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Project: Analyzing Unicorn Companies
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    Did you know that the average return from investing in stocks is 10% per year! 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.
    Unknown integration
    DataFrameavailable as
    df
    variable
    WITH top_industries AS(
    	SELECT i.industry AS industry,COUNT(i.*) AS num_unicorns
    	FROM industries AS i
    	INNER JOIN dates AS d
    	ON i.company_id = d.company_id
    	WHERE EXTRACT(year from d.date_joined) in (2019,2020,2021)
    	GROUP BY industry
    	ORDER BY num_unicorns DESC
    	LIMIT 3
    ),
    
    yearly_rankings AS(
    	SELECT i.industry AS industry,
    	EXTRACT(year from d.date_joined) AS year,
    	COUNT(i.*) AS num_unicorns,
    	AVG(f.valuation) AS average_valuation
    	FROM industries AS i
    	INNER JOIN dates AS d
    	ON i.company_id = d.company_id
    	INNER JOIN funding AS f
    	ON d.company_id = f.company_id
    	WHERE EXTRACT(year from d.date_joined) in (2019,2020,2021)
    	GROUP BY industry,year
    	
    )
    SELECT yr.industry,
    yr.year,
    yr.num_unicorns,
    ROUND(yr.average_valuation/1000000000,2) AS average_valuation_billions
    FROM yearly_rankings AS yr
    JOIN top_industries AS ti ON yr.industry = ti.industry
    WHERE year in (2019,2020,2021)
    ORDER BY industry,year DESC;
    
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.