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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.
-- Common Table Expression (CTE) to define the top industries based on the number of unicorns
WITH top_industries AS (
SELECT
i.industry, -- Select the industry name
COUNT(d.company_id) AS num_unicorns -- Count the number of companies (unicorns) in each industry
FROM
industries i -- Source table for industries
JOIN
dates d ON i.company_id = d.company_id -- Join with dates table to access joining dates
WHERE
EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021) -- Filter for specific years (2019, 2020, 2021)
GROUP BY
i.industry -- Group the results by industry
ORDER BY
num_unicorns DESC -- Order the results by number of unicorns in descending order
LIMIT 3 -- Limit the result to the top 3 industries
)
-- Main query to retrieve unicorn counts and average valuations for the top industries
SELECT
ti.industry, -- Select the industry from the top industries CTE
EXTRACT(YEAR FROM d.date_joined) AS year, -- Extract the year from the date_joined field
COUNT(d.company_id) AS num_unicorns, -- Count the number of unicorns per industry per year
ROUND(AVG(f.valuation) / 1e9, 2) AS average_valuation_billions -- Calculate and round the average valuation in billions
FROM
top_industries ti -- Reference the top industries CTE
JOIN
industries i ON ti.industry = i.industry -- Join again with the industries table to get details
JOIN
dates d ON i.company_id = d.company_id -- Join with dates table again to access joining dates
JOIN
funding f ON i.company_id = f.company_id -- Join with funding table to access company valuations
WHERE
EXTRACT(YEAR FROM d.date_joined) IN (2019, 2020, 2021) -- Filter for the same years as above
GROUP BY
ti.industry, year -- Group results by industry and year
ORDER BY
year DESC, num_unicorns DESC; -- Order results first by year (descending) then by number of unicorns (descending)