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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.
WITH unicorn_per_industry AS (
SELECT industry, COUNT(i.company_id) AS num_unicorns, d.year_founded AS year
FROM public.industries i
INNER JOIN public.dates d
ON d.company_id = i.company_id
WHERE date_joined >= '2019-01-01' AND date_joined <= '2021-12-31'
GROUP BY year, industry),
unicorn_industry AS (
SELECT industry, year, num_unicorns, RANK() OVER (ORDER BY num_unicorns) AS rnk
FROM unicorn_per_industry),
ranked_industry AS
(SELECT industry, num_unicorns, rnk, year
FROM unicorn_industry
WHERE rnk < 4),
valuation AS (
SELECT i.industry, AVG(f.valuation) AS average_valuation_billions
FROM public.funding f
INNER JOIN public.industries i
ON i.company_id = f.company_id
GROUP BY i.industry)
SELECT r.industry, r.year, r.num_unicorns, v.average_valuation_billions
FROM ranked_industry r
INNER JOIN valuation v
ON r.industry = v.industry
ORDER BY year DESC, num_unicorns DESC;