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Siddarth Laxminarayanan/

Analyzing Streaming Service Content in SQL - Solution

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Analyzing Streaming Service Content in SQL

Welcome to your webinar workspace! You can follow along as we analyze the data in a SQL database and visualize the results.

To set up your integration, create a PostgreSQL integration with the following credentials:

  • Integration Name: Streaming Codealong
  • Hostname: workspacedemodb.datacamp.com
  • Database: streaming
  • Username: streaming_codealong
  • Password: streaming_codealong

Note: This is the solution workspace, and you will need to connect your own integration (or setup one using the credentials above) to use it!

Exploring our data

Let's start by checking out the data we will be working with. We can start with the amazon, hulu, netflix, and disney tables.

Unknown integration
DataFrameavailable as
df
variable
SELECT *
FROM amazon
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
DataFrameavailable as
df
variable
SELECT *
FROM hulu
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
DataFrameavailable as
df
variable
SELECT * 
FROM netflix
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
Unknown integration
DataFrameavailable as
df
variable
SELECT *
FROM disney
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

We can also inspect the genres table, which is different from the other tables.

Unknown integration
DataFrameavailable as
df
variable
SELECT *
FROM genres
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

Preparing our data

Joining the different tables

Our data appears to mostly have the same column names. So we can join the data with a series of UNIONs, which will append each table to the previous one.

We use UNION ALL to preserve any possible duplicate rows, as we will want to count entries if they appear in multiple services.

Unknown integration
DataFrameavailable as
df
variable
SELECT *
FROM amazon
UNION ALL
SELECT *
FROM hulu
UNION ALL
SELECT * 
FROM netflix
UNION ALL
SELECT *
FROM disney
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

One problem with the above approach is that we lose out on the streaming service information. So let's repeat our query, but add in the required info!

Unknown integration
DataFrameavailable as
df
variable
SELECT *, 'amazon' AS service
FROM amazon
UNION
SELECT *, 'hulu' AS service
FROM hulu
UNION
SELECT *, 'netflix' AS service
FROM netflix
UNION
SELECT *, 'disney' AS service
FROM disney
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

Great! But we have one more table that might prove useful. Let's add in the genre information with a join.

To do this, we will need to use a Common Table Expression, or CTE.

Unknown integration
DataFrameavailable as
df
variable
WITH service_data AS (
	SELECT *, 'amazon' AS service
    FROM amazon
    UNION
    SELECT *, 'hulu' AS service
    FROM hulu
    UNION
    SELECT *, 'netflix' AS service
    FROM netflix
    UNION
    SELECT *, 'disney' AS service
    FROM disney
)

SELECT *
FROM service_data AS sd
LEFT JOIN genres AS g
	ON sd.title = g.film
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

Inspecting missing data

It looks like we are missing some values in the age and imdb columns. We will also check the rotten_tomatoes column because we may use it later. Let's see how extensive this problem is.

To calculate the null values per column, we will use a combination of SUM() and CASE WHEN to count the number of null values.




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