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You have been asked to support an investment firm by analyzing trends in high-growth companies. They are interested in understanding which industries are producing the highest valuations and the rate at which new high-value companies are emerging. Providing them with this information gives them a competitive insight as to industry trends and how they should structure their portfolio looking forward.
You have been given access to their unicorns database, which contains the following tables:
dates
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
date_joined | The date that the company became a unicorn. |
year_founded | The year that the company was founded. |
funding
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
valuation | Company value in US dollars. |
funding | The amount of funding raised in US dollars. |
select_investors | A list of key investors in the company. |
industries
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
industry | The industry that the company operates in. |
companies
| Column | Description |
|---|---|
company_id | A unique ID for the company. |
company | The name of the company. |
city | The city where the company is headquartered. |
country | The country where the company is headquartered. |
continent | The continent where the company is headquartered. |
The output
Your query should return a table in the following format:
| industry | year | num_unicorns | average_valuation_billions |
|---|---|---|---|
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
| industry1 | 2021 | --- | --- |
| industry2 | 2020 | --- | --- |
| industry3 | 2019 | --- | --- |
Where industry1, industry2, and industry3 are the three top-performing industries.
-- Find the top 3 unicorn-generating industries in the period between 2019 and 2021 (inclusive) --
SELECT industry
FROM companies
FULL JOIN industries
USING(company_id)
FULL JOIN funding
USING(company_id)
FULL JOIN dates
USING(company_id)
WHERE EXTRACT(YEAR from date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY COUNT(company) DESC
LIMIT 3;-- Find the year when each company belonging to the above industries became a unicorn, identified by the company ID --
SELECT company_id, EXTRACT(YEAR from date_joined) as year
FROM companies
JOIN dates
USING(company_id)
JOIN industries
USING(company_id)
WHERE EXTRACT(YEAR from date_joined) IN (2019, 2020, 2021) AND industry in (SELECT industry FROM v);-- Given then company IDs and years when each company became a unicorn, count the number of companies corresponding to each year between 2019 and 2021 and each of the top 3 industries. Sort by year first, then by new unicorn count. --
SELECT industry, year, COUNT(*) num_unicorns, ROUND(AVG(valuation) / 1000000000 , 2) AS average_valuation_billions
FROM companies
JOIN industries
USING(company_id)
JOIN funding
USING(company_id)
JOIN unicorn_year
USING(company_id)
GROUP BY industry, year
ORDER BY year, COUNT(company) DESC;