPO,United States,347
Pear Therapeutics,Boston,Healthcare,59,0.22,11/14/2022,Post-IPO,United States,409
Cardlytics,Atlanta,Marketing,51,NULL,11/14/2022,Post-IPO,United States,212
Cloudinary,SF Bay Area,Media,40,0.08,11/14/2022,Unknown,United States,100
Nestcoin,Lagos,Crypto,30,NULL,11/14/2022,Seed,Nigeria,6
Gokada,Lagos,Transportation,20,NULL,11/14/2022,Unknown,Nigeria,12
Shopee,Jakarta,Food,NULL,NULL,11/14/2022,Unknown,Indonesia,NULL
Tricida,SF Bay Area,Healthcare,NULL,0.57,11/14/2022,Post-IPO,United States,624
Veev,SF Bay Area,Real Estate,100,0.3,11/11/2022,Series D,United States,597
Forto,Berlin,Logistics,60,0.08,11/11/2022,Series D,United States,593
Chipax,Santiago,Finance,NULL,NULL,11/11/2022,Seed,Chile,2
Juniper,Atlanta,Marketing,NULL,NULL,11/11/2022,Acquired,United States,NULL
Offerpad,Phoenix,Real Estate,NULL,0.07,11/11/2022,Post-IPO,United States,355
GoTo Group,Jakarta,Transportation,1300,0.12,11/10/2022,Post-IPO,Indonesia,1300
Juul,SF Bay Area,,400,0.3,11/10/2022,Unknown,United States,1500
Blend,SF Bay Area,Finance,100,0.06,11/10/2022,Post-IPO,United States,665
InfluxData,SF Bay Area,Data,65,0.27,11/10/2022,Series D,United States,119
Coinbase,SF Bay Area,Crypto,60,NULL,11/10/2022,Post-IPO,United States,549
SoundHound,SF Bay Area,Other,45,0.1,11/10/2022,Post-IPO,United States,326
Wistia,Boston,Marketing,40,NULL,11/10/2022,Unknown,United States,18
Ocavu,Lehi,Crypto,20,0.48,11/10/2022,Series A,United States,11
Avast,Phoenix,Security,NULL,0.25,11/10/2022,Unknown,United States,NULL
Reforge,SF Bay Area,Education,NULL,NULL,11/10/2022,Series B,United States,81
SendCloud,Eindhoven,Logistics,NULL,0.1,11/10/2022,Series C,United States,200
Voly,Sydney,Food,NULL,NULL,11/10/2022,Seed,Australia,13
Wavely,SF Bay Area,HR,NULL,1,11/10/2022,Unknown,United States,NULL
ZenBusiness,Austin,Other,NULL,NULL,11/10/2022,Series C,United States,277
Meta,SF Bay Area,Consumer,11000,0.13,11/9/2022,Post-IPO,United States,26000
Redfin,Seattle,Real Estate,862,0.13,11/9/2022,Post-IPO,United States,320
Flyhomes,Seattle,Real Estate,300,0.4,11/9/2022,Series C,United States,310
AvantStay,Los Angeles,Travel,144,0.22,11/9/2022,Private Equity,United States,686
Root Insurance,Columbus,Finance,137,0.2,11/9/2022,Post-IPO,United States,527
Liftoff,SF Bay Area,Marketing,130,0.15,11/9/2022,Acquired,United States,6
Cameo,Chicago,Consumer,80,NULL,11/9/2022,Unknown,United States,165
Plum,Bengaluru,Healthcare,36,0.1,11/9/2022,Series A,India,20
Kabam,SF Bay Area,Consumer,35,0.07,11/9/2022,Acquired,United States,244
HighRadius,Bengaluru,Finance,25,NULL,11/9/2022,Series C,India,475
Namogoo,Tel Aviv,Marketing,25,0.15,11/9/2022,Series C,United States,69
Amobee,SF Bay Area,Marketing,NULL,NULL,11/9/2022,Acquired,United States,72
CloudFactory,Nairobi,Data,NULL,0.12,11/9/2022,Private Equity,Kenya,78
Coursera,SF Bay Area,Education,NULL,NULL,11/9/2022,Post-IPO,United States,458
Faze Medicines,Boston,Healthcare,NULL,1,11/9/2022,Series B,United States,81
RingCentral,SF Bay Area,Other,NULL,0.1,11/9/2022,Post-IPO,United States,44
Spotify,Stockholm,Media,NULL,NULL,11/9/2022,Post-IPO,Sweden,2100
EverBridge,Boston,Other,200,NULL,11/8/2022,Post-IPO,United States,476
Repertoire Immune Medicines,Boston,Healthcare,65,0.5,11/8/2022,Series B,United States,257
Astra,SF Bay Area,Aerospace,NULL,0.16,11/8/2022,Post-IPO,United States,300
Beat,Athens,Transportation,NULL,NULL,11/8/2022,Series B,Greece,6
NanoString,Seattle,Healthcare,NULL,0.1,11/8/2022,Post-IPO,United States,731
SADA,Los Angeles,Other,NULL,0.11,11/8/2022,Acquired,United States,NULL
Salesforce,SF Bay Area,Sales,1000,0.01,11/7/2022,Post-IPO,United States,65
Unacademy,Bengaluru,Education,350,0.1,11/7/2022,Series H,India,838
Zendesk,SF Bay Area,Support,350,0.05,11/7/2022,Acquired,United States,85
Dock,Sao Paulo,Finance,190,0.12,11/7/2022,Private Equity,Brazil,280
Code42,Minneapolis,Security,NULL,0.15,11/7/2022,Unknown,United States,137
Domino Data Lab,SF Bay Area,Data,NULL,0.25,11/7/2022,Series F,United States,223
Varonis,New York City,Security,110,0.05,11/6/2022,Post-IPO,United States,30
Brainly,Krakow,Education,25,NULL,11/6/2022,Series D,Poland,148
Practically,Hyderabad,Education,NULL,NULL,11/6/2022,Unknown,India,14
Twitter,SF Bay Area,Consumer,3700,0.5,11/4/2022,Post-IPO,United States,12900
Udaan,Bengaluru,Retail,350,NULL,11/4/2022,Unknown,India,1500
Planetly,Berlin,Other,200,1,11/4/2022,Acquired,Germany,5
KoinWorks,Jakarta,Finance,70,0.08,11/4/2022,Unknown,Indonesia,180
Exodus,Nebraska City,Crypto,59,0.22,11/4/2022,Unknown,United States,60
Benitago Group,New York City,Retail,NULL,0.14,11/4/2022,Series A,United States,380
Mythical Games,Los Angeles,Crypto,NULL,0.1,11/4/2022,Series C,United States,260
Stripe,SF Bay Area,Finance,1000,0.14,11/3/2022,Series H,United States,2300
Lyft,SF Bay Area,Transportation,700,0.13,11/3/2022,Post-IPO,United States,4900
LendingTree,Charlotte,Finance,200,NULL,11/3/2022,Post-IPO,United States,NULL
Pleo,Copenhagen,Finance,150,0.15,11/3/2022,Series C,United States,428
Delivery Hero,Berlin,Food,100,NULL,11/3/2022,Post-IPO,Germany,8300
Shippo,SF Bay Area,Logistics,60,0.2,11/3/2022,Series E,United States,154
Affirm,SF Bay Area,Finance,NULL,0.01,11/3/2022,Post-IPO,United States,1500
CloudKitchens,Los Angeles,Real Estate,NULL,NULL,11/3/2022,Unknown,United States,1300
LiveRamp,SF Bay Area,Marketing,NULL,0.1,11/3/2022,Post-IPO,United States,16
Provi,Chicago,Food,NULL,NULL,11/3/2022,Series C,United States,150
Rubius,Boston,Healthcare,NULL,0.82,11/3/2022,Post-IPO,United States,445
Snapdocs,SF Bay Area,Real Estate,NULL,0.15,11/3/2022,Series D,United States,253
Studio,SF Bay Area,Education,NULL,NULL,11/3/2022,Series B,United States,50
Opendoor,SF Bay Area,Real Estate,550,0.18,11/2/2022,Post-IPO,United States,1900
Chime,SF Bay Area,Finance,156,0.12,11/2/2022,Series G,United States,2300
Chargebee,SF Bay Area,Finance,142,0.1,11/2/2022,Series H,United States,468
Dapper Labs,Vancouver,Crypto,134,0.22,11/2/2022,Series D,United States,607
Checkmarx,Tel Aviv,Security,100,0.1,11/2/2022,Series C,Israel,92
Smava,Berlin,Finance,100,0.15,11/2/2022,Unknown,Germany,188
Iron Ox,SF Bay Area,Food,50,0.5,11/2/2022,Series C,United States,103
Digital Currency Gruop,Stamford,Crypto,10,0.13,11/2/2022,Unknown,United States,NULL
BitMEX,Non-U.S.,Crypto,NULL,0.3,11/2/2022,Seed,Seychelles,0
Signicat,Trondheim,Security,NULL,NULL,11/2/2022,Acquired,Norway,8
Argo AI,SF Bay Area,Transportation,259,NULL,11/1/2022,Unknown,United States,3600
Booking.com,Grand Rapids,Travel,226,NULL,11/1/2022,Acquired,United States,NULL
Oracle,SF Bay Area,Other,200,NULL,11/1/2022,Post-IPO,United States,NULL
Upstart,SF Bay Area,Finance,140,0.07,11/1/2022,Post-IPO,United States,144
Gem,SF Bay Area,Recruiting,100,0.33,11/1/2022,Series C,United States,148
Oda,Oslo,Food,70,0.18,11/1/2022,Unknown,Sweden,377
Oda,Oslo,Food,70,0.18,11/1/2022,Unknown,Norway,477
Oda,Oslo,Food,70,0.06,11/1/2022,Unknown,Norway,479
Hootsuite,Vancouver,Marketing,50,0.05,11/1/2022,Series C,Canada,300
Drop,Toronto,Marketing,24,NULL,11/1/2022,Series B,Canada,56
Tapps Games,Sao Paulo,Consumer,10,NULL,11/1/2022,Unknown,Brazil,NULL
Brightline,SF Bay Area,Healthcare,NULL,0.2,11/1/2022,Series C,United States,212
Help Scout,Boston,Support,NULL,NULL,11/1/2022,Series B,United States,28
Kry,Stockholm,Healthcare,300,0.1,10/31/2022,Series D,Sweden,568
Notarize,Boston,Legal,60,NULL,10/31/2022,Series D,United States,213
EquityZen,New York City,Finance,30,0.27,10/31/2022,Series B,United States,11
Equitybee,SF Bay Area,Finance,25,0.2,10/31/2022,Series B,United States,85
Dukaan,Bengaluru,Retail,23,NULL,10/31/2022,Series A,India,17
Amazon,Seattle,Retail,150,NULL,10/28/2022,Post-IPO,United States,108
Fifth Season,Pittsburgh,Food,100,1,10/28/2022,Series B,United States,35
Advata,Seattle,Healthcare,32,0.21,10/28/2022,NULL,United States,NULL
Springlane,Düsseldorf,Food,NULL,0.35,10/28/2022,Series C,Germany,11
RenoRun,Montreal,Construction,210,0.43,10/27/2022,Series B,Canada,163
Recharge,Los Angeles,Finance,84,0.17,10/27/2022,Series B,United States,277
Lattice,SF Bay Area,HR,13,NULL,10/27/2022,Series F,United States,328
GoNuts,Mumbai,Media,NULL,1,10/27/2022,Seed,India,NULL
Spreetail,Austin,Retail,NULL,NULL,10/27/2022,NULL,United States,NULL
MindBody,San Luis Obispo,Fitness,400,0.15,10/26/2022,Post-IPO,United States,114
Zillow,Seattle,Real Estate,300,0.05,10/26/2022,Post-IPO,United States,97
Cybereason,Boston,Security,200,0.17,10/26/2022,Series F,United States,750
Argo AI,Pittsburgh,Transportation,173,NULL,10/26/2022,Unknown,United States,3600
GoFundMe,SF Bay Area,Finance,94,0.12,10/26/2022,Series A,United States,NULL
Carbon,SF Bay Area,Hardware,NULL,NULL,10/26/2022,Series E,United States,683
Fundbox,SF Bay Area,Finance,150,0.42,10/25/2022,Series D,United States,553
Embroker,SF Bay Area,Finance,30,0.12,10/25/2022,Series C,United States,142
Vee,Tel Aviv,HR,17,0.5,10/25/2022,Seed,Israel,15
Callisto Media,SF Bay Area,Media,NULL,0.35,10/25/2022,Series D,United States,NULL
Convoy,Seattle,Logistics,NULL,NULL,10/25/2022,Series E,United States,1100
Philips,Amsterdam,Healthcare,4000,0.05,10/24/2022,Post-IPO,Netherlands,NULL
Cerebral,SF Bay Area,Healthcare,400,0.2,10/24/2022,Series C,United States,462
Snyk,Boston,Security,198,0.14,10/24/2022,Series F,United States,849
McMakler,Berlin,Real Estate,100,NULL,10/24/2022,Unknown,Germany,214
Unico,Sao Paulo,Other,50,0.04,10/24/2022,Series D,Brazil,336
OrCam,Jerusalem,Healthcare,62,0.16,10/23/2022,Unknown,Israel,86
Antidote Health,Tel Aviv,Healthcare,23,0.38,10/23/2022,Unknown,Israel,36
Khoros,Austin,Sales,120,0.1,10/21/2022,Private Equity,United States,138
F5,Seattle,Security,100,0.01,10/21/2022,Post-IPO,United States,NULL
Elinvar,Berlin,Finance,43,0.33,10/21/2022,Unknown,Germany,30
Synapsica,New Delhi,Healthcare,30,0.3,10/21/2022,Series A,India,4
Volta,SF Bay Area,Transportation,NULL,0.54,10/21/2022,Post-IPO,United States,575
Hotmart,Belo Horizonte,Marketing,227,0.12,10/20/2022,Series C,Brazil,127
Zeus Living,SF Bay Area,Real Estate,64,0.46,10/20/2022,Series C,United States,151
Loom,SF Bay Area,Product,23,0.11,10/20/2022,Series C,United States,203
Sales Boomerang,Baltimore,Sales,20,NULL,10/20/2022,Private Equity,United States,5
Arrival,London,Transportation,NULL,NULL,10/20/2022,Post-IPO,United Kingdom,629
Roofstock,SF Bay Area,Real Estate,NULL,0.2,10/20/2022,Series E,United States,365
Starry,Boston,Other,NULL,0.5,10/20/2022,Post-IPO,United States,260
Gopuff,Philadelphia,Food,250,NULL,10/19/2022,Series H,United States,3400
AtoB,SF Bay Area,Finance,32,0.3,10/19/2022,Series B,United States,177
InfoSum,London,Security,20,0.12,10/19/2022,Series B,United Kingdom,88
Clever Real Estate,St. Louis,Real Estate,NULL,NULL,10/19/2022,Series B,United States,13
Collibra,Brussels,Data,NULL,NULL,10/19/2022,Series G,Belgium,596
Side,SF Bay Area,Real Estate,NULL,NULL,10/19/2022,Unknown,United States,313
Faire,SF Bay Area,Retail,84,0.07,10/18/2022,Series G,United States,1700
Leafly,Seattle,Retail,56,0.21,10/18/2022,Post-IPO,United States,71
Ada Health,Berlin,Healthcare,50,NULL,10/17/2022,Series B,Germany,189
Tiendanube,Buenos Aires,Marketing,50,0.05,10/17/2022,Unknown,Argentina,NULL
Microsoft,Seattle,Other,NULL,NULL,10/17/2022,Post-IPO,United States,1
Nuri,Berlin,Crypto,NULL,1,10/17/2022,Series B,Germany,42
Flipboard,SF Bay Area,Media,24,0.21,10/16/2022,Unknown,United States,235
Huawei,Shenzen,Hardware,NULL,NULL,10/16/2022,Unknown,China,NULL
Clear Capital,Reno,Real Estate,378,0.27,10/14/2022,Unknown,United States,NULL
Beyond Meat,Los Angeles,Food,200,0.19,10/14/2022,Post-IPO,United States,122
Flux Systems,London,Finance,NULL,1,10/14/2022,Unknown,United Kingdom,9
Qin1,Noida,Education,NULL,1,10/14/2022,Seed,India,NULL
Salesforce,SF Bay Area,Sales,90,NULL,10/13/2022,Post-IPO,United States,65
Playdots,New York City,Consumer,65,1,10/13/2022,Acquired,United States,10
ExtraHop,Seattle,Security,NULL,NULL,10/13/2022,Series C,United States,61
Byju's,Bengaluru,Education,2500,0.05,10/12/2022,Private Equity,India,5500
6sense,SF Bay Area,Sales,150,0.1,10/12/2022,Series E,United States,426
Sinch,Stockholm,Other,150,NULL,10/12/2022,Post-IPO,Sweden,1500
FrontRow,Bengaluru,Education,130,0.75,10/12/2022,Series A,India,17
Noom,New York City,Healthcare,500,0.1,10/11/2022,Series F,United States,657
MX,Lehi,Finance,200,NULL,10/11/2022,Series C,United States,450
Brex,SF Bay Area,Finance,136,0.11,10/11/2022,Series D,United States,1500
Pacaso,SF Bay Area,Real Estate,100,0.3,10/11/2022,Series C,United States,217
Sketch,The Hague,Other,80,NULL,10/11/2022,Series A,Netherlands,20
Udacity,SF Bay Area,Education,55,0.13,10/11/2022,Unknown,United States,235
Linkfire,Copenhagen,Marketing,35,0.35,10/11/2022,Seed,Denmark,2
Emitwise,London,Energy,NULL,NULL,10/11/2022,Series A,United Kingdom,16
GSR,Hong Kong,Crypto Currency,NULL,NULL,10/11/2022,Unknown,Hong Kong,NULL
VanHack,Vancouver,HR,NULL,NULL,10/11/2022,Seed,United States,NULL
HelloFresh,SF Bay Area,Food,611,NULL,10/10/2022,Post-IPO,United States,367
Momentive,SF Bay Area,Marketing,180,0.11,10/10/2022,Post-IPO,United States,1100
Pavilion Data,SF Bay Area,Infrastructure,96,0.96,10/10/2022,Series D,United States,103
Redesign Health,New York City,Healthcare,67,0.2,10/10/2022,Series C,United States,315
Nyriad,Austin,Infrastructure,NULL,0.33,10/10/2022,Unknown,United States,58
BioMarin,SF Bay Area,Healthcare,120,0.04,10/7/2022,Post-IPO,United States,585
Rev.com,Austin,Data,85,NULL,10/7/2022,Series D,United States,30
Crypto.com,Singapore,Crypto,2000,0.3,10/6/2022,Unknown,Singapore,156
Peloton,New York City,Fitness,500,0.12,10/6/2022,Post-IPO,United States,1900
Landing,Birmingham,Real Estate,110,NULL,10/6/2022,Series C,United States.,347
Turnitin,SF Bay Area,Education,51,0.05,10/6/2022,Acquired,United States,NULL
Impossible Foods,SF Bay Area,Food,50,0.06,10/6/2022,Series H,United States,1900
Atome,Singapore,Finance,NULL,NULL,10/6/2022,Unknown,Singapore,645
First AML,Auckland,Finance,NULL,NULL,10/6/2022,Series B,New Zealand,29
Foresight Insurance,SF Bay Area,Finance,NULL,0.4,10/6/2022,Series B,United States,58
Spotify,Stockholm,Media,NULL,NULL,10/6/2022,Post-IPO,Sweden,2100
Built In,Chicago,Recruiting,50,0.25,10/5/2022,Series C,United States,29
TwinStrand,Seattle,Healthcare,NULL,0.5,10/5/2022,Series B,United States,73
8x8,SF Bay Area,Support,200,0.09,10/4/2022,Post-IPO,United States,253
Homie,Salt Lake City,Real Estate,40,0.13,10/4/2022,Series B,United States,35
Fivetran,SF Bay Area,Data,NULL,0.05,10/4/2022,Series D,United States,NULL
Xendit,Jakarta,Finance,NULL,0.05,10/4/2022,Series D,Indonesia,534
Zoomo,Sydney,Transportation,NULL,0.16,10/4/2022,Series B,Australia,105
ActiveCampaign,Chicago,Marketing,NULL,0.15,10/3/2022,Series C,United States,360
Tempo,SF Bay Area,Fitness,NULL,NULL,10/3/2022,Series C,United States,298
WazirX,Mumbai,Crypto,60,0.4,10/2/2022,Acquired,India,NULL
Spin,SF Bay Area,Transportation,78,0.1,9/30/2022,Acquired,United States,8
Carsome,Kuala Lumpur,Transportation,NULL,0.1,9/30/2022,Series E,Malaysia,607
Pastel,SF Bay Area,Food,NULL,1,9/30/2022,Unknown,United States,NULL
Truepill,SF Bay Area,Healthcare,NULL,NULL,9/30/2022,Series D,United States,255
Westwing,Munich,Retail,125,NULL,9/29/2022,Post-IPO,Germany,237
Mux,SF Bay Area,Infrastructure,40,0.2,9/29/2022,Series D,United States,173
Solarisbank,Berlin,Finance,NULL,0.1,9/29/2022,Unknown,Germany,385
Zenjob,Berlin,HR,NULL,NULL,9/29/2022,Series D,Germany,107
DocuSign,SF Bay Area,Sales,671,0.09,9/28/2022,Post-IPO,United States,536
Front,SF Bay Area,Support,NULL,NULL,9/28/2022,Series D,United States,203
Volta,SF Bay Area,Transportation,NULL,0.1,9/28/2022,Post-IPO,United States,575
Divvy Homes,SF Bay Area,Real Estate,40,0.12,9/27/2022,Series B,United States,180
Graphcore,Bristol,Data,NULL,NULL,9/27/2022,Unknown,United Kingdom,692
Instacart,SF Bay Area,Food,NULL,NULL,9/24/2022,Unknown,United States,2900
Konfio,Mexico City,Finance,180,NULL,9/23/2022,Series E,United States,706
Moss,Berlin,Fin-Tech,70,0.15,9/23/2022,Series B,Germany,150
Foxtrot,Chicago,Food,26,0.035,9/23/2022,Series C,United States,166
Truiloo,Vancouver,Security,24,0.05,9/23/2022,Series D,Canada,474
Pesto,SF Bay Area,Other,NULL,1,9/23/2022,Seed,United States,6
NYDIG,New York City,Crypto,110,0.33,9/22/2022,Private Equity,United States,1400
Klarna,Stockholm,Finance,100,NULL,9/22/2022,Unknown,Sweden,3700
Made.com,London,Retail,NULL,0.35,9/22/2022,Series D,United Kingdom,136
Kitty Hawk,SF Bay Area,Aerospace,100,1,9/21/2022,Unknown,United States,1
Candidate Labs,SF Bay Area,HR,NULL,NULL,9/21/2022,Seed,United States,5
Compass,New York City,Real Estate,271,NULL,9/20/2022,Post-IPO,United States,1600
Curative,Los Angeles,Healthcare,109,NULL,9/20/2022,Seed,United States,8
Ada,Toronto,Support,78,0.16,9/20/2022,Series C,Canada,190
99,Sao Paulo,Transportation,75,0.02,9/20/2022,Acquired,Brazil,244
Ouster,SF Bay Area,Transportation,NULL,0.1,9/20/2022,Post-IPO,United States,282
Zappos,Las Vegas,Retail,NULL,0.04,9/20/2022,Acquired,United States,62
Ola,Bengaluru,Transportation,200,NULL,9/19/2022,Series J,India,5000
Vesalius Therapeutics,Boston,Healthcare,29,0.43,9/19/2022,Series B,United States,75
VideoAmp,Los Angeles,Marketing,NULL,0.02,9/19/2022,Series F,United States,456
Shopee,Jakarta,Food,NULL,NULL,9/18/2022,Unknown,Indonesia,NULL
Clear,Bengaluru,Finance,190,0.2,9/16/2022,Series C,India,140
TrueLayer,London,Finance,40,0.1,9/16/2022,Series E,United States,271
LivePerson,New York City,Support,193,0.11,9/15/2022,Post-IPO,United States,42
Acast,Stockholm,Media,70,0.15,9/15/2022,Post-IPO,Sweden,126
WorkRamp,SF Bay Area,HR,35,0.2,9/15/2022,Series C,United States,67
DayTwo,SF Bay Area,Healthcare,NULL,NULL,9/15/2022,Series B,United States,90
NextRoll,SF Bay Area,Marketing,NULL,0.07,9/15/2022,Unknown,United States,108
Twilio,SF Bay Area,Other,800,0.11,9/14/2022,Post-IPO,United States,614
Pitch,Berlin,Marketing,59,0.3,9/14/2022,Series B,Germany,137
Infarm,Berlin,Other,50,0.05,9/14/2022,Series D,Germany,604
Netflix,SF Bay Area,Media,30,NULL,9/14/2022,Post-IPO,United States,121900
Bitrise,Budapest,Infrastructure,NULL,0.14,9/14/2022,Series C,Hungary,83
Rubius,Boston,Healthcare,160,0.75,9/13/2022,Post-IPO,United States,445
Checkout.com,London,Finance,100,0.05,9/13/2022,Series D,United Kingdom,1800
Taboola,New York City,Marketing,100,0.06,9/13/2022,Post-IPO,United States,445
Patreon,SF Bay Area,Media,80,0.17,9/13/2022,Series F,United States,413
FullStory,Atlanta,Marketing,NULL,0.12,9/13/2022,Unknown,United States,197
Propzy,Ho Chi Minh City,Real Estate,NULL,1,9/13/2022,Series A,Vietnam,33
Quicko,Sao Paulo,Transportation,60,NULL,9/12/2022,Acquired,Brazil,28
Mode Analytics,SF Bay Area,Data,25,NULL,9/12/2022,Series D,United States,81
Compete,Tel Aviv,HR,11,0.28,9/12/2022,Series A,Israel,17
Karbon,SF Bay Area,Other,NULL,0.23,9/12/2022,Series B,United States,91
Rent the Runway,New York City,Retail,NULL,0.24,9/12/2022,Post-IPO,United States,526
Sama,SF Bay Area,Data,NULL,NULL,9/12/2022,Series B,United States,84
SkipTheDishes,Winnipeg,Food,350,NULL,9/9/2022,Acquired,Canada,6
Brighte,Sydney,Energy,58,NULL,9/9/2022,Series C,Australia,145
Patreon,SF Bay Area,Media,5,NULL,9/9/2022,Series F,United States,413
Amber Group,Hong Kong,Crypto,NULL,0.1,9/9/2022,Series B,Hong Kong,328
Capiter,Cairo,Finance,NULL,NULL,9/9/2022,Series A,Egypt,33
CommonBond,New York City,Finance,NULL,1,9/9/2022,Series D,United States,125
DreamBox Learning,Seattle,Education,NULL,NULL,9/9/2022,Acquired,United States,175
Flowhub,Denver,Retail,NULL,0.15,9/9/2022,Unknown,United States,45
Lido Learning,Mumbai,Education,NULL,1,9/9/2022,Series C,India,20
GoStudent,Vienna,Education,200,NULL,9/8/2022,Series D,Austria,686
Pomelo Fashion,Bangkok,Retail,55,0.08,9/8/2022,Unknown,Thailand,120
Genome Medical,SF Bay Area,Healthcare,23,NULL,9/8/2022,Series C,United States,120
BigBear.ai,Baltimore,Data,NULL,0.07,9/8/2022,Post-IPO,United States,200
Realtor.com,SF Bay Area,Real Estate,NULL,NULL,9/8/2022,Acquired,United States,NULL
Simple Feast,Copenhagen,Food,150,1,9/7/2022,Unknown,Denmark,173
Foodpanda,Singapore,Food,60,NULL,9/7/2022,Acquired,Singapore,749
Uber,Vilnius,Transportation,60,NULL,9/7/2022,Post-IPO,Lithuania,24700
Rupeek,Bengaluru,Finance,50,NULL,9/7/2022,Unknown,India,172
Intercom,SF Bay Area,Support,49,0.05,9/7/2022,Series D,United States,240
Pendo,Raleigh,Product,45,0.05,9/7/2022,Series F,United States,469
Demandbase,SF Bay Area,Sales,27,0.03,9/7/2022,Series H,United States,143
Firebolt,Tel Aviv,Data,NULL,NULL,9/7/2022,Series C,Israel,264
Medly,New York City,Healthcare,NULL,0.5,9/7/2022,Series C,United States,100
Xsight Labs,Tel Aviv,Other,NULL,NULL,9/7/2022,Series D,Israel,100
Brave Care,Portland,Healthcare,40,0.33,9/6/2022,Series B,United States,42
Lawgeex,Tel Aviv,Legal,30,0.33,9/6/2022,Series C,Israel,41
Juniper Square,SF Bay Area,Real Estate,NULL,0.14,9/6/2022,Series C,United States,108
Medium,SF Bay Area,Media,NULL,0.25,9/6/2022,Unknown,United States,163
Kuda,Lagos,Finance,23,0.05,9/2/2022,Series B,Nigeria,91
Alerzo,Ibadan,Retail,NULL,NULL,9/2/2022,Series B,Nigeria,16
Sea,Singapore,Consumer,NULL,NULL,9/2/2022,Post-IPO,Singapore,8600
2TM,Sao Paulo,Crypto,100,0.15,9/1/2022,Unknown,Brazil,250
Innovaccer,SF Bay Area,Healthcare,90,0.08,9/1/2022,Series E,United States,379
Shopify,Ottawa,Retail,70,NULL,9/1/2022,Post-IPO,Canada,122
Urban Sports Club,Berlin,Fitness,55,0.15,9/1/2022,Unknown,Germany,95
Hedvig,Stockholm,Finance,12,NULL,9/1/2022,Series B,Sweden,67
Snap,Los Angeles,Consumer,1280,0.2,8/31/2022,Post-IPO,United States,4900
GoodRx,Los Angeles,Healthcare,140,0.16,8/31/2022,Post-IPO,United States,910
Smava,Berlin,Finance,100,0.1,8/31/2022,Unknown,Germany,188
Hippo Insurance,SF Bay Area,Finance,70,0.1,8/31/2022,Post-IPO,United States,1300
Clari,SF Bay Area,Sales,45,NULL,8/31/2022,Series F,United States,496
Koo,Bengaluru,Consumer,40,NULL,8/31/2022,Series B,India,44
TCR2,Boston,Healthcare,30,0.2,8/31/2022,Post-IPO,United States,173
Apartment List,SF Bay Area,Real Estate,29,0.1,8/31/2022,Series D,United States,169
Artnight,Berlin,Retail,26,0.36,8/31/2022,Unknown,Germany,NULL
Snagajob,Richmond,HR,NULL,NULL,8/31/2022,Unknown,United States,221
The Wing,New York City,Real Estate,NULL,1,8/31/2022,Series C,United States,117
Viamo,Accra,Other,NULL,NULL,8/31/2022,Unknown,Ghana,NULL
Electric,New York City,Other,81,NULL,8/30/2022,Series D,United States,212
Immersive Labs,Bristol,Security,38,0.1,8/30/2022,Series C,United Kingdom,123
Nate,New York City,Retail,30,NULL,8/30/2022,Series A,United States,47
Meesho,Bengaluru,Retail,300,NULL,8/29/2022,Series F,India,1100
54gene,Washington D.C.,Healthcare,95,0.3,8/29/2022,Series B,United States,44
Fungible,SF Bay Area,Crypto,NULL,NULL,8/29/2022,Series C,United States,310
Skillz,SF Bay Area,Consumer,NULL,NULL,8/29/2022,Post-IPO,United States,287
Otonomo,Tel Aviv,Transportation,NULL,NULL,8/28/2022,Post-IPO,Israel,231
Zymergen,SF Bay Area,Other,80,NULL,8/26/2022,Acquired,United States,974
Okta,SF Bay Area,Security,24,NULL,8/26/2022,Post-IPO,United States,1200
Argyle,New York City,Finance,20,0.07,8/26/2022,Series B,United States,78
Better.com,New York City,Real Estate,NULL,NULL,8/26/2022,Unknown,United States,905
FreshDirect,Philadelphia,Food,40,NULL,8/25/2022,Acquired,United States,280
Loja Integrada,Sao Paulo,Retail,25,0.1,8/25/2022,Acquired,Brazil,NULL
Impact.com,Los Angeles,Marketing,NULL,0.1,8/25/2022,Private Equity,United States,361
ShipBob,Chicago,Logistics,NULL,0.07,8/25/2022,Series E,United States,330
Reali,SF Bay Area,Real Estate,140,1,8/24/2022,Series B,United States,117
Loop,Washington D.C.,Finance,15,0.2,8/24/2022,Series A,United States,24
Pix,SF Bay Area,Food,NULL,NULL,8/24/2022,Unknown,United States,NULL
Tier Mobility,Berlin,Transportation,180,0.16,8/23/2022,Series D,Germany,646
Packable,New York City,Retail,138,0.2,8/23/2022,Unknown,United States,472
Q4,Toronto,Other,50,0.08,8/23/2022,Series C,Canada,91
Skedulo,SF Bay Area,HR,31,0.08,8/23/2022,Series C,United States,114
Plato,SF Bay Area,HR,29,0.5,8/23/2022,Seed,United States,3
DataRobot,Boston,Data,NULL,0.26,8/23/2022,Series G,United States,1000
Kogan,Melbourne,Retail,NULL,NULL,8/23/2022,Post-IPO,Australia,NULL
Skillshare,New York City,Education,NULL,NULL,8/23/2022,Unknown,United States,136
Mr. Yum,Melbourne,Food,NULL,0.17,8/22/2022,Series A,United States,73
ShopX,Bengaluru,Retail,NULL,1,8/22/2022,Unknown,India,56
NSO,Tel Aviv,Security,100,0.14,8/21/2022,Seed,Israel,1
Tufin,Boston,Security,55,0.1,8/21/2022,Acquired,United States,21
Amperity,Seattle,Marketing,13,0.03,8/20/2022,Series D,United States,187
Wayfair,Boston,Retail,870,0.05,8/19/2022,Post-IPO,United States,1700
Stripe,SF Bay Area,Finance,50,NULL,8/19/2022,Series H,United States,2300
Hodlnaut,Singapore,Crypto,40,0.8,8/19/2022,Unknown,Singapore,NULL
New Relic,SF Bay Area,Infrastructure,110,0.05,8/18/2022,Post-IPO,United States,214
Wheel,Austin,Healthcare,35,0.17,8/18/2022,Series C,United States,215
Petal,New York City,Finance,NULL,NULL,8/18/2022,Series D,United States,704
Thirty Madison,New York City,Healthcare,NULL,0.1,8/18/2022,Series C,United States,209
Vendasta,Saskatoon,Marketing,NULL,0.05,8/18/2022,Series D,Canada,178
Malwarebytes,SF Bay Area,Security,125,0.14,8/17/2022,Series B,United States,80
Fluke,Sao Paulo,Other,83,0.82,8/17/2022,Seed,Brazil,NULL
Swyftx,Brisbane,Crypto,74,0.21,8/17/2022,Unknown,Australia,NULL
Tempo Automation,SF Bay Area,Other,54,NULL,8/17/2022,Series C,United States,74
Genesis,New York City,Crypto,52,0.2,8/17/2022,Series A,United States,NULL
Warren,Porto Alegre,Finance,50,NULL,8/17/2022,Series C,Brazil,104
AlayaCare,Montreal,Healthcare,80,0.14,8/16/2022,Series D,Canada,293
Pliops,Tel Aviv,Data,12,0.09,8/16/2022,Series D,Israel,205
Woven,Indianapolis,HR,5,0.15,8/16/2022,Series A,United States,11
Crypto.com,Singapore,Crypto,NULL,NULL,8/16/2022,Unknown,Singapore,156
Edmodo,SF Bay Area,Education,NULL,1,8/16/2022,Acquired,United States,77
Snapdocs,SF Bay Area,Real Estate,NULL,0.1,8/16/2022,Series D,United States,253
Updater,New York City,Other,NULL,NULL,8/16/2022,Unknown,United States,467
Sema4,Stamford,Healthcare,250,0.13,8/15/2022,Post-IPO,United States,791
Blend,SF Bay Area,Finance,220,0.12,8/15/2022,Post-IPO,United States,665
ContraFect,New York City,Healthcare,16,0.37,8/15/2022,Post-IPO,United States,380
ThredUp,Chicago,Retail,NULL,0.15,8/15/2022,Post-IPO,United States,305
Anywell,Tel Aviv,Real Estate,11,NULL,8/14/2022,Series A,Israel,15
Almanac,SF Bay Area,Other,NULL,NULL,8/13/2022,Series A,United States,45
Peloton,New York City,Fitness,784,0.13,8/12/2022,Post-IPO,United States,1900
Core Scientific,Austin,Crypto,NULL,0.1,8/12/2022,Post-IPO,United States,169
Orbit,SF Bay Area,Other,NULL,NULL,8/12/2022,Series A,United States,20
Truepill,SF Bay Area,Healthcare,175,0.33,8/11/2022,Series D,United States,255
Calm,SF Bay Area,Healthcare,90,0.2,8/11/2022,Series C,United States,218
FourKites,Chicago,Logistics,60,0.08,8/11/2022,Series D,United States,201
Marketforce,Nairobi,Retail,54,0.09,8/11/2022,Series A,Kenya,42
Betterfly,Santiago,Healthcare,30,NULL,8/11/2022,Series C,Chile,204
Expert360,Sydney,Recruiting,7,NULL,8/11/2022,Series C,Australia,26
Guidewire,SF Bay Area,Finance,NULL,0.02,8/11/2022,Post-IPO,United States,24
Trybe,Sao Paulo,Education,47,0.1,8/10/2022,Series B,Brazil,40
Permutive,London,Marketing,30,0.12,8/10/2022,Series C,United Kingdom,105
Homeward,Austin,Real Estate,NULL,0.2,8/10/2022,Series B,United States,160
Pollen,London,Marketing,NULL,1,8/10/2022,Series C,United Kingdom,238
Vedanta Biosciences,Boston,Healthcare,NULL,0.2,8/10/2022,Series D,United States,301
GoHealth,Chicago,Healthcare,800,0.2,8/9/2022,Post-IPO,United States,75
Hootsuite,Vancouver,Marketing,400,0.3,8/9/2022,Series C,Canada,300
Nutanix,SF Bay Area,Infrastructure,270,0.04,8/9/2022,Post-IPO,United States,1100
Quanterix,Boston,Healthcare,130,0.25,8/9/2022,Post-IPO,United States,533
Wix,Tel Aviv,Marketing,100,NULL,8/9/2022,Post-IPO,Israel,58
MadeiraMadeira,Curitiba,Retail,60,0.03,8/9/2022,Series E,Brazil,338
Melio,New York City,Finance,60,NULL,8/9/2022,Series D,United States,504
Linktree,Melbourne,Consumer,50,0.17,8/9/2022,Unknown,Australia,165
Shogun,SF Bay Area,Retail,48,0.3,8/9/2022,Series C,United States,114
Absci,Vancouver,Healthcare,40,NULL,8/9/2022,Post-IPO,United States,237
Dooly,Vancouver,Sales,12,NULL,8/9/2022,Series B,Canada,102
Berkeley Lights,SF Bay Area,Healthcare,NULL,0.12,8/9/2022,Post-IPO,United States,272
DailyPay,New York City,Finance,NULL,0.15,8/9/2022,Unknown,United States,814
Haus,SF Bay Area,Food,NULL,1,8/9/2022,Seed,United States,7
Kaltura,New York City,Media,NULL,0.1,8/9/2022,Post-IPO,United States,166
Shift,SF Bay Area,Transportation,NULL,NULL,8/9/2022,Post-IPO,United States,504
Sweetgreen,Los Angeles,Food,NULL,NULL,8/9/2022,Post-IPO,United States,478
Groupon,Chicago,Retail,500,0.15,8/8/2022,Post-IPO,United States,1400
Loggi,Sao Paulo,Logistics,500,0.15,8/8/2022,Series F,Brazil,507
Vroom,New York City,Transportation,337,NULL,8/8/2022,Post-IPO,United States,1300
Warby Parker,New York City,Consumer,63,NULL,8/8/2022,Post-IPO,United States,535
Labelbox,SF Bay Area,Data,36,NULL,8/8/2022,Series D,United States,188
Perion,Tel Aviv,Marketing,20,0.05,8/8/2022,Post-IPO,Israel,76
Daily Harvest,New York City,Food,NULL,0.15,8/8/2022,Series D,United States,120
DataRobot,Boston,Data,NULL,NULL,8/8/2022,Series G,United States,1000
iRobot,Boston,Consumer,140,0.1,8/5/2022,Acquired,United States,30
Mejuri,Toronto,Retail,50,0.1,8/5/2022,Series B,Canada,28
Uberflip,Toronto,Marketing,31,0.17,8/5/2022,Series A,Canada,42
Slync,Dallas,Logistics,NULL,NULL,8/5/2022,Series B,United States,76
Talkdesk,SF Bay Area,Support,NULL,NULL,8/5/2022,Series D,United States,497
Doma,SF Bay Area,Finance,250,0.13,8/4/2022,Post-IPO,United States,679
Article,Vancouver,Retail,216,0.17,8/4/2022,Series B,Canada,NULL
Jam City,Los Angeles,Consumer,200,0.17,8/4/2022,Unknown,United States,652
10X Genomics,SF Bay Area,Healthcare,100,0.08,8/4/2022,Post-IPO,United States,242
LEAD,Mumbai,Education,80,0.04,8/4/2022,Series E,India,166
Zendesk,SF Bay Area,Support,80,NULL,8/4/2022,Acquired,United States,85
On Deck,SF Bay Area,Education,73,0.33,8/4/2022,Series A,United States,20
RenoRun,Montreal,Construction,70,0.12,8/4/2022,Series B,Canada,163
RingCentral,SF Bay Area,Support,50,NULL,8/4/2022,Post-IPO,United States,44
Medly,New York City,Healthcare,NULL,0.16,8/4/2022,Series C,United States,100
Nomad,Sao Paulo,Finance,NULL,0.2,8/4/2022,Series B,Brazil,290
StubHub,SF Bay Area,Consumer,NULL,NULL,8/4/2022,Acquired,United States,59
Weedmaps,Los Angeles,Other,NULL,0.1,8/4/2022,Acquired,United States,NULL
Zenius,Jakarta,Education,NULL,0.3,8/4/2022,Series B,Indonesia,20
Healthcare.com,Miami,Healthcare,149,NULL,8/3/2022,Series C,United States,244
Unbounce,Vancouver,Marketing,47,0.2,8/3/2022,Series A,Canada,39
Beyond Meat,Los Angeles,Food,40,NULL,8/3/2022,Post-IPO,United States,122
The Org,New Delhi,HR,13,NULL,8/3/2022,Series B,United States,39
CarDekho,Gurugram,Transportation,NULL,NULL,8/3/2022,Series E,India,497
Puppet,Portland,Infrastructure,NULL,0.15,8/3/2022,Acquired,United States,189
SoundCloud,Berlin,Consumer,NULL,0.2,8/3/2022,Unknown,Germany,542
Talkwalker,Luxembourg,Marketing,NULL,0.15,8/3/2022,Private Equity,Luxembourg,9
Robinhood,SF Bay Area,Finance,713,0.23,8/2/2022,Post-IPO,United States,5600
Latch,New York City,Security,115,0.37,8/2/2022,Post-IPO,United States,342
Vedantu,Bengaluru,Education,100,NULL,8/2/2022,Series E,India,292
Seegrid,Pittsburgh,Logistics,90,NULL,8/2/2022,Unknown,United States,107
Nylas,SF Bay Area,Product,80,0.25,8/2/2022,Series C,United States,175
Outreach,Seattle,Sales,60,0.05,8/2/2022,Series G,United States,489
Sendy,Nairobi,Logistics,54,0.2,8/2/2022,Series B,Kenya,26
The Predictive Index,Boston,HR,40,NULL,8/2/2022,Acquired,United States,71
Sendy,Nairobi,Logistics,30,0.1,8/2/2022,Series B,Kenya,26
Stedi,Boulder,Product,23,0.3,8/2/2022,Series B,United States,75
Glossier,New York City,Retail,19,0.08,8/2/2022,Series E,United States,266
Butterfly Network,New Haven,Healthcare,NULL,0.1,8/2/2022,Post-IPO,United States,530
FuboTV,New York City,Media,NULL,NULL,8/2/2022,Post-IPO,United States,151
Hash,Sao Paulo,Finance,58,0.5,8/1/2022,Series C,Brazil,58
Classkick,Chicago,Education,NULL,NULL,8/1/2022,Seed,United States,1
DeHaat,Gurugram,Food,NULL,NULL,8/1/2022,Series D,India,194
OnlyFans,London,Media,NULL,NULL,8/1/2022,Unknown,United Kingdom,NULL
Oracle,SF Bay Area,Other,NULL,NULL,8/1/2022,Post-IPO,United States,NULL
Perceptive Automata,Boston,Transportation,NULL,1,8/1/2022,Series A,United States,20
Whereby,Oslo,Other,NULL,NULL,8/1/2022,Series A,Norway,10
Metigy,Sydney,Marketing,75,1,7/31/2022,Series B,Australia,18
Vee,Tel Aviv,HR,16,0.32,7/31/2022,Seed,Israel,15
Gatherly,Atlanta,Marketing,NULL,0.5,7/31/2022,NULL,United States,NULL
Ola,Bengaluru,Transportation,1000,NULL,7/29/2022,Series J,India,5000
Clearco,Toronto,Fin-Tech,125,0.25,7/29/2022,Series C,Canada,681
Imperfect Foods,SF Bay Area,Food,50,NULL,7/29/2022,Series D,United States,229
Shelf Engine,Seattle,Food,43,NULL,7/29/2022,Series B,United States,58
Quantcast,SF Bay Area,Marketing,40,0.06,7/29/2022,Series C,United States,65
Sherpa,Toronto,Travel,22,NULL,7/29/2022,Unknown,Canada,11
CoinFLEX,Victoria,Crypto,NULL,NULL,7/29/2022,Unknown,Seychelles,11
MissFresh,Beijing,Food,NULL,NULL,7/29/2022,Post-IPO,China,1700
Yabonza,Sydney,Real Estate,NULL,1,7/29/2022,Unknown,Australia,6
Ribbon,New York City,Real Estate,136,NULL,7/28/2022,Series C,United States,405
Career Karma,SF Bay Area,Education,60,0.33,7/28/2022,Series B,United States,51
Metromile,SF Bay Area,Finance,60,0.2,7/28/2022,Acquired,United States,510
Laybuy,Auckland,Finance,45,NULL,7/28/2022,Post-IPO,New Zealand,130
Allbirds,SF Bay Area,Retail,23,NULL,7/28/2022,Post-IPO,United States,202
TextNow,Waterloo,Consumer,22,NULL,7/28/2022,Seed,Canada,1
2U,Washington D.C.,Education,NULL,0.2,7/28/2022,Post-IPO,United States,426
Bikayi,Bengaluru,Retail,NULL,NULL,7/28/2022,Series A,India,12
Brainbase,Los Angeles,Sales,NULL,NULL,7/28/2022,Series A,United States,12
Change.org,SF Bay Area,Other,NULL,0.19,7/28/2022,Series D,United States,72
Tapas Media,SF Bay Area,Media,NULL,NULL,7/28/2022,Acquired,United States,17
Turntide,SF Bay Area,Energy,NULL,0.2,7/28/2022,Unknown,United States,491
Rivian,Detroit,Transportation,840,0.06,7/27/2022,Post-IPO,United States,10700
Vox Media,Washington D.C.,Media,39,0.02,7/27/2022,Series F,United States,307
Coinsquare,Toronto,Crypto,30,0.24,7/27/2022,Unknown,Canada,98
Skai,Tel Aviv,Marketing,30,0.04,7/27/2022,Series E,Israel,60
Shopify,Ottawa,Retail,1000,0.1,7/26/2022,Post-IPO,Canada,122
McMakler,Berlin,Real Estate,90,NULL,7/26/2022,Unknown,Germany,214
Fiverr,Tel Aviv,Other,60,0.08,7/26/2022,Post-IPO,Israel,111
InDebted,Sydney,Finance,40,0.17,7/26/2022,Series B,Australia,41
Outbrain,New York City,Marketing,38,0.03,7/26/2022,Post-IPO,United States,394
Dover,SF Bay Area,Recruiting,23,0.3,7/26/2022,Series A,United States,22
Immutable,Sydney,Crypto,20,0.06,7/26/2022,Series C,Australia,279
Zymergen,SF Bay Area,Other,80,NULL,7/25/2022,Acquired,United States,974
Pear Therapeutics ,Boston,Healthcare,25,0.09,7/25/2022,Post-IPO,United States,409
Included Health,SF Bay Area,Healthcare,NULL,0.06,7/25/2022,Series E,United States,272
Soluto,Tel Aviv,Support,120,1,7/24/2022,Acquired,Israel,18
Eucalyptus,Sydney,Healthcare,50,0.2,7/22/2022,Series C,United States,69
Workstream,SF Bay Area,HR,45,NULL,7/22/2022,Series B,United States,58
Quanto,Sao Paulo,Finance,28,0.22,7/22/2022,Series A,Brazil,15
Clarify Health,SF Bay Area,Healthcare,15,0.05,7/22/2022,Series D,United States,328
Arete,Miami,Security,NULL,NULL,7/22/2022,Unknown,United States,NULL
Boosted Commerce,Los Angeles,Retail,NULL,0.05,7/22/2022,Series B,United States,137
Owlet,Lehi,Healthcare,NULL,NULL,7/22/2022,Post-IPO,United States,178
People.ai,SF Bay Area,Sales,NULL,NULL,7/22/2022,Series D,United States,200
Wizeline,SF Bay Area,Product,NULL,NULL,7/22/2022,Acquired,United States,62
Blockchain.com,London,Crypto,150,0.25,7/21/2022,Series D,United Kingdom,490
Callisto Media,SF Bay Area,Media,140,0.35,7/21/2022,Series D,United States,NULL
AppGate,Miami,Security,130,0.22,7/21/2022,Post-IPO,United States,NULL
WHOOP,Boston,Fitness,95,0.15,7/21/2022,Series F,United States,404
Rad Power Bikes,Seattle,Transportation,63,0.1,7/21/2022,Series D,United States,329
Lunchbox,New York City,Food,60,0.33,7/21/2022,Series B,United States,72
RealSelf,Seattle,Healthcare,11,0.05,7/21/2022,Series B,United States,42
98point6,Seattle,Healthcare,NULL,0.1,7/21/2022,Series E,United States,247
Catalyst,New York City,Support,NULL,NULL,7/21/2022,Series B,United States,45
InVision,New York City,Product,NULL,0.5,7/21/2022,Series F,United States,356
Mural,SF Bay Area,Product,NULL,NULL,7/21/2022,Series C,United States,192
Smarsh,Portland,Other,NULL,NULL,7/21/2022,Private Equity,United States,NULL
Just Eat Takeaway,Amsterdam,Food,390,NULL,7/20/2022,Post-IPO,Netherlands,2800
Flyhomes,Seattle,Real Estate,200,0.2,7/20/2022,Series C,United States,310
Varo,SF Bay Area,Finance,75,NULL,7/20/2022,Series E,United States,992
BlueStacks,SF Bay Area,Other,60,NULL,7/20/2022,Series C,United States,48
Lyft,SF Bay Area,Transportation,60,0.02,7/20/2022,Post-IPO,United States,4900
Introhive,Ferdericton,Sales,57,0.16,7/20/2022,Series C,Canada,125
Zencity,Tel Aviv,Other,30,0.2,7/20/2022,Unknown,Israel,51
Splice,New York City,Media,23,NULL,7/20/2022,Series D,United States,159
Forma.ai,Toronto,Sales,15,0.09,7/20/2022,Series B,Canada,58
Arc,SF Bay Area,HR,13,NULL,7/20/2022,Seed,United States,1
Invitae,SF Bay Area,Healthcare,1000,NULL,7/19/2022,Post-IPO,United States,2000
Olive,Columbus,Healthcare,450,0.31,7/19/2022,Series H,United States,856
M1,Chicago,Finance,38,NULL,7/19/2022,Series E,United States,323
SellerX,Berlin,Retail,28,NULL,7/19/2022,Unknown,Germany,766
Stint,London,HR,28,0.2,7/19/2022,Unknown,United Kingdom,NULL
Capsule,New York City,Healthcare,NULL,0.13,7/19/2022,Series D,United States,570
PACT Pharma,SF Bay Area,Healthcare,94,NULL,7/18/2022,Series C,United States,200
Gemini,New York City,CryptoCurrency,68,0.07,7/18/2022,Unknown,United States,423
Lusha,New York City,Marketing,30,0.1,7/18/2022,Series B,United States,245
Elemy,SF Bay Area,Healthcare,NULL,NULL,7/18/2022,Series B,United States,323
Freshly,New York City,Food,NULL,0.25,7/18/2022,Acquired,United States,107
Hydrow,Boston,Fitness,NULL,0.35,7/18/2022,Series D,United States,269
TikTok,Los Angeles,Consumer,NULL,NULL,7/18/2022,Acquired,United States,NULL
Vimeo,New York City,Consumer,NULL,0.06,7/18/2022,Post-IPO,United States,450
Bright Money,Bengaluru,Finance,100,0.5,7/15/2022,Series A,India,31
Project44,Chicago,Logistics,63,0.05,7/15/2022,Unknown,United States,817
Heroes,London,Retail,24,0.2,7/15/2022,Unknown,United States,265
Aspire,SF Bay Area,Marketing,23,NULL,7/15/2022,Series A,United States,27
StyleSeat,SF Bay Area,Consumer,NULL,0.17,7/15/2022,Series C,United States,40
Zego,London,Finance,85,0.17,7/14/2022,Series C,United Kingdom,202
The Mom Project,Chicago,HR,54,0.15,7/14/2022,Series C,United States,115
Unstoppable Domains,SF Bay Area,Crypto Currency,42,0.25,7/14/2022,Series B,United States,7
Kiavi,SF Bay Area,Real Estate,39,0.07,7/14/2022,Series E,United States,240
Alto Pharmacy,SF Bay Area,Healthcare,NULL,NULL,7/14/2022,Series E,United States,560
Cosuno,Berlin,Construction,NULL,NULL,7/14/2022,Series B,Germany,45
OpenSea,New York City,Crypto,NULL,0.2,7/14/2022,Series C,United States,427
Wave,Dakar,Finance,300,0.15,7/13/2022,Series A,Senegal,292
Tonal,SF Bay Area,Fitness,262,0.35,7/13/2022,Series E,United States,450
Fabric,New York City,Logistics,120,0.4,7/13/2022,Series C,United States,336
Bryter,Berlin,Product,100,0.3,7/13/2022,Series B,Germany,89
ChowNow,Los Angeles,Food,100,0.2,7/13/2022,Series C,United States,64
Involves,Florianópolis,Retail,70,0.18,7/13/2022,Unknown,Brazil,23
100 Thieves,Los Angeles,Consumer,12,NULL,7/13/2022,Series C,United States,120
Nuro,SF Bay Area,Transportation,7,NULL,7/13/2022,Series D,United States,2100
Arrival,London,Transportation,NULL,0.3,7/13/2022,Post-IPO,United Kingdom,629
CircleUp,SF Bay Area,Finance,NULL,NULL,7/13/2022,Series C,United States,53
Papa,Miami,Other,NULL,0.15,7/13/2022,Series D,United States,241
Gopuff,Philadelphia,Food,1500,0.1,7/12/2022,Series H,United States,3400
Fraazo,Mumbai,Food,150,NULL,7/12/2022,Series B,India,63
Babylon,London,Healthcare,100,NULL,7/12/2022,Post-IPO,United Kingdom,1100
Hubilo,SF Bay Area,Marketing,45,0.12,7/12/2022,Series B,United States,153
Airlift,Lahore,Logistics,NULL,1,7/12/2022,Series B,Pakistan,109
Microsoft,Seattle,Other,NULL,NULL,7/12/2022,Post-IPO,United States,1
Spring,SF Bay Area,Retail,NULL,NULL,7/12/2022,Unknown,United States,61
Hopin,London,Other,242,0.29,7/11/2022,Series D,United Kingdom,1000
Alice,Sao Paulo,Healthcare,63,NULL,7/11/2022,Series C,Brazil,174
SundaySky,New York City,Marketing,24,0.13,7/11/2022,Series D,United States,74
Apeel Sciences,Santa Barbara,Food,NULL,NULL,7/11/2022,Series E,United States,640
Forward,SF Bay Area,Healthcare,NULL,0.05,7/11/2022,Series D,United States,225
Ignite,SF Bay Area,Crypto,NULL,0.5,7/11/2022,Series A,United States,9
Nextbite,Denver,Food,NULL,NULL,7/9/2022,Series C,United States,150
PuduTech,Shenzen,Other,1500,NULL,7/8/2022,Series C,China,184
Butler Hospitality,New York City,Food,1000,1,7/8/2022,Series B,United States,50
Calibrate,New York City,Healthcare,156,0.24,7/8/2022,Series B,United States,127
NextRoll,SF Bay Area,Marketing,NULL,0.03,7/8/2022,Unknown,United States,108
Argo AI,Pittsburgh,Transportation,150,0.05,7/7/2022,Unknown,United States,3600
Next Insurance,SF Bay Area,Finance,150,0.17,7/7/2022,Series E,United States,881
Adwerx,Durham,Marketing,40,NULL,7/7/2022,Unknown,United States,20
Emotive,Los Angeles,Marketing,30,0.18,7/7/2022,Series B,United States,78
Cedar,New York City,Healthcare,NULL,0.24,7/7/2022,Series D,United States,351
Twitter,SF Bay Area,Consumer,NULL,NULL,7/7/2022,Post-IPO,United States,5700
Remote,SF Bay Area,HR,100,0.09,7/6/2022,Series C,United States,496
Shopify,Ottawa,Retail,50,NULL,7/6/2022,Post-IPO,Canada,122
Anodot,Tel Aviv,Data,35,0.27,7/6/2022,Series C,United States,64
SQream,New York City,Data,30,0.18,7/6/2022,Series B,United States,77
Motif Foodworks,Boston,Food,NULL,NULL,7/6/2022,Series B,United States,344
Loft,Sao Paulo,Real Estate,384,0.12,7/5/2022,Unknown,Brazil,788
Bizzabo,New York City,Marketing,120,0.3,7/5/2022,Series E,United States,194
eToro,Tel Aviv,Finance,100,0.06,7/5/2022,Unknown,Israel,322
Verbit,New York City,Data,80,0.1,7/5/2022,Series E,United States,569
Outschool,SF Bay Area,Education,31,0.18,7/5/2022,Series D,United States,240
Bullish,Hong Kong,Crypto,30,0.08,7/5/2022,Unknown,Hong Kong,300
Transmit Security,Boston,Security,27,0.07,7/5/2022,Series A,United States,583
Thimble,New York City,Fin-Tech,20,0.33,7/5/2022,Series A,United States,28
Syte,Tel Aviv,Retail,13,0.08,7/5/2022,Series C,Israel,71
Lightricks,Jerusalem,Consumer,80,0.12,7/4/2022,Series D,Israel,335
Chessable,London,Consumer,29,NULL,7/4/2022,Acquired,United Kingdom,0
Sendle,Sydney,Logistics,27,0.12,7/4/2022,Series C,Australia,69
Lendis,Berlin,Other,18,0.15,7/4/2022,Series A,Germany,90
Airtasker,Sydney,Consumer,NULL,NULL,7/4/2022,Series C,Australia,26
Gorillas,Berlin,Food,540,NULL,7/3/2022,Series C,Germany,1300
Celsius,New York City,Crypto,150,0.25,7/3/2022,Series B,United States,864
LetsGetChecked,New York City,Healthcare,NULL,NULL,7/2/2022,Series D,United States,263
Perx Health,Sydney,Healthcare,NULL,NULL,7/2/2022,Seed,Australia,2
Zepto,Brisbane,Finance,NULL,0.1,7/2/2022,Series A,Australia,25
WanderJaunt,SF Bay Area,Travel,85,1,7/1/2022,Series B,United States,26
Canoo,Los Angeles,Transportation,58,0.06,7/1/2022,Post-IPO,United States,300
Bamboo Health,Louisville,Healthcare,52,NULL,7/1/2022,Unknown,United States,NULL
Teleport,SF Bay Area,Infrastructure,15,0.06,7/1/2022,Series C,United States,169
Remesh,New York City,Support,NULL,NULL,7/1/2022,Series A,United States,38
Enjoy,SF Bay Area,Retail,400,0.18,6/30/2022,Post-IPO,United States,310
Crejo.Fun,Bengaluru,Education,170,1,6/30/2022,Seed,India,3
Stash Financial,New York City,Finance,40,0.08,6/30/2022,Unknown,United States,480
Nate,New York City,Retail,30,0.2,6/30/2022,Series A,United States,47
Snyk,Boston,Security,30,NULL,6/30/2022,Series F,United States,849
Stream,Boulder,Product,20,0.12,6/30/2022,Series B,United States,58
Finleap Connect,Hamburg,Finance,14,0.1,6/30/2022,Series A,Germany,22
Abra,SF Bay Area,Crypto,12,0.05,6/30/2022,Series C,United States,106
Gavelytics,Los Angeles,Legal,NULL,1,6/30/2022,Seed,United States,5
Secfi,SF Bay Area,Finance,NULL,NULL,6/30/2022,Series A,United States,7
Sundae,SF Bay Area,Real Estate,NULL,0.15,6/30/2022,Series C,United States,135
Toppr,Mumbai,Education,350,NULL,6/29/2022,Acquired,India,112
Unity,SF Bay Area,Other,200,0.04,6/29/2022,Post-IPO,United States,1300
Niantic,SF Bay Area,Consumer,85,0.08,6/29/2022,Series D,United States,770
AvantStay,Los Angeles,Travel,80,NULL,6/29/2022,Unknown,United States,811
Qumulo,Seattle,Data,80,0.19,6/29/2022,Series E,United States,347
Clutch,Toronto,Transportation,76,0.22,6/29/2022,Series B,Canada,153
Parallel Wireless,Nashua,Infrastructure,60,NULL,6/29/2022,Unknown,United States,8
Oye Rickshaw,New Delhi,Transportation,40,0.2,6/29/2022,Unknown,India,13
Rows,Berlin,Other,18,0.3,6/29/2022,Series B,Germany,25
Baton,SF Bay Area,Transportation,16,0.25,6/29/2022,Series A,United States,13
Substack,SF Bay Area,Media,13,0.14,6/29/2022,Series B,United States,82
CommentSold,Huntsville,Retail,NULL,NULL,6/29/2022,Unknown,United States,NULL
Degreed,SF Bay Area,Education,NULL,0.15,6/29/2022,Series D,United States,411
HomeLight,SF Bay Area,Real Estate,NULL,0.19,6/29/2022,Series D,United States,743
Modsy,SF Bay Area,Retail,NULL,NULL,6/29/2022,Series C,United States,72
Volt Bank,Sydney,Finance,NULL,1,6/29/2022,Series E,Australia,90
Huobi,Beijing,Crypto,300,0.3,6/28/2022,Unknown,China,2
WhiteHat Jr,Mumbai,Education,300,NULL,6/28/2022,Acquired,India,11
StockX,Detroit,Retail,120,0.08,6/28/2022,Series E,United States,690
Sidecar Health,Los Angeles,Healthcare,110,0.4,6/28/2022,Series C,United States,163
StockX,Detroit,Retail,80,NULL,6/28/2022,Series E,United States,690
Vezeeta,Dubai,Healthcare,50,0.1,6/28/2022,Series D,United Arab Emirates,71
Bright Machines,SF Bay Area,Data,30,0.08,6/28/2022,Unknown,Israel,250
HealthMatch,Sydney,Healthcare,18,0.5,6/28/2022,Series B,Australia,20
Booktopia,Sydney,Retail,NULL,NULL,6/28/2022,Series A,Australia,23
Nova Benefits,Bengaluru,Healthcare,NULL,0.3,6/28/2022,Series B,India,41
Una Brands,Singapore,Retail,NULL,0.1,6/28/2022,Series A,Singapore,55
AppLovin,SF Bay Area,Marketing,300,0.12,6/27/2022,Post-IPO,United States,1600
UiPath,New York City,Data,210,0.05,6/27/2022,Post-IPO,United States,2000
Udaan,Bengaluru,Retail,180,0.04,6/27/2022,Unknown,India,1500
Cue,San Diego,Healthcare,170,NULL,6/27/2022,Post-IPO,United States,999
Banxa,Melbourne,Crypto,70,0.3,6/27/2022,Post-IPO,Australia,13
SafeGraph,SF Bay Area,Data,27,0.25,6/27/2022,Series B,United States,61
Amount,Chicago,Finance,NULL,0.18,6/27/2022,Unknown,United States,283
Postscript,SF Bay Area,Marketing,43,NULL,6/26/2022,Series C,United States,106
Bitpanda,Vienna,Crypto,270,0.27,6/24/2022,Series C,Austria,546
Sunday,Atlanta,Finance,90,0.23,6/24/2022,Series A,United States,124
Bestow,Dallas,Finance,41,0.14,6/24/2022,Series C,United States,137
Ethos Life,SF Bay Area,Finance,40,0.12,6/24/2022,Series D,United States,406
Feather,New York City,Retail,NULL,NULL,6/24/2022,Unknown,United States,76
Give Legacy,Boston,Healthcare,NULL,NULL,6/24/2022,Series B,United States,45
Netflix,SF Bay Area,Media,300,0.03,6/23/2022,Post-IPO,United States,121900
Aura,Boston,Security,70,0.09,6/23/2022,Series F,United States,500
Pipl,Spokane,Security,22,0.13,6/23/2022,Unknown,United States,19
Vouch,SF Bay Area,Finance,15,0.07,6/23/2022,Series C,United States,159
Voyage SMS,Los Angeles,Marketing,8,0.13,6/23/2022,Unknown,United States,10
Esper,Seattle,Other,NULL,0.12,6/23/2022,Series A,United States,10
Kune,Nairobi,Food,NULL,1,6/23/2022,Seed,Kenya,1
Mark43,New York City,Other,NULL,NULL,6/23/2022,Series E,United States,229
Orchard,New York City,Real Estate,NULL,0.1,6/23/2022,Series D,United States,472
Ro,New York City,Healthcare,NULL,0.18,6/23/2022,Unknown,United States,1000
StreamElements,Tel Aviv,Media,NULL,0.2,6/23/2022,Series B,Israel,111
MasterClass,SF Bay Area,Education,120,0.2,6/22/2022,Series E,United States,461
IronNet,Washington D.C.,Security,90,0.35,6/22/2022,Post-IPO,United States,410
Bungalow,SF Bay Area,Real Estate,70,NULL,6/22/2022,Series C,United States,171
IronNet,Washington D.C.,Security,55,0.17,6/22/2022,Post-IPO,United States,410
Sprinklr,New York City,Support,50,NULL,6/22/2022,Post-IPO,United States,429
Superpedestrian,Boston,Transportation,35,0.07,6/22/2022,Series C,United States,261
Voi,Stockholm,Transportation,35,0.1,6/22/2022,Series D,United States,515
Balto,St. Louis,Sales,30,NULL,6/22/2022,Series B,United States,51
Ritual,Toronto,Food,23,0.16,6/22/2022,Series C,Canada,134
Mindgeek,Luxembourg,Media,NULL,NULL,6/22/2022,Unknown,Luxembourg,NULL
Voly,Sydney,Food,NULL,0.5,6/22/2022,Seed,Australia,13
Ebanx,Curitiba,Finance,340,0.2,6/21/2022,Series B,Brazil,460
TaskUs,Los Angeles,Support,52,0,6/21/2022,Post-IPO,United States,279
Community,Los Angeles,Marketing,40,0.3,6/21/2022,Unknown,United States,40
Sourcegraph,SF Bay Area,Product,24,0.08,6/21/2022,Series D,United States,248
Frubana,Bogota,Food,NULL,0.03,6/21/2022,Series C,Colombia,202
SuperLearn,Bengaluru,Education,NULL,1,6/21/2022,Seed,India,0
Tray.io,SF Bay Area,Data,NULL,0.1,6/21/2022,Series C,United States,109
Bybit,Singapore,Crypto,600,0.3,6/20/2022,Unknown,Singapore,NULL
SummerBio,SF Bay Area,Healthcare,101,1,6/20/2022,Unknown,United States,7
Trax,Singapore,Retail,100,0.12,6/20/2022,Series E,Singapore,1000
Aqgromalin,Chennai,Food,80,0.3,6/20/2022,Unknown,India,12
Bonsai,Toronto,Retail,30,0.55,6/20/2022,Series A,Canada,27
Brighte,Sydney,Energy,30,0.15,6/20/2022,Series C,Australia,145
Buzzer,New York City,Media,NULL,0.2,6/20/2022,Unknown,United States,32
Bybit,Singapore,Crypto,NULL,NULL,6/20/2022,Unknown,Singapore,NULL
CityMall,Gurugram,Retail,191,0.3,6/19/2022,Series C,India,112
BitOasis,Dubai,Crypto,9,0.05,6/19/2022,Series B,United Arab Emirates,30
Unacademy,Bengaluru,Education,150,0.03,6/18/2022,Series H,India,838
Bytedance,Shanghai,Consumer,150,NULL,6/17/2022,Unknown,China,9400
Socure,Reno,Finance,69,0.13,6/17/2022,Series E,United States,646
Finite State,Columbus,Security,16,0.2,6/17/2022,Series B,United States,49
JOKR,New York City,Food,50,0.05,6/16/2022,Series B,United States,430
Zumper,SF Bay Area,Real Estate,45,0.15,6/16/2022,Series D,United States,178
PharmEasy,Mumbai,Healthcare,40,NULL,6/16/2022,Unknown,India,1600
Circulo Health,Columbus,Healthcare,NULL,0.5,6/16/2022,Series A,United States,50
Swappie,Helsinki,Retail,250,0.17,6/15/2022,Series C,Finland,169
Wealthsimple,Toronto,Finance,159,0.13,6/15/2022,Unknown,Canada,900
Weee!,SF Bay Area,Food,150,0.1,6/15/2022,Series E,United States,863
Notarize,Boston,Legal,110,0.25,6/15/2022,Series D,United States,213
Elementor,Tel Aviv,Media,60,0.15,6/15/2022,Unknown,Israel,65
Tonkean,SF Bay Area,Other,23,0.23,6/15/2022,Series B,United States,83
Airtame,Copenhagen,Consumer,15,NULL,6/15/2022,Unknown,Denmark,7
OpenWeb,Tel Aviv,Media,14,0.05,6/15/2022,Series E,Israel,223
Swyft,Toronto,Logistics,10,0.3,6/15/2022,Series A,Canada,20
Crehana,Lima,Education,NULL,NULL,6/15/2022,Series B,Peru,93
JetClosing,Seattle,Real Estate,NULL,1,6/15/2022,Series B,United States,44
Coinbase,SF Bay Area,Crypto,1100,0.18,6/14/2022,Post-IPO,United States,549
Redfin,Seattle,Real Estate,470,0.08,6/14/2022,Post-IPO,United States,319
Compass,New York City,Real Estate,450,0.1,6/14/2022,Post-IPO,United States,1600
Sami,Sao Paulo,Healthcare,75,0.15,6/14/2022,Unknown,Brazil,36
Breathe,New Delhi,Healthcare,50,0.33,6/14/2022,Series A,India,6
Hunty,Bogota,HR,30,NULL,6/14/2022,Seed,Colombia,6
TIFIN,Boulder,Crypto,24,0.1,6/14/2022,Series D,United States,204
Addi,Bogota,Finance,NULL,NULL,6/14/2022,Series C,Colombia,376
Shopee,Singapore,Food,NULL,NULL,6/14/2022,Unknown,Singapore,NULL
BlockFi,New York City,Crypto,250,0.2,6/13/2022,Series E,United States,1000
Wave Sports and Entertainment,Los Angeles,Media,56,0.33,6/13/2022,Series B,United States,65
Studio,SF Bay Area,Education,33,0.4,6/13/2022,Series B,United States,50
Automox,Boulder,Infrastructure,NULL,NULL,6/13/2022,Series C,United States,152
Desktop Metal,Boston,Other,NULL,0.12,6/13/2022,Post-IPO,United States,811
Crypto.com,Singapore,Crypto,260,0.05,6/10/2022,Unknown,Singapore,156
FarEye,New Delhi,Logistics,250,0.3,6/10/2022,Series E,India,150
Berlin Brands Group,Berlin,Retail,100,0.1,6/10/2022,Unknown,Germany,1000
Sanar,Sao Paulo,Healthcare,60,0.2,6/10/2022,Unknown,Brazil,11
Freetrade,London,Finance,45,0.15,6/10/2022,Unknown,United Kingdom,133
Albert,Los Angeles,Finance,20,0.08,6/10/2022,Series C,United States,175
Keepe,Seattle,Real Estate,NULL,NULL,6/10/2022,Unknown,United States,6
Liongard,Houston,Infrastructure,NULL,NULL,6/10/2022,Series B,United States,22
Ziroom,Beijing,Real Estate,NULL,0.2,6/10/2022,Unknown,China,2100
OneTrust,Atlanta,Security,950,0.25,6/9/2022,Series C,United States,926
Stitch Fix,SF Bay Area,Retail,330,0.15,6/9/2022,Post-IPO,United States,79
Daniel Wellington,Stockholm,Retail,200,0.15,6/9/2022,Unknown,Sweden,NULL
Convoy,Seattle,Logistics,90,0.07,6/9/2022,Series E,United States,1100
Hologram,Chicago,Infrastructure,80,0.4,6/9/2022,Series B,United States,82
Boozt,Malmo,Retail,70,0.05,6/9/2022,Post-IPO,Sweden,56
The Grommet,Boston,Retail,40,1,6/9/2022,Acquired,United States,5
Stashaway,Singapore,Finance,31,0.14,6/9/2022,Series D,Singapore,61
Preply,Boston,Education,26,0.05,6/9/2022,Series C,United States,100
Jellysmack,New York City,Media,NULL,0.08,6/9/2022,Series C,United States,22
Starship,SF Bay Area,Transportation,NULL,0.11,6/9/2022,Series B,United States,197
Trade Republic,Berlin,Finance,NULL,NULL,6/9/2022,Series C,Germany,1200
Sonder,SF Bay Area,Travel,250,0.21,6/8/2022,Post-IPO,United States,839
Kavak,Sao Paulo,Transportation,150,NULL,6/8/2022,Series E,Brazil,1600
Truepill,SF Bay Area,Healthcare,150,0.15,6/8/2022,Series D,United States,255
iPrice Group,Kuala Lumpur,Retail,50,0.2,6/8/2022,Unknown,Malaysia,26
Memmo,Stockholm,Consumer,NULL,0.4,6/8/2022,Unknown,Sweden,24
Cazoo,London,Transportation,750,0.15,6/7/2022,Post-IPO,United Kingdom,2000
Cazoo,London,Transportation,750,0.15,6/7/2022,Post-IPO,United Kingdom,2000
Rupeek,Bengaluru,Finance,180,0.15,6/7/2022,Unknown,India,172
Lummo,Jakarta,Marketing,150,NULL,6/7/2022,Series C,Indonesia,149
Bird,Los Angeles,Transportation,138,0.23,6/7/2022,Post-IPO,United States,783
ID.me,Washington D.C.,Security,130,NULL,6/7/2022,Unknown,United States,275
KiwiCo,SF Bay Area,Retail,40,NULL,6/7/2022,Unknown,United States,11
Bond,SF Bay Area,Finance,NULL,NULL,6/7/2022,Series A,United States,42
CyberCube,SF Bay Area,Finance,NULL,NULL,6/7/2022,Series B,United States,35
Propzy,Ho Chi Minh City,Real Estate,NULL,0.5,6/7/2022,Series A,Vietnam,33
Clearco,Toronto,Finance,60,NULL,6/6/2022,Series C,Canada,681
Dutchie,Bend,Other,50,0.07,6/6/2022,Series D,United States,603
Clearco,Dublin,Finance,NULL,NULL,6/6/2022,Series C,Ireland,681
Deep Instinct,New York City,Security,NULL,0.1,6/6/2022,Series D,United States,259
Sendoso,SF Bay Area,Marketing,NULL,0.14,6/6/2022,Series C,United States,152
Eruditus,Mumbai,Education,40,NULL,6/4/2022,Series E,India,1200
Afterverse,Brasilia,Consumer,60,0.2,6/3/2022,Unknown,Brazil,NULL
Superhuman,SF Bay Area,Consumer,23,0.22,6/3/2022,Series C,United States,108
Food52,New York City,Food,21,0.15,6/3/2022,Acquired,United States,176
5B Solar,Sydney,Energy,NULL,0.25,6/3/2022,Series A,Australia,12
Clubhouse,SF Bay Area,Consumer,NULL,NULL,6/3/2022,Series C,United States,110
Tesla,Austin,Transportation,NULL,0.1,6/3/2022,Post-IPO,United States,20200
Carbon Health,SF Bay Area,Healthcare,250,0.08,6/2/2022,Series D,United States,522
Favo,Sao Paulo,Food,170,NULL,6/2/2022,Series A,Brazil,31
PolicyGenius,New York City,Finance,170,0.25,6/2/2022,Series E,United States,286
Yojak,Gurugram,Construction,140,0.5,6/2/2022,Seed,India,3
Envato,Melbourne,Marketing,100,NULL,6/2/2022,Unknown,Australia,NULL
Gemini,New York City,Crypto,100,0.1,6/2/2022,Unknown,United States,423
Stord,Atlanta,Logistics,59,0.08,6/2/2022,Series D,United States,325
Gather,SF Bay Area,Consumer,30,0.33,6/2/2022,Series B,United States,76
Shef,SF Bay Area,Food,29,NULL,6/2/2022,Series A,United States,28
IRL,SF Bay Area,Consumer,25,0.25,6/2/2022,Series C,United States,197
Esme Learning,Boston,Education,NULL,NULL,6/2/2022,Unknown,United States,37
Kaodim,Selangor,Consumer,NULL,1,6/2/2022,Series B,United States,17
Rain,Manama,Finance,NULL,NULL,6/2/2022,Series B,Bahrain,202
TomTom,Amsterdam,Transportation,500,0.1,6/1/2022,Post-IPO,Netherlands,NULL
Cybereason,Boston,Security,100,0.06,6/1/2022,Series F,United States,750
Udayy,Gurugram,Education,100,1,6/1/2022,Seed,India,2
2TM,Sao Paulo,Crypto,90,0.12,6/1/2022,Unknown,Brazil,250
Curve,London,Finance,65,0.1,6/1/2022,Series C,United Kingdom,182
Loom,SF Bay Area,Product,34,0.14,6/1/2022,Series C,United States,203
Skillshare,New York City,Education,31,NULL,6/1/2022,Unknown,United States,136
Impala,London,Travel,30,0.35,6/1/2022,Series B,United Kingdom,32
Eaze,SF Bay Area,Consumer,25,NULL,6/1/2022,Series D,United States,202
Side,SF Bay Area,Real Estate,NULL,0.1,6/1/2022,Unknown,United States,313
Truck It In,Karachi,Logistics,NULL,0.3,6/1/2022,Seed,Pakistan,17
Playtika,Tel Aviv,Consumer,250,0.06,5/31/2022,Post-IPO,Israel,NULL
Replicated,Los Angeles,Infrastructure,50,NULL,5/31/2022,Series C,United States,85
Tomo,Stamford,Finance,44,0.33,5/31/2022,Series A,United States,110
Getta,Tel Aviv,Transportation,30,NULL,5/31/2022,Series A,Israel,49
BookClub,Salt Lake City,Education,12,0.25,5/31/2022,Series A,United States,26
Cerebral,SF Bay Area,Healthcare,NULL,NULL,5/31/2022,Series C,United States,462
SWVL,Dubai,Transportation,400,0.32,5/30/2022,Post-IPO,United Arab Emirates,132
Mobile Premier League,Bengaluru,Consumer,100,0.1,5/30/2022,Series E,India,375
SumUp,Sao Paulo,Finance,100,0.03,5/30/2022,Unknown,Brazil,50
Tempus Ex,SF Bay Area,Data,NULL,NULL,5/30/2022,Unknown,United States,36
FrontRow,Bengaluru,Education,145,0.3,5/27/2022,Series A,India,17
Daloopa,New Delhi,Data,40,NULL,5/27/2022,Series A,India,23
Uncapped,London,Finance,29,0.26,5/27/2022,Unknown,United Kingdom,118
Akerna,Denver,Logistics,NULL,NULL,5/27/2022,Unknown,United States,46
Terminus,Atlanta,Marketing,NULL,NULL,5/27/2022,Series C,United States,120
Terminus,Atlanta,Marketing,NULL,NULL,5/27/2022,Unknown,United States,192
VTEX,Sao Paulo,Retail,200,0.13,5/26/2022,Post-IPO,Brazil,365
Bitso,Mexico City,Crypto,80,0.11,5/26/2022,Series C,Mexico,378
Foodpanda,Bucharest,Food,80,NULL,5/26/2022,Acquired,Romania,749
Dazn,London,Consumer,50,0.02,5/26/2022,Unknown,United Kingdom,NULL
Lacework,SF Bay Area,Security,300,0.2,5/25/2022,Series D,United States,1900
Bolt,SF Bay Area,Finance,240,0.27,5/25/2022,Series E,United States,1300
PeerStreet,Los Angeles,Finance,75,NULL,5/25/2022,Series C,United States,121
Kontist,Berlin,Finance,50,0.25,5/25/2022,Series B,Germany,53
Nuri,Berlin,Finance,45,0.2,5/25/2022,Series B,Germany,42
Coterie Insurance,Cincinnati,Finance,30,0.2,5/25/2022,Series B,United States,70
Airlift,Lahore,Logistics,NULL,0.31,5/25/2022,Series B,Pakistan,109
Getir,Istanbul,Food,NULL,0.14,5/25/2022,Series E,Turkey,1800
Zapp,London,Food,NULL,0.1,5/25/2022,NULL,United Kingdom,300
Gorillas,Berlin,Food,300,0.5,5/24/2022,Series C,Germany,1300
Zenius,Jakarta,Education,200,NULL,5/24/2022,Series B,Indonesia,20
The Zebra,Austin,Finance,40,NULL,5/24/2022,Series D,United States,256
Klarna,Stockholm,Finance,700,0.1,5/23/2022,Unknown,Sweden,3700
PayPal,SF Bay Area,Finance,83,NULL,5/23/2022,Post-IPO,United States,216
Buenbit,Buenos Aires,Crypto,80,0.45,5/23/2022,Series A,Argentina,11
BeyondMinds,Tel Aviv,Data,65,1,5/23/2022,Series A,Israel,16
ClickUp,San Diego,Other,60,0.07,5/23/2022,Series C,United States,537
Airtime,New York City,Consumer,30,0.2,5/23/2022,Series B,United States,33
Olist,Curitiba,Retail,NULL,NULL,5/23/2022,Series E,Brazil,322
MFine,Bengaluru,Healthcare,600,0.75,5/21/2022,Series C,India,97
Latch,New York City,Security,130,0.28,5/20/2022,Post-IPO,United States,342
Outside,Boulder,Media,87,0.15,5/20/2022,Series B,United States,174
Skillz,SF Bay Area,Consumer,70,0.1,5/20/2022,Post-IPO,United States,287
Cars24,Gurugram,Transportation,600,0.06,5/19/2022,Series G,India,1300
Vedantu,Bengaluru,Education,424,0.07,5/18/2022,Series E,India,292
Netflix,SF Bay Area,Media,150,0.01,5/17/2022,Post-IPO,United States,121900
Kry,Stockholm,Healthcare,100,0.1,5/17/2022,Series D,Sweden,568
Picsart,Miami,Consumer,90,0.08,5/17/2022,Series C,United States,195
Zak,Sao Paulo,Food,100,0.4,5/16/2022,Series A,United States,29
Zulily,Seattle,Retail,NULL,NULL,5/16/2022,Acquired,United States,194
AliExpress Russia,Moscow,Retail,400,0.4,5/14/2022,Acquired,Russia,60
Thirty Madison,New York City,Healthcare,24,NULL,5/14/2022,Acquired,United States,209
CommonBond,New York City,Finance,22,NULL,5/13/2022,Series D,United States,130
Subspace,Los Angeles,Infrastructure,NULL,1,5/13/2022,Series B,United States,NULL
Zwift,Los Angeles,Fitness,150,NULL,5/12/2022,Series C,United States,619
Section4,New York City,Education,32,NULL,5/12/2022,Series A,United States,37
Tripwire,Portland,Security,NULL,NULL,5/12/2022,Acquired,United States,29
DataRobot,Boston,Data,70,0.07,5/11/2022,Series G,United States,1000
Carvana,Phoenix,,2500,0.12,5/10/2022,Post-IPO,United States,1600
Doma,SF Bay Area,Finance,310,0.15,5/10/2022,Post-IPO,United States,679
Pollen,London,Travel,200,0.33,5/10/2022,Series C,United Kingdom,238
Latch,New York City,Security,30,0.06,5/10/2022,Post-IPO,United States,342
Meero,Paris,Other,350,0.5,5/9/2022,Series C,France,293
Vroom,New York City,Transportation,270,0.14,5/9/2022,Post-IPO,United States,1300
Reef,Miami,Transportation,750,0.05,5/6/2022,Unknown,United States,1500
divvyDOSE,Davenport,Healthcare,62,NULL,5/6/2022,Acquired,United States,0
Vedantu,Bengaluru,Education,200,0.03,5/5/2022,Series E,India,292
Mural,SF Bay Area,Product,90,0.1,5/5/2022,Series C,United States,192
On Deck,SF Bay Area,Education,72,0.25,5/5/2022,Series A,United States,20
SEND,Sydney,Food,300,1,5/4/2022,Seed,Australia,3
Colossus,Boston,Energy,97,NULL,5/4/2022,Series A,United States,41
Cameo,Chicago,Consumer,87,0.25,5/4/2022,Unknown,United States,165
Mainstreet,SF Bay Area,Finance,45,0.3,5/4/2022,Series A,United States,64
Ideoclick,Seattle,Retail,40,NULL,5/4/2022,Series A,United States,7
Vise,New York City,Finance,25,NULL,5/4/2022,Series C,United States,128
Bizpay,Sydney,Finance,NULL,0.3,5/4/2022,Unknown,Australia,45
Thrasio,Boston,Retail,NULL,NULL,5/2/2022,Unknown,United States,3400
Avo,Tel Aviv,Food,500,0.67,5/1/2022,Series B,Israel,45
Noom,New York City,Healthcare,495,NULL,4/29/2022,Series F,United States,657
Domestika,SF Bay Area,Education,150,0.19,4/28/2022,Series D,United States,130
Netflix,SF Bay Area,Media,25,NULL,4/28/2022,Post-IPO,United States,121900
Wahoo Fitness,Atlanta,Fitness,50,NULL,4/27/2022,Unknown,United States,NULL
Robinhood,SF Bay Area,Finance,340,0.09,4/26/2022,Post-IPO,United States,5600
Bonsai,Toronto,Retail,29,0.34,4/26/2022,Series A,Canada,27
Sigfox,Toulouse,Other,64,NULL,4/25/2022,Acquired,France,277
Clyde,New York City,Marketing,22,NULL,4/25/2022,Series B,United States,58
Xiaohongshu,Shanghai,Consumer,180,0.09,4/21/2022,Series E,China,917
Facily,Sao Paulo,Retail,260,0.3,4/20/2022,Series D,Brazil,502
Lemonade,New York City,Finance,52,NULL,4/20/2022,Post-IPO,United States,481
Blend,SF Bay Area,Finance,200,0.1,4/19/2022,Post-IPO,United States,665
QuintoAndar,Sao Paulo,Real Estate,160,0.04,4/19/2022,Series E,Brazil,755
Loft,Sao Paulo,Real Estate,159,NULL,4/19/2022,Unknown,Brazil,788
Better.com,New York City,Real Estate,NULL,NULL,4/19/2022,Unknown,United States,905
Automox,Boulder,Infrastructure,NULL,0.11,4/18/2022,Series C,United States,152
Humble,SF Bay Area,Media,10,NULL,4/15/2022,Acquired,United States,4
Halcyon Health,New York City,Healthcare,NULL,1,4/15/2022,Unknown,United States,2
Ahead,SF Bay Area,Healthcare,44,1,4/14/2022,Unknown,United States,9
Truepill,SF Bay Area,Healthcare,NULL,NULL,4/14/2022,Series D,United States,255
Rad Power Bikes,Seattle,Transportation,100,0.14,4/12/2022,Series D,United States,329
Meesho,Bengaluru,Retail,150,NULL,4/11/2022,Series F,India,1100
Food52,New York City,Food,20,0.1,4/8/2022,Acquired,United States,176
Unacademy,Bengaluru,Education,1000,0.17,4/7/2022,Series H,India,838
Goodfood,Calgary,Food,70,NULL,4/7/2022,Post-IPO,Canada,16
Workrise,Austin,Energy,450,NULL,4/5/2022,Series E,United States,752
Fast,SF Bay Area,Finance,NULL,1,4/5/2022,Series B,United States,124
BitMEX,Non-U.S.,Crypto,75,0.25,4/4/2022,Seed,Seychelles,0
Legible,Vancouver,Media,23,0.38,4/4/2022,Post-IPO,Canada,3
Lightico,Tel Aviv,Finance,20,NULL,3/31/2022,Series B,Israel,42
Sea,Bengaluru,Retail,350,NULL,3/30/2022,Post-IPO,India,8600
Rasa,Berlin,Data,59,0.4,3/30/2022,Series B,Germany,40
Gopuff,Philadelphia,Food,450,0.03,3/29/2022,Series H,United States,3400
Thinkific,Vancouver,Education,100,0.2,3/29/2022,Post-IPO,Canada,22
Furlenco,Bengaluru,Retail,180,NULL,3/26/2022,Series D,India,228
Grove Collaborative,SF Bay Area,Retail,NULL,0.17,3/19/2022,Series E,United States,474
Storytel,Stockholm,Media,100,0.1,3/17/2022,Post-IPO,Sweden,275
Curology,SF Bay Area,Healthcare,150,NULL,3/16/2022,Series D,United States,19
Trell,Bengaluru,Retail,300,0.5,3/15/2022,Series B,India,62
Knock,New York City,Real Estate,115,0.46,3/15/2022,Unknown,United States,654
Talis Biomedical,SF Bay Area,Healthcare,NULL,0.25,3/15/2022,Post-IPO,United States,8
Sezzle,Minneapolis,Finance,NULL,0.2,3/10/2022,Post-IPO,United States,301
Better.com,New York City,Real Estate,3000,0.33,3/8/2022,Unknown,United States,905
Adaptive Biotechnologies,Seattle,Healthcare,100,0.12,3/8/2022,Post-IPO,United States,406
Hyperscience,New York City,Data,100,0.25,3/3/2022,Series E,United States,289
WeDoctor,Non-U.S.,Healthcare,500,NULL,3/2/2022,Series F,China,1400
Wish,SF Bay Area,Retail,190,0.15,3/1/2022,Post-IPO,United States,1600
iFit,Logan,Fitness,NULL,NULL,2/25/2022,Private Equity,United States,200
OKCredit,New Delhi,Finance,30,NULL,2/24/2022,Series B,India,85
Lido,Mumbai,Education,150,NULL,2/21/2022,Series A,India,24
Virgin Hyperloop,Los Angeles,Transportation,111,0.5,2/21/2022,Unknown,United States,368
Trustly,Stockholm,Finance,120,NULL,2/17/2022,Acquired,Sweden,28
Liv Up,Sao Paulo,Food,100,0.15,2/16/2022,Series D,Brazil,118
Homie,Salt Lake City,Real Estate,119,0.29,2/14/2022,Series B,United States,35
Hopin,London,Other,138,0.12,2/10/2022,Series D,United States,1000
Daily Harvest,New York City,Food,60,0.2,2/10/2022,Series D,United States,120
Peloton,New York City,Fitness,2800,0.2,2/8/2022,Post-IPO,United States,1900
Defined.ai,Seattle,Data,NULL,NULL,2/7/2022,Series B,United States,78
Rhino,New York City,Real Estate,57,0.2,2/3/2022,Unknown,United States,133
Gopuff,Philadelphia,Food,100,NULL,1/26/2022,Series H,United States,3400
Glossier,New York City,Consumer,80,0.33,1/26/2022,Series E,United States,266
Root Insurance,Columbus,Finance,330,NULL,1/20/2022,Post-IPO,United States,527
Spin,SF Bay Area,Transportation,NULL,0.25,1/8/2022,Acquired,United States,8
Delivery Hero,Berlin,Food,300,NULL,12/22/2021,Post-IPO,Germany,8300
iFit,Logan,Fitness,NULL,NULL,12/8/2021,Private Equity,United States,200
Better.com,New York City,Real Estate,900,0.09,12/1/2021,Unknown,United States,905
BitTitan,Seattle,Data,70,0.27,11/18/2021,Acquired,United States,46
Zillow,Seattle,Real Estate,2000,0.25,11/2/2021,Post-IPO,United States,97
InVision,New York City,Product,22,NULL,10/5/2021,Unknown,United States,356
Ozy Media,SF Bay Area,Media,NULL,1,10/1/2021,Series C,United States,70
Zymergen,SF Bay Area,Other,120,NULL,9/23/2021,Post-IPO,United States,974
Imperfect Foods,SF Bay Area,Food,NULL,NULL,9/22/2021,Series D,United States,229
Genius,New York City,Consumer,NULL,NULL,9/15/2021,Acquired,United States,78
Treehouse,Portland,Education,41,0.9,9/14/2021,Series B,United States,12
Casper,New York City,Retail,NULL,NULL,9/14/2021,Post-IPO,United States,339
Tanium,Seattle,Security,30,NULL,8/30/2021,Unknown,United States,1000
Flockjay,SF Bay Area,Education,37,0.5,8/24/2021,Series A,United States,14
Bytedance,Shanghai,Consumer,1800,NULL,8/5/2021,Unknown,China,9400
Pagarbook,Bengaluru,HR,80,NULL,7/26/2021,Series A,India,17
Katerra,SF Bay Area,Construction,2434,1,6/1/2021,Unknown,United States,1600
SumUp,London,Finance,NULL,NULL,5/5/2021,Unknown,United Kingdom,53
Lambda School,SF Bay Area,Education,65,NULL,4/29/2021,Series C,United States,122
Madefire,SF Bay Area,Media,NULL,1,4/29/2021,Series B,United States,16
Patreon,SF Bay Area,Media,36,NULL,4/26/2021,Series D,United States,165
New Relic,SF Bay Area,Infrastructure,160,0.07,4/6/2021,Post-IPO,United States,214.5
Medium,SF Bay Area,Media,NULL,NULL,3/24/2021,Series C,United States,132
HuffPo,New York City,Media,47,NULL,3/9/2021,Acquired,United States,37
Subspace,Los Angeles,Infrastructure,NULL,NULL,3/3/2021,Series B,United States,NULL
Clumio,SF Bay Area,Data,NULL,NULL,3/1/2021,Series C,United States,186
DJI,SF Bay Area,Consumer,NULL,NULL,2/24/2021,Unknown,United States,105
Ninjacart,Bengaluru,Food,200,NULL,2/23/2021,Unknown,India,194
Bounce,Bengaluru,Transportation,200,0.4,2/22/2021,Series D,India,214.2
ThredUp,Chicago,Retail,243,NULL,2/9/2021,Series F,United States,305
Indigo,Boston,Other,80,NULL,2/9/2021,Series F,United States,1200
Limelight,New York City,Recruiting,13,1,2/4/2021,Unknown,United States,NULL
Quandoo,Berlin,Food,87,0.2,2/3/2021,Acquired,Germany,39
Hubba,Toronto,Retail,45,1,2/1/2021,Series B,Canada,61
Bytedance,Mumbai,Consumer,1800,NULL,1/27/2021,Unknown,India,7400
Privitar,London,Data,20,NULL,1/27/2021,Series C,United Kingdom,150
Shutterfly,SF Bay Area,Retail,800,NULL,1/25/2021,Acquired,United States,50
Postmates,SF Bay Area,Food,180,0.15,1/23/2021,Acquired,United States,763
Instacart,SF Bay Area,Food,1877,NULL,1/21/2021,Unknown,United States,2400
Pocketmath,Singapore,Marketing,21,1,1/20/2021,Unknown,Singapore,20
Dropbox,SF Bay Area,Other,315,0.15,1/13/2021,Post-IPO,United States,1700
Aura Financial,SF Bay Area,Finance,NULL,1,1/11/2021,Unknown,United States,584
Simple,Portland,Finance,NULL,1,1/7/2021,Acquired,United States,15
WhiteHat Jr,Mumbai,Education,1800,NULL,1/6/2021,Acquired,India,11
Pulse Secure,SF Bay Area,Security,78,NULL,12/23/2020,Acquired,United States,NULL
Breather,Montreal,Real Estate,120,0.8,12/16/2020,Series D,Canada,131
Actifio,Boston,Data,54,NULL,12/16/2020,Acquired,United States,352
OYO,Gurugram,Travel,600,NULL,12/8/2020,Series F,India,3200
Zinier,SF Bay Area,HR,NULL,NULL,11/25/2020,Series C,United States,120
Aya,Toronto,Finance,5,0.25,11/19/2020,Seed,United States,3
Domio,New York City,Real Estate,NULL,0.5,11/18/2020,Series B,United States,116
Bridge Connector,Nashville,Healthcare,154,1,11/17/2020,Series B,United States,45
Tidepool,SF Bay Area,Healthcare,18,0.4,11/17/2020,Unknown,United States,NULL
Igenous,Seattle,Data,NULL,NULL,11/17/2020,Series C,United States,67
Scoop,SF Bay Area,Transportation,NULL,NULL,11/17/2020,Series C,United States,95
Worksmith,Austin,Retail,30,0.5,11/9/2020,Unknown,United States,3.8
Rubica,Seattle,Security,NULL,1,11/5/2020,Series A,United States,14
Bossa Nova,SF Bay Area,Retail,NULL,0.5,11/2/2020,Unknown,United States,101.6
LivePerson,New York City,Support,30,NULL,11/1/2020,Post-IPO,United States,42
Remedy,Austin,Healthcare,82,NULL,10/29/2020,Series A,United States,12.5
Knotel,New York City,Real Estate,20,0.08,10/29/2020,Series C,United States,560
Cheetah,SF Bay Area,Food,NULL,NULL,10/25/2020,Series B,United States,67
CodeCombat,SF Bay Area,Education,8,NULL,10/23/2020,Series A,United States,8
Quibi,Los Angeles,Media,NULL,1,10/21/2020,Private Equity,United States,1800
Zomato,Jakarta,Food,NULL,NULL,10/20/2020,Series J,Indonesia,914
GetYourGuide,Berlin,Travel,90,0.17,10/14/2020,Series E,Germany,656
OLX India,New Delhi,Marketing,250,NULL,10/10/2020,Acquired,India,28
Chef,Seattle,Infrastructure,NULL,NULL,10/8/2020,Acquired,United States,105
Alto Pharmacy,SF Bay Area,Healthcare,47,0.06,9/29/2020,Series D,United States,356
TheWrap,Los Angeles,Media,NULL,NULL,9/29/2020,Series B,United States,3.5
HumanForest,London,Transportation,NULL,NULL,9/25/2020,Private Equity,United Kingdom,1.54
WeWork ,Shenzen,Real Estate,NULL,NULL,9/23/2020,Series H,China,19500
Air,New York City,Marketing,NULL,0.16,9/16/2020,Series A,United States,18
NS8,Las Vegas,Data,240,0.95,9/11/2020,Series A,United States,157.9
Bleacher Report,London,Media,20,NULL,9/11/2020,Acquired,United Kingdom,40.5
HubHaus,SF Bay Area,Real Estate,NULL,1,9/11/2020,Series A,United States,13.4
Waze,SF Bay Area,Transportation,30,0.054,9/9/2020,Acquired,United States,67
Ouster,SF Bay Area,Transportation,NULL,0.1,9/8/2020,Series B,United States,132
Swing Education,SF Bay Area,Education,NULL,NULL,9/5/2020,Series B,United States,22.8
Akerna,Denver,Logistics,NULL,NULL,9/2/2020,Post-IPO,United States,NULL
Awok,Dubai,Retail,NULL,1,9/2/2020,Series A,United Arab Emirates,30
Big Fish Games,Seattle,Media,250,NULL,9/1/2020,Acquired,United States,95.2
GoBear,Singapore,Finance,22,0.11,9/1/2020,Unknown,Singapore,97
MakeMyTrip,New Delhi,Travel,350,0.1,8/31/2020,Post-IPO,India,548
Salesforce,SF Bay Area,Sales,1000,0.0185,8/26/2020,Post-IPO,United States,65.4
kununu,Boston,Recruiting,NULL,NULL,8/26/2020,Acquired,United States,NULL
Superloop,Brisbane,Infrastructure,30,NULL,8/24/2020,Post-IPO,Australia,36
Spaces,Los Angeles,Media,NULL,NULL,8/24/2020,Acquired,United States,9.5
StreamSets,SF Bay Area,Data,NULL,NULL,8/20/2020,Series C,United States,76.2
Docly,London,Healthcare,8,0.8,8/19/2020,Seed,United Kingdom,15.5
Mapify,Berlin,Travel,NULL,NULL,8/19/2020,Seed,Germany,1.3
Lumina Networks,SF Bay Area,Infrastructure,NULL,1,8/18/2020,Series A,United States,10
DJI,Shenzen,Consumer,NULL,NULL,8/17/2020,Unknown,China,105
Shopify,Ottawa,Retail,30,NULL,8/14/2020,Post-IPO,Canada,122.3
InVision,New York City,Product,NULL,NULL,8/14/2020,Series F,United States,350.2
HopSkipDrive,Los Angeles,Transportation,NULL,NULL,8/12/2020,Unknown,United States,45
Mozilla,SF Bay Area,Consumer,250,0.25,8/11/2020,Unknown,United States,2.3
Eatsy,Singapore,Food,20,1,8/8/2020,Seed,Singapore,0.9755
Glossier,New York City,Retail,150,0.38,8/7/2020,Series D,United States,186.4
The Appraisal Lane,Montevideo,Transportation,NULL,NULL,8/7/2020,Seed,Uruguay,1.8
Thriver,Toronto,Food,75,0.5,8/6/2020,Series B,Canada,53
Vesta,Atlanta,Sales,56,NULL,8/5/2020,Unknown,United States,20
Booking.com,Amsterdam,Travel,4375,0.25,7/30/2020,Acquired,Netherlands,NULL
Buy.com / Rakuten,SF Bay Area,Retail,87,1,7/30/2020,Acquired,United States,42.4
tZero,New York City,Finance,NULL,NULL,7/30/2020,Acquired,United States,472
Pared,SF Bay Area,Food,NULL,NULL,7/29/2020,Unknown,United States,13
Procore,Los Angeles,Construction,180,0.09,7/28/2020,Unknown,United States,649
Swiggy,Bengaluru,Food,350,0.05,7/27/2020,Series I,India,1600
Zeitgold,Berlin,Finance,75,0.72,7/27/2020,Series B,Germany,60.18
Perkbox,London,HR,NULL,NULL,7/27/2020,Unknown,United Kingdom,29.7
Checkr,SF Bay Area,HR,64,0.12,7/23/2020,Series D,United States,309
Sorabel,Jakarta,Retail,NULL,1,7/23/2020,Series B,Indonesia,27
LinkedIn,SF Bay Area,Recruiting,960,0.06,7/21/2020,Acquired,United States,154
Lighter Capital,Seattle,Finance,22,0.49,7/20/2020,Series B,United States,15
Curefit,Bengaluru,Fitness,120,NULL,7/17/2020,Series D,India,404.6
Snaptravel,Toronto,Travel,NULL,NULL,7/16/2020,Series A,Canada,22.4
Optimizely,SF Bay Area,Marketing,60,0.15,7/15/2020,Series D,United States,251.2
Skyscanner,Edinburgh,Travel,300,0.2,7/14/2020,Acquired,United Kingdom,197.2
Vox Media,Washington D.C.,Media,NULL,NULL,7/14/2020,Series F,United States,307.6
Yelp,SF Bay Area,Consumer,63,NULL,7/13/2020,Post-IPO,United States,56
Bizongo,Mumbai,Logistics,140,NULL,7/10/2020,Series C,India,70
Zilingo,Singapore,Retail,100,0.12,7/9/2020,Series D,Singapore,307
PaySense,Mumbai,Finance,40,NULL,7/9/2020,Acquired,India,25.6
Funding Circle,SF Bay Area,Finance,85,NULL,7/8/2020,Post-IPO,United States,746
OnDeck,New York City,Finance,NULL,NULL,7/8/2020,Post-IPO,United States,1200
The Wing,New York City,Real Estate,56,NULL,7/1/2020,Series C,United States,117
Sharethrough,SF Bay Area,Marketing,18,NULL,7/1/2020,Series D,United States,38
Kongregate,SF Bay Area,Media,12,NULL,7/1/2020,Series C,United States,19
Havenly,Denver,Consumer,5,NULL,7/1/2020,Series C,United States,57.8
G2,Chicago,Marketing,17,0.05,6/30/2020,Series C,United States,100.8
Hired,SF Bay Area,Recruiting,NULL,NULL,6/30/2020,Series D,United States,132
Katerra,SF Bay Area,Construction,400,0.07,6/29/2020,Series E,United States,1300
Bounce,Bengaluru,Transportation,130,0.22,6/29/2020,Series D,India,214.2
Argo AI,Munich,Transportation,100,NULL,6/29/2020,Unknown,Germany,3600
Bossa Nova,SF Bay Area,Retail,61,NULL,6/29/2020,Unknown,United States,101.6
New Relic,Portland,Infrastructure,20,NULL,6/29/2020,Post-IPO,United States,214.5
Engine eCommerce,Fayetteville,Retail,NULL,1,6/28/2020,Unknown,United States,4
Byton,SF Bay Area,Transportation,NULL,NULL,6/27/2020,Series C,United States,1200
Sprinklr,New York City,Support,NULL,NULL,6/25/2020,Series F,United States,228
GoDaddy,Austin,Marketing,451,0.06,6/24/2020,Post-IPO,United States,NULL
Sonos,New York City,Consumer,174,0.12,6/24/2020,Post-IPO,United States,455.2
OYO,Dallas,Travel,NULL,NULL,6/24/2020,Series F,United States,3200
Gojek,Jakarta,Transportation,430,0.09,6/23/2020,Series F,Indonesia,4800
ScaleFactor,Austin,Finance,90,0.9,6/23/2020,Series C,United States,103
Dark,SF Bay Area,Product,6,1,6/23/2020,Seed,United States,3
Intuit,SF Bay Area,Finance,715,0.07,6/22/2020,Post-IPO,United States,18
WeWork,London,Real Estate,200,NULL,6/19/2020,Series H,United Kingdom,19500
Atlas Obscura ,New York City,Media,15,NULL,6/18/2020,Series B,United States,32
Navi,Bengaluru,Finance,40,0.25,6/17/2020,Private Equity,India,582
PaisaBazaar,Gurugram,Finance,1500,0.5,6/16/2020,Series G,India,496
Grab,Singapore,Transportation,360,0.05,6/16/2020,Series I,Singapore,9900
Splunk,SF Bay Area,Data,70,0.01,6/16/2020,Post-IPO,United States,40
Redox,Madison,Healthcare,44,0.25,6/16/2020,Series C,United States,50
Conga,Denver,Data,NULL,0.11,6/15/2020,Acquired,United States,117
Stockwell AI,SF Bay Area,Retail,NULL,1,6/15/2020,Series B,United States,10
Uber,Amsterdam,Transportation,225,0.25,6/12/2020,Post-IPO,Netherlands,24700
SynapseFI,SF Bay Area,Finance,63,0.48,6/12/2020,Series B,United States,50
ScaleFocus,Sofia,Infrastructure,120,0.1,6/11/2020,Unknown,Bulgaria,NULL
Branch,New York City,Retail,3,0.27,6/11/2020,Seed,United States,2
Her Campus Media,Boston,Media,10,0.18,6/10/2020,Unknown,United States,NULL
Integrate.ai,Toronto,Support,26,NULL,6/9/2020,Unknown,Canada,39.6
The Athletic,SF Bay Area,Media,46,0.08,6/5/2020,Series D,United States,139
Ethos Life,SF Bay Area,Finance,18,0.14,6/5/2020,Series C,United States,106
Lastline,SF Bay Area,Security,50,0.4,6/4/2020,Series C,United States,52
Builder,Los Angeles,Product,39,0.14,6/4/2020,Series A,United States,29
Outdoorsy,SF Bay Area,Transportation,NULL,NULL,6/4/2020,Series C,United States,75
Monzo,London,Finance,120,0.08,6/3/2020,Series F,United Kingdom,324
Kitty Hawk,SF Bay Area,Aerospace,70,NULL,6/3/2020,Unknown,United States,1
SpotHero,Chicago,Transportation,40,0.21,6/3/2020,Series D,United States,117
Credit Sesame,SF Bay Area,Finance,22,0.14,6/3/2020,Series F,United States,120
Circ,Berlin,Transportation,100,NULL,6/2/2020,Acquired,Germany,55
Rivian,Detroit,Transportation,40,0.02,6/2/2020,Private Equity,United States,3100
Fundbox,SF Bay Area,Finance,14,0.15,6/2/2020,Series C,United States,453
Descartes Labs,Santa Fe,Data,12,0.16,6/2/2020,Series B,United States,58
MakerBot,New York City,Consumer,12,NULL,6/2/2020,Acquired,United States,10
Stitch Fix,SF Bay Area,Retail,1400,0.18,6/1/2020,Post-IPO,United States,79
MakeMyTrip,Gurugram,Travel,350,0.1,6/1/2020,Post-IPO,India,548
CrowdStreet,Portland,Real Estate,24,0.22,6/1/2020,Series C,United States,24
Brex,SF Bay Area,Finance,62,0.15,5/29/2020,Series C,United States,732
Loftium,Seattle,Real Estate,32,0.6,5/29/2020,Series A,United States,17
BookMyShow,Mumbai,Consumer,270,0.18,5/28/2020,Unknown,India,224
TrueCar,Los Angeles,Transportation,219,0.3,5/28/2020,Post-IPO,United States,340
StubHub,SF Bay Area,Consumer,200,NULL,5/28/2020,Acquired,United States,19
Culture Amp,Melbourne,HR,36,0.08,5/28/2020,Series E,Australia,157
The Sill,New York City,Retail,20,0.25,5/28/2020,Series A,United States,7
Instructure,Salt Lake City,Education,150,0.12,5/27/2020,Acquired,United States,89
Ebanx,Curitiba,Finance,62,0.08,5/27/2020,Unknown,Brazil,30
Uber,Bengaluru,Transportation,600,0.23,5/26/2020,Post-IPO,India,24700
Bluprint,Denver,Education,137,1,5/26/2020,Acquired,United States,108
Acorns,Portland,Finance,50,NULL,5/26/2020,Unknown,United States,207
CarDekho,Gurugram,Transportation,200,NULL,5/25/2020,Series D,India,247
Teamwork,Cork,Other,21,NULL,5/22/2020,Unknown,Ireland,NULL
Cvent,Washington D.C.,Marketing,400,0.1,5/21/2020,Acquired,United States,146
PickYourTrail,Chennai,Travel,70,0.35,5/21/2020,Series A,India,3
Glitch,New York City,Product,18,0.36,5/21/2020,Series A,United States,30
Kapten / Free Now,Paris,Transportation,NULL,NULL,5/21/2020,Acquired,France,100
Ola,Bengaluru,Transportation,1400,0.35,5/20/2020,Series J,India,3800
Samsara,SF Bay Area,Logistics,300,0.18,5/20/2020,Series F,United States,530
Stay Alfred,Spokane,Travel,221,1,5/20/2020,Series B,United States,62
SoFi,SF Bay Area,Finance,112,0.07,5/20/2020,Private Equity,United States,2500
ShareChat,Bengaluru,Marketing,101,0.25,5/20/2020,Series D,India,222
Intercom,SF Bay Area,Support,39,0.06,5/20/2020,Series D,United States,240
Livspace,Bengaluru,Retail,450,0.15,5/19/2020,Series D,India,157
Pollen,London,Travel,69,0.31,5/19/2020,Series B,United Kingdom,88
Dotscience,London,Product,10,1,5/19/2020,Unknown,United Kingdom,NULL
Divvy,Salt Lake City,Finance,NULL,NULL,5/19/2020,Series C,United States,252
Uber,SF Bay Area,Transportation,3000,0.13,5/18/2020,Post-IPO,United States,24700
Agoda,Singapore,Travel,1500,0.25,5/18/2020,Acquired,Singapore,NULL
Swiggy,Bengaluru,Food,1100,0.14,5/18/2020,Series I,India,1600
WeWork,New Delhi,Real Estate,100,0.2,5/18/2020,Series H,India,19500
Rubrik,SF Bay Area,Infrastructure,57,NULL,5/18/2020,Series E,United States,553
Intapp,SF Bay Area,Legal,45,0.05,5/18/2020,Private Equity,United States,NULL
Checkmarx,Tel Aviv,Security,NULL,NULL,5/18/2020,Acquired,Israel,92
Datera,SF Bay Area,Infrastructure,NULL,0.1,5/18/2020,Series C,United States,63
Magicbricks,Noida,Real Estate,250,NULL,5/17/2020,Unknown,India,300
Zomato,Gurugram,Food,520,0.13,5/15/2020,Series J,India,914
Lendingkart,Ahmedabad,Finance,500,0.5,5/15/2020,Unknown,India,200
Lattice,SF Bay Area,HR,16,0.1,5/15/2020,Series C,United States,49
Masse,New York City,Retail,NULL,1,5/15/2020,Seed,United States,3
Cruise,SF Bay Area,Transportation,150,0.08,5/14/2020,Acquired,United States,5300
Quartz,New York City,Media,80,0.4,5/14/2020,Acquired,United States,NULL
Integral Ad Science,New York City,Marketing,70,0.1,5/14/2020,Acquired,United States,116
Ridecell,SF Bay Area,Transportation,35,0.15,5/14/2020,Series B,United States,73
Veem,SF Bay Area,Finance,30,NULL,5/14/2020,Unknown,United States,69
Sift,SF Bay Area,Security,NULL,NULL,5/14/2020,Series D,United States,106
Workfront,Salt Lake City,Marketing,NULL,NULL,5/14/2020,Series C,United States,375
Deliv,SF Bay Area,Retail,669,1,5/13/2020,Series C,United States,80
Mercos,Joinville,Sales,51,0.4,5/13/2020,Unknown,Brazil,2
Kickstarter,New York City,Finance,25,0.18,5/13/2020,Unknown,United States,10
Intersect,Toronto,Product,19,0.11,5/13/2020,Acquired,Canada,NULL
Mode Analytics,SF Bay Area,Data,17,NULL,5/13/2020,Series C,United States,46
BetterCloud,New York City,Security,NULL,NULL,5/13/2020,Series E,United States,111
WeFit,Hanoi,Fitness,NULL,1,5/13/2020,Seed,Vietnam,1
Zymergen ,SF Bay Area,Other,NULL,0.1,5/13/2020,Series C,United States,574
Stone,Sao Paulo,Finance,1300,0.2,5/12/2020,Post-IPO,Brazil,NULL
Zeus Living,SF Bay Area,Real Estate,73,0.5,5/12/2020,Series B,United States,79
Mixpanel,SF Bay Area,Data,65,0.19,5/12/2020,Series B,United States,77
Hireology,Chicago,Recruiting,36,0.17,5/12/2020,Series D,United States,52
Top Hat,Toronto,Education,16,0.03,5/12/2020,Series D,Canada,104
Datto,Norwalk,Infrastructure,NULL,NULL,5/12/2020,Acquired,United States,100
Petal,New York City,Finance,NULL,NULL,5/12/2020,Series B,United States,47
Revolut,London,Finance,60,0.03,5/11/2020,Series D,United Kingdom,837
Cadre,New York City,Real Estate,28,0.25,5/11/2020,Series C,United States,133
N26,New York City,Finance,9,0.01,5/8/2020,Series D,United States,782
Jump,New York City,Transportation,500,1,5/7/2020,Acquired,United States,11
Glassdoor,SF Bay Area,Recruiting,300,0.3,5/7/2020,Acquired,United States,204
Numbrs,Zurich,Finance,62,0.5,5/7/2020,Series B,Switzerland,78
Flywire,Boston,Finance,60,0.12,5/7/2020,Series E,United States,263
SalesLoft,Atlanta,Sales,55,NULL,5/7/2020,Series D,United States,145
Tally,SF Bay Area,Finance,28,0.23,5/7/2020,Series C,United States,92
Airy Rooms,Jakarta,Travel,NULL,1,5/7/2020,Unknown,Indonesia,NULL
Uber,SF Bay Area,Transportation,3700,0.14,5/6/2020,Post-IPO,United States,24700
Validity,Boston,Data,130,0.33,5/6/2020,Private Equity,United States,NULL
Flatiron School,New York City,Education,100,NULL,5/6/2020,Acquired,United States,15
Rubicon Project,Los Angeles,Marketing,50,0.08,5/6/2020,Post-IPO,United States,60
Segment,SF Bay Area,Data,50,0.1,5/6/2020,Series D,United States,283
OPay,Lagos,Finance,NULL,0.7,5/6/2020,Series B,Nigeria,170
ThoughtSpot,SF Bay Area,Data,NULL,NULL,5/6/2020,Series E,United States,743
Airbnb,SF Bay Area,Travel,1900,0.25,5/5/2020,Private Equity,United States,5400
Juul,SF Bay Area,Consumer,900,0.3,5/5/2020,Unknown,United States,1500
Andela,New York City,Recruiting,135,0.1,5/5/2020,Series D,United States,181
Stack Overflow,New York City,Recruiting,40,0.15,5/5/2020,Series D,United States,68
Pipedrive,Tallinn,Sales,31,NULL,5/5/2020,Series C,Estonia,90
Workable,Boston,Recruiting,25,0.1,5/5/2020,Series C,United States,84
Cloudera,SF Bay Area,Infrastructure,NULL,NULL,5/5/2020,Post-IPO,United States,1000
Handshake,SF Bay Area,Recruiting,NULL,NULL,5/5/2020,Series C,United States,74
League,Toronto,Healthcare,NULL,NULL,5/5/2020,Unknown,Canada,76
CureFit,Bengaluru,Fitness,800,0.16,5/4/2020,Series D,India,404
Careem,Dubai,Transportation,536,0.31,5/4/2020,Acquired,United Arab Emirates,771
Oriente,Hong Kong,Finance,400,0.2,5/4/2020,Series B,Hong Kong,175
Veriff,Tallinn,Security,63,0.21,5/4/2020,Series A,Estonia,7
Element AI,Montreal,Other,62,0.15,5/4/2020,Series B,Canada,257
Deputy,Sydney,HR,60,0.3,5/4/2020,Series B,Australia,106
Loopio,Toronto,Sales,11,0.08,5/4/2020,Series A,Canada,11
Castlight Health,SF Bay Area,Healthcare,NULL,0.13,5/4/2020,Post-IPO,United States,184
Trivago,Dusseldorf,Travel,NULL,NULL,5/4/2020,Acquired,Germany,55
Trax,Tel Aviv,Retail,120,0.1,5/3/2020,Series D,Israel,386
LiveTiles,New York City,Other,50,NULL,5/3/2020,Post-IPO,United States,46
OYO,London,Travel,150,NULL,5/1/2020,Series F,United Kingdom,2400
Namely,New York City,HR,110,0.4,5/1/2020,Series E,United States,217
Culture Trip,London,Media,95,0.32,5/1/2020,Series B,United Kingdom,102
Sandbox VR,SF Bay Area,Consumer,80,0.8,5/1/2020,Series A,United States,82
Virtudent,Boston,Healthcare,70,NULL,5/1/2020,Series A,United States,10
Monese,Lisbon,Finance,35,NULL,5/1/2020,Unknown,Portugal,80
TheSkimm,New York City,Media,26,0.2,5/1/2020,Series C,United States,28
Automatic,SF Bay Area,Transportation,NULL,1,5/1/2020,Acquired,United States,24
Flynote,Bengaluru,Travel,NULL,NULL,5/1/2020,Seed,India,1
Bullhorn,Boston,Sales,100,NULL,4/30/2020,Acquired,United States,32
Care.com,Boston,Consumer,81,NULL,4/30/2020,Acquired,United States,156
AirMap,Los Angeles,Aerospace,NULL,NULL,4/30/2020,Unknown,United States,75
Cohesity,SF Bay Area,Data,NULL,NULL,4/30/2020,Series E,United States,660
Fandom,SF Bay Area,Media,NULL,0.14,4/30/2020,Series E,United States,145
PicoBrew,Seattle,Food,NULL,1,4/30/2020,Series A,United States,15
Lyft,SF Bay Area,Transportation,982,0.17,4/29/2020,Post-IPO,United States,4900
WeWork,SF Bay Area,Real Estate,300,NULL,4/29/2020,Series H,United States,2250
Kayak / OpenTable,Stamford,Travel,160,0.08,4/29/2020,Acquired,United States,229
Kitopi,New York City,Food,124,NULL,4/29/2020,Series B,United States,89
Lime,SF Bay Area,Transportation,80,0.13,4/29/2020,Series D,United States,765
Transfix,New York City,Logistics,24,0.1,4/29/2020,Series D,United States,128
Yoco,Cape Town,Finance,NULL,NULL,4/29/2020,Series B,South Africa,23
TripAdvisor,Boston,Travel,900,0.25,4/28/2020,Post-IPO,United States,3
Renmoney,Lagos,Finance,391,0.5,4/28/2020,Unknown,Nigeria,NULL
Deliveroo,London,Food,367,0.15,4/28/2020,Series G,United Kingdom,1500
App Annie,SF Bay Area,Data,80,0.18,4/28/2020,Unknown,United States,156
OpenX,Los Angeles,Marketing,35,0.15,4/28/2020,Unknown,United States,70
PayJoy,SF Bay Area,Finance,27,0.25,4/28/2020,Series B,United States,71
Shipsi,Los Angeles,Retail,20,0.5,4/28/2020,Seed,United States,2
Desktop Metal,Boston,Other,NULL,NULL,4/28/2020,Series E,United States,436
Migo,SF Bay Area,Finance,NULL,0.25,4/28/2020,Series B,United States,37
Automation Anywhere,SF Bay Area,Other,260,0.1,4/27/2020,Series B,United States,840
JetClosing,Seattle,Real Estate,20,0.2,4/27/2020,Series A,United States,24
Qwick,Phoenix,Recruiting,NULL,0.7,4/27/2020,Seed,United States,4
OYO,Sao Paulo,Travel,500,NULL,4/25/2020,Series F,Brazil,2400
Stoqo,Jakarta,Food,250,1,4/25/2020,Series A,Indonesia,NULL
Submittable,Missoula,Other,30,0.2,4/25/2020,Series B,United States,17
Divergent 3D,Los Angeles,Transportation,57,0.36,4/24/2020,Series B,United States,88
Ada Support,Toronto,Support,36,0.23,4/24/2020,Series B,Canada,60
UPshow,Chicago,Marketing,19,0.3,4/24/2020,Series A,United States,7
Lighter Capital,Seattle,Finance,18,0.22,4/24/2020,Series B,United States,15
Horizn Studios,Berlin,Travel,15,0.25,4/24/2020,Series B,Germany,30
Welkin Health,SF Bay Area,Healthcare,10,0.33,4/24/2020,Series B,United States,29
Jiobit,Chicago,Consumer,6,0.21,4/24/2020,Unknown,United States,12
TutorMundi,Sao Paulo,Education,4,1,4/24/2020,Series A,Brazil,2
Cheddar,New York City,Media,NULL,NULL,4/24/2020,Acquired,United States,54
GoCardless,London,Finance,NULL,NULL,4/24/2020,Series E,United Kingdom,122
StockX,Detroit,Retail,100,0.12,4/23/2020,Series C,United States,160
Zenefits,SF Bay Area,HR,87,0.15,4/23/2020,Series C,United States,583
Sisense,New York City,Data,80,0.09,4/23/2020,Series F,United States,274
Oscar Health,New York City,Healthcare,70,0.05,4/23/2020,Unknown,United States,1300
Simon Data,New York City,Marketing,6,NULL,4/23/2020,Series C,United States,68
Convoy,Seattle,Logistics,NULL,0.01,4/23/2020,Series D,United States,665
PowerReviews,Chicago,Retail,NULL,NULL,4/23/2020,Acquired,United States,78
Singular,SF Bay Area,Marketing,NULL,NULL,4/23/2020,Series B,United States,50
Magic Leap,Miami,Consumer,1000,0.5,4/22/2020,Series E,United States,2600
When I Work,Minneapolis,HR,55,0.35,4/22/2020,Series B,United States,24
Ike,SF Bay Area,Transportation,10,0.14,4/22/2020,Series A,United States,52
Airy Rooms,Jakarta,Travel,NULL,0.7,4/22/2020,Unknown,Indonesia,NULL
Clearbit,SF Bay Area,Data,NULL,NULL,4/22/2020,Series A,United States,17
ExtraHop,Seattle,Security,NULL,NULL,4/22/2020,Series C,United States,61
Nearmap,Sydney,Construction,NULL,0.1,4/22/2020,Post-IPO,Australia,15
Swiggy,Bengaluru,Food,800,NULL,4/21/2020,Series I,India,1600
Paytm,New Delhi,Finance,500,NULL,4/21/2020,Unknown,India,2200
Lending Club,SF Bay Area,Finance,460,0.3,4/21/2020,Post-IPO,United States,392
Houzz,SF Bay Area,Consumer,155,0.1,4/21/2020,Series E,United States,613
Casper,New York City,Retail,78,0.21,4/21/2020,Post-IPO,United States,339
RealSelf,Seattle,Healthcare,40,0.13,4/21/2020,Series B,United States,42
Freshbooks,Toronto,Finance,38,0.09,4/21/2020,Series B,Canada,75
Patreon,SF Bay Area,Media,30,0.13,4/21/2020,Series D,United States,165
Lambda School,SF Bay Area,Education,19,NULL,4/21/2020,Series B,United States,48
Politico / Protocol,Washington D.C.,Media,13,NULL,4/21/2020,Unknown,United States,NULL
Bringg,Tel Aviv,Logistics,10,0.1,4/21/2020,Series D,Israel,84
Klook,Hong Kong,Travel,300,0.15,4/20/2020,Series D,Hong Kong,521
ConsenSys,New York City,Crypto,91,0.14,4/20/2020,Unknown,United States,10
GumGum,Los Angeles,Marketing,90,0.25,4/20/2020,Series D,United States,58
Kueski,Guadalajara,Finance,90,0.3,4/20/2020,Series B,Mexico,38
Movidesk,Blumenau,Support,33,0.25,4/20/2020,Seed,Brazil,1
Zum,SF Bay Area,Transportation,28,NULL,4/20/2020,Series C,United States,71
Komodo Health,SF Bay Area,Healthcare,23,0.09,4/20/2020,Series C,United States,50
Forward,SF Bay Area,Healthcare,10,0.03,4/20/2020,Series C,United States,100
Hipcamp,SF Bay Area,Travel,NULL,0.6,4/20/2020,Series B,United States,40.5
Motif Investing,SF Bay Area,Finance,NULL,1,4/18/2020,Series E,United States,126
BlackBuck,Bengaluru,Logistics,200,NULL,4/17/2020,Series D,India,293
ContaAzul,Joinville,Education,140,0.35,4/17/2020,Series D,Brazil,37
Greenhouse Software,New York City,Recruiting,120,0.28,4/17/2020,Series D,United States,110
QuintoAndar,Sao Paulo,Real Estate,88,0.08,4/17/2020,Series D,Brazil,335
Loft,Sao Paulo,Real Estate,47,0.1,4/17/2020,Series C,Brazil,263
Zilingo,Singapore,Retail,44,0.05,4/17/2020,Series D,Singapore,307
Labster,Copenhagen,Education,40,NULL,4/17/2020,Series B,Denmark,34
Sweetgreen,Los Angeles,Food,35,0.1,4/17/2020,Series I,United States,478
People.ai,SF Bay Area,Marketing,30,0.18,4/17/2020,Series C,United States,100
Tor,Boston,Security,13,0.37,4/17/2020,Unknown,United States,1
BitGo,SF Bay Area,Crypto,NULL,0.12,4/17/2020,Series B,United States,69
Dispatch,Boston,Other,NULL,0.38,4/17/2020,Series A,United States,18
Food52,New York City,Food,NULL,NULL,4/17/2020,Acquired,United States,96
Influitive,Toronto,Marketing,NULL,NULL,4/17/2020,Unknown,Canada,59
CarGurus,Boston,T
gitextract_arvppbev/ ├── Advanced - CTEs.sql ├── Advanced - Stored Procedures.sql ├── Advanced - Temp Tables.sql ├── Advanced - Triggers and Events.sql ├── Beginner - Group By + Order By.sql ├── Beginner - Having vs Where.sql ├── Beginner - Limit and Aliasing.sql ├── Beginner - Parks_and_Rec_Create_db.sql ├── Beginner - Select Statement.sql ├── Beginner - Where Statement.sql ├── Intermediate - Case Statements.sql ├── Intermediate - Joins.sql ├── Intermediate - String Functions.sql ├── Intermediate - Subqueries.sql ├── Intermediate - Unions.sql ├── Intermediate - Window Functions.sql ├── Portfolio Project - Data Cleaning.sql ├── Portfolio Project - EDA.sql ├── README.md └── layoffs.csv
SYMBOL INDEX (5 symbols across 3 files) FILE: Advanced - Temp Tables.sql type temp_table (line 8) | CREATE TEMPORARY TABLE temp_table FILE: Beginner - Parks_and_Rec_Create_db.sql type employee_demographics (line 10) | CREATE TABLE employee_demographics ( type employee_salary (line 20) | CREATE TABLE employee_salary ( type parks_departments (line 62) | CREATE TABLE parks_departments ( FILE: Portfolio Project - Data Cleaning.sql type `world_layoffs` (line 124) | CREATE TABLE `world_layoffs`.`layoffs_staging2` (
Condensed preview — 20 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (228K chars).
[
{
"path": "Advanced - CTEs.sql",
"chars": 2702,
"preview": "-- Using Common Table Expressions (CTE)\n-- A CTE allows you to define a subquery block that can be referenced within the"
},
{
"path": "Advanced - Stored Procedures.sql",
"chars": 3213,
"preview": "-- 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 e"
},
{
"path": "Advanced - Temp Tables.sql",
"chars": 1621,
"preview": "-- Using Temporary Tables\n-- Temporary tables are tables that are only visible to the session that created them. \n-- The"
},
{
"path": "Advanced - Triggers and Events.sql",
"chars": 3444,
"preview": "-- Triggers\n\n-- a Trigger is a block of code that executes automatically executes when an event takes place in a table.\n"
},
{
"path": "Beginner - Group By + Order By.sql",
"chars": 2635,
"preview": "-- Group By\n-- When you use the GROUP BY clause in a MySQL query, it groups together rows that have the same values in t"
},
{
"path": "Beginner - Having vs Where.sql",
"chars": 863,
"preview": "-- Having vs Where\n\n-- Both were created to filter rows of data, but they filter 2 separate things\n-- Where is going to "
},
{
"path": "Beginner - Limit and Aliasing.sql",
"chars": 1459,
"preview": "-- LIMIT and ALIASING\n\n-- Limit is just going to specify how many rows you want in the output\n\n\nSELECT *\nFROM employee_d"
},
{
"path": "Beginner - Parks_and_Rec_Create_db.sql",
"chars": 2311,
"preview": "DROP DATABASE IF EXISTS `Parks_and_Recreation`;\nCREATE DATABASE `Parks_and_Recreation`;\nUSE `Parks_and_Recreation`;\n\n\n\n\n"
},
{
"path": "Beginner - Select Statement.sql",
"chars": 2603,
"preview": "-- SELECT STATEMENET\n\n-- the SELECT statement is used to work with columns and specify what columns you want to work see"
},
{
"path": "Beginner - Where Statement.sql",
"chars": 1222,
"preview": "#WHERE Clause:\n#-------------\n#The WHERE clause is used to filter records (rows of data)\n\n#It's going to extract only th"
},
{
"path": "Intermediate - Case Statements.sql",
"chars": 1738,
"preview": "-- Case Statements\n\n-- A Case Statement allows you to add logic to your Select Statement, sort of like an if else statem"
},
{
"path": "Intermediate - Joins.sql",
"chars": 3761,
"preview": "-- Joins\n\n-- joins allow you to combine 2 tables together (or more) if they have a common column.\n-- doesn't mean they n"
},
{
"path": "Intermediate - String Functions.sql",
"chars": 2741,
"preview": "#Now let's look at string functions. These help us change and look at strings differently.\n\nSELECT * \nFROM bakery.custom"
},
{
"path": "Intermediate - Subqueries.sql",
"chars": 2166,
"preview": "# Subqueries\n\n#So subqueries are queries within queries. Let's see how this looks.\n\nSELECT *\nFROM employee_demographics;"
},
{
"path": "Intermediate - Unions.sql",
"chars": 1971,
"preview": "#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"
},
{
"path": "Intermediate - Window Functions.sql",
"chars": 3168,
"preview": "-- Window Functions\n\n-- windows functions are really powerful and are somewhat like a group by - except they don't roll "
},
{
"path": "Portfolio Project - Data Cleaning.sql",
"chars": 8234,
"preview": "-- SQL Project - Data Cleaning\n\n-- https://www.kaggle.com/datasets/swaptr/layoffs-2022\n\n\n\n\n\n\nSELECT * \nFROM world_layoff"
},
{
"path": "Portfolio Project - EDA.sql",
"chars": 3635,
"preview": "-- EDA\n\n-- Here we are jsut going to explore the data and find trends or patterns or anything interesting like outliers\n"
},
{
"path": "README.md",
"chars": 22,
"preview": "# MySQL-YouTube-Series"
},
{
"path": "layoffs.csv",
"chars": 170050,
"preview": "company,location,industry,total_laid_off,percentage_laid_off,date,stage,country,funds_raised_millions\r\nAtlassian,Sydney,"
}
]
About this extraction
This page contains the full source code of the AlexTheAnalyst/MySQL-YouTube-Series GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 20 files (214.4 KB), approximately 78.5k tokens, and a symbol index with 5 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.