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 UnicornCounts AS (SELECT i.industry,
EXTRACT(year FROM d.date_joined) AS year,
COUNT(DISTINCT d.company_id) AS num_unicorns,
ROUND(AVG(f.valuation) / 1000000000.0, 2) AS average_valuation_billions
FROM industries AS i
LEFT JOIN dates AS d
ON i.company_id = d.company_id
LEFT JOIN funding AS f
ON i.company_id = f.company_id
WHERE EXTRACT(year FROM d.date_joined) BETWEEN 2019 AND 2021
GROUP BY i.industry,
year),
TopIndustries AS (SELECT industry,
SUM(num_unicorns) AS total_unicorns
FROM UnicornCounts
GROUP BY industry
ORDER BY total_unicorns DESC
LIMIT 3)
SELECT ui.industry,
ui.year,
ui.num_unicorns,
ui.average_valuation_billions
FROM (SELECT uc.industry,
uc.year,
uc.num_unicorns,
uc.average_valuation_billions,
ROW_NUMBER() OVER (PARTITION BY uc.industry ORDER BY uc.year DESC) AS row_num
FROM UnicornCounts uc
JOIN TopIndustries ti
ON uc.industry = ti.industry) AS ui
ORDER BY ui.year DESC,
ui.num_unicorns DESC;