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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

ColumnDescription
company_idA unique ID for the company.
date_joinedThe date that the company became a unicorn.
year_foundedThe year that the company was founded.

funding

ColumnDescription
company_idA unique ID for the company.
valuationCompany value in US dollars.
fundingThe amount of funding raised in US dollars.
select_investorsA list of key investors in the company.

industries

ColumnDescription
company_idA unique ID for the company.
industryThe industry that the company operates in.

companies

ColumnDescription
company_idA unique ID for the company.
companyThe name of the company.
cityThe city where the company is headquartered.
countryThe country where the company is headquartered.
continentThe continent where the company is headquartered.

The output

Your query should return a table in the following format:

industryyearnum_unicornsaverage_valuation_billions
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------
industry12021------
industry22020------
industry32019------

Where industry1, industry2, and industry3 are the three top-performing industries.

Spinner
DataFrameas
df1
variable
-- Explore the data in the table
WITH industry_performance AS (
    -- Step 1: Combine all necessary tables
    SELECT
        i.industry,
        EXTRACT(YEAR FROM d.date_joined) AS year,
        COUNT(DISTINCT d.company_id) AS num_unicorns,
        AVG(f.valuation / 1000000000) AS average_valuation_billions -- Converting valuation to billions
    FROM
        industries i
        JOIN dates d ON i.company_id = d.company_id
        JOIN funding f ON i.company_id = f.company_id
    GROUP BY
        i.industry,
        EXTRACT(YEAR FROM d.date_joined)
),

-- Step 2: Rank industries by their average valuation per year
ranked_industries AS (
    SELECT
        industry,
        year,
        num_unicorns,
        average_valuation_billions,
        ROW_NUMBER() OVER (PARTITION BY year ORDER BY average_valuation_billions DESC) AS rank
    FROM
        industry_performance
)

-- Step 3: Select top 3 industries for each year
SELECT
    industry,
    year,
    num_unicorns,
    average_valuation_billions
FROM
    ranked_industries
WHERE
    rank <= 3
ORDER BY
    year DESC,
    average_valuation_billions DESC;