Did you know that the average return from investing in stocks is 10% per year (not accounting for inflation)? But who wants to be average?!
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.
WITH sub AS (
SELECT industry
FROM industries AS i
INNER JOIN dates AS d
USING(company_id)
WHERE EXTRACT(year FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY COUNT(*) DESC
LIMIT 3
)
SELECT industry,
EXTRACT(year FROM date_joined) AS year,
COUNT(*) AS num_unicorns,
ROUND(AVG(valuation/1000000000), 2) AS average_valuation_billions
FROM dates AS d
INNER JOIN funding AS f
USING(company_id)
INNER JOIN industries AS i
USING(company_id)
WHERE EXTRACT(year FROM d.date_joined) IN (2019, 2020, 2021)
AND industry IN (SELECT industry FROM sub)
GROUP BY year,industry
ORDER BY industry DESC, year DESC;WITH schema_check AS (
SELECT column_name, data_type, udt_name
FROM INFORMATION_SCHEMA.COLUMNS
),
-- Check permissions
permission_check AS (
SELECT *
FROM information_schema.table_privileges
WHERE table_schema = 'public' AND grantee = current_user
)
SELECT *
FROM schema_check;WITH sub AS (
SELECT industry
FROM industries AS i
INNER JOIN dates AS d
USING(company_id)
WHERE EXTRACT(year FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY industry
ORDER BY COUNT(*) DESC
LIMIT 3
), unicorn_stats AS (
SELECT
industry,
EXTRACT(year FROM date_joined) AS year,
COUNT(*) AS num_unicorns,
ROUND(AVG(valuation/1000000000.0), 2) AS average_valuation_billions,
RANK() OVER (PARTITION BY EXTRACT(year FROM date_joined) ORDER BY COUNT(*) DESC) AS rank_by_year
FROM dates AS d
INNER JOIN funding AS f
USING(company_id)
INNER JOIN industries AS i
USING(company_id)
WHERE EXTRACT(year FROM d.date_joined) IN (2019, 2020, 2021)
AND industry IN (SELECT industry FROM sub)
GROUP BY year, industry
)
SELECT
industry,
year,
num_unicorns,
average_valuation_billions,
RANK() OVER (PARTITION BY year ORDER BY num_unicorns DESC) AS rank_within_year,
(SELECT industry FROM unicorn_stats WHERE year = u.year AND rank_by_year = 1) AS top_industry_by_year
FROM unicorn_stats AS u
ORDER BY industry DESC, year DESC;