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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.
-- 1. Finding the top industries
WITH top_industries AS (
SELECT
i.industry,
COUNT(*) AS total_unicorns
FROM dates d
JOIN industries i ON d.company_id = i.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry
ORDER BY total_unicorns DESC
LIMIT 3
),
-- 2. Gathering yearly rankings data
industry_year_stats AS (
SELECT
i.industry,
EXTRACT(YEAR FROM d.date_joined) AS year,
COUNT(*) AS num_unicorns,
ROUND(AVG(f.valuation::numeric) / 1000000000.0, 2) AS average_valuation_billions
FROM dates d
JOIN industries i ON d.company_id = i.company_id
JOIN funding f ON d.company_id = f.company_id
WHERE EXTRACT(YEAR FROM d.date_joined) BETWEEN 2019 AND 2021
AND i.industry IN (SELECT industry FROM top_industries)
AND f.valuation IS NOT NULL
GROUP BY i.industry, EXTRACT(YEAR FROM d.date_joined)
),
-- 3. Returning the final results
expanded_years AS (
SELECT industry, year
FROM top_industries, (SELECT 2019 AS year UNION ALL SELECT 2020 UNION ALL SELECT 2021) y
),
final_stats AS (
SELECT
e.industry,
e.year,
COALESCE(iys.num_unicorns, 0) AS num_unicorns,
COALESCE(iys.average_valuation_billions, 0.00) AS average_valuation_billions
FROM expanded_years e
LEFT JOIN industry_year_stats iys
ON e.industry = iys.industry AND e.year = iys.year
)
SELECT
industry,
year,
num_unicorns,
average_valuation_billions
FROM final_stats
ORDER BY industry, year DESC;
SELECT * FROM public.dates