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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 top_industries AS (
-- top 3 industries across 2019-2021 by total unicorn count
SELECT i.industry,
COUNT(*) AS total_unicorns_2019_2021
FROM industries AS i
JOIN dates AS d
ON i.company_id = d.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry
ORDER BY total_unicorns_2019_2021 DESC
LIMIT 3
),
yearly_rankings AS (
-- per-industry per-year stats for target years
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined)::INT AS year,
COUNT(*) AS num_unicorns,
AVG(f.valuation) AS avg_valuation
FROM industries AS i
JOIN dates AS d
ON i.company_id = d.company_id
JOIN funding AS f
ON i.company_id = f.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021)
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
)
-- Finding the industry with the most unicorns between 2019 and 2021
SELECT
yr.industry,
yr.year,
yr.num_unicorns,
ROUND(yr.avg_valuation / 1000000000.0, 2) AS average_valuation_billions
FROM yearly_rankings AS yr
WHERE yr.year IN (2019, 2020, 2021)
AND yr.industry IN (SELECT industry FROM top_industries)
ORDER BY yr.year DESC, yr.num_unicorns DESC;