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Analyzing Streaming Service Content in SQL - Codealong

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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

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.

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This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
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This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
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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.

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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.

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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!

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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.

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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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