Adventures in Machine Learning

Unlocking the Beat: Analyzing Music Streaming Data with Common Table Expressions

Analyzing data has become an integral part of many industries from healthcare to finance, and now even the music streaming industry. With the rise of music streaming platforms, companies have access to a vast amount of data that can help them understand their users’ behavior better.

In this article, we will explore two topics: the use of Common Table Expressions (CTEs) in SQL Server and how to analyze data in the music streaming industry. What are CTEs?

CTEs, or Common Table Expressions, are temporary named results that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. They are widely used in SQL Server to simplify complex queries, improve the readability of code, and reduce the need for subqueries.

With CTEs, you can name a subquery and reference it later in your SQL statement, making your code easier to understand.

Syntax and use cases of CTEs

To create a CTE, include the keyword “WITH” followed by the name of the CTE and the query that defines it. For example:

“`

WITH sales_cte AS (

SELECT salesperson_id, SUM(amount) as total_sales

FROM sales

GROUP BY salesperson_id)

SELECT *

FROM sales_cte

“`

In this example, we create a CTE named “sales_cte” that includes the total sales for each salesperson, then we reference that CTE in the second SELECT statement. There are many use cases for CTEs, including recursive queries, extracting data from a source system into a target system, and simplifying queries with complex subqueries.

Interactive exercises are also available online to help improve your understanding of CTEs.

Example 1: Finding average highest and lowest numbers of daily streams

Let’s assume you have a table named “daily_streams” that contains the number of streams for each day. Using a CTE, you can calculate the average of the highest and lowest numbers of streams per day.

“`

WITH daily_streams_cte AS (

SELECT date, streams,

RANK() OVER (ORDER BY streams DESC) AS rank_desc,

RANK() OVER (ORDER BY streams ASC) AS rank_asc

FROM daily_streams)

SELECT AVG(streams) AS avg_highest_streams, AVG(streams) AS avg_lowest_streams

FROM daily_streams_cte

WHERE rank_desc = 1 OR rank_asc = 1

“`

In this query, we use the RANK function to assign a rank to each row in the “daily_streams” table based on the number of streams. We then reference that CTE to calculate the average of the highest and lowest numbers of streams per day.

Example 2: Calculating average total fee paid per song

Assuming you have a table that contains the artist, song title, and the fee paid for each song, you can use a CTE to calculate the average total fee paid per song. “`

WITH song_fees_cte AS (

SELECT artist, song_title, SUM(fee_paid) AS total_fee

FROM song_fees

GROUP BY artist, song_title)

SELECT AVG(total_fee) AS avg_fee_per_song

FROM song_fees_cte

“`

In this query, we create a CTE that groups songs by artist and title, then sums up the fee paid for each song. We then reference that CTE to calculate the average total fee paid per song.

Example 3: Finding each artist’s most streamed album

Let’s assume you have a dataset that contains an “artist” table, an “album” table, and a “streams” table that shows how many times each song from each album was streamed. Using a CTE, you can find the most streamed album for each artist.

“`

WITH artist_streams_cte AS (

SELECT a.name AS artist_name,

b.name AS album_name,

c.streams,

RANK() OVER (PARTITION BY a.id ORDER BY c.streams DESC) AS album_rank

FROM artist a

JOIN album b ON a.id = b.artist_id

JOIN streams c ON b.id = c.album_id)

SELECT artist_name, album_name, streams

FROM artist_streams_cte

WHERE album_rank = 1

“`

In this query, we create a CTE that joins the “artist,” “album,” and “streams” tables, then uses the RANK function to assign a rank to each album based on the number of streams. We then reference that CTE to find the most streamed album for each artist.

Analyzing Music Streaming Platform Data

Now that we have covered the basics of CTEs, let’s dive into analyzing a dataset in the music streaming industry. Music streaming platforms have access to a wealth of data that can help them improve their service.

For example, they can use data to understand their users’ behavior, improve their recommendation systems, or even sign new artists based on their popularity on the platform.

Description of dataset

The dataset we will be using contains three tables: an “artist” table, an “album” table, and a “streams” table. The “artist” table contains information about each artist, including their name and ID.

The “album” table contains information about each album, including the name of the album and the ID of the artist who created it. Finally, the “streams” table contains information about how many times each song from each album was streamed, including the ID of the album that the song belongs to.

Example 1: Finding average highest and lowest numbers of daily streams (using dataset)

Assuming we have a “streams” table that contains the number of streams for each day, we can use SQL to calculate the average of the highest and lowest numbers of streams per day. “`

WITH daily_streams_cte AS (

SELECT date, streams,

RANK() OVER (ORDER BY streams DESC) AS rank_desc,

RANK() OVER (ORDER BY streams ASC) AS rank_asc

FROM streams)

SELECT AVG(streams) AS avg_highest_streams, AVG(streams) AS avg_lowest_streams

FROM daily_streams_cte

WHERE rank_desc = 1 OR rank_asc = 1

“`

In this query, we use the RANK function to assign a rank to each row in the “streams” table based on the number of streams. We then reference that CTE to calculate the average of the highest and lowest numbers of streams per day.

Example 2: Calculating average total fee paid per song (using dataset)

Assuming we have a “song_fees” table that contains the artist, song title, and the fee paid for each song, we can use SQL to calculate the average total fee paid per song. “`

WITH song_fees_cte AS (

SELECT artist_id, song_title, SUM(fee_paid) AS total_fee

FROM song_fees

GROUP BY artist_id, song_title)

SELECT AVG(total_fee) AS avg_fee_per_song

FROM song_fees_cte

“`

In this query, we create a CTE that groups songs by artist and title, then sums up the fee paid for each song. We then reference that CTE to calculate the average total fee paid per song.

Example 3: Finding each artist’s most streamed album (using dataset)

Assuming we have a dataset that contains an “artist” table, an “album” table, and a “streams” table that shows how many times each song from each album was streamed, we can use SQL to find the most streamed album for each artist. “`

WITH artist_streams_cte AS (

SELECT a.name AS artist_name,

b.name AS album_name,

c.streams,

RANK() OVER (PARTITION BY a.id ORDER BY c.streams DESC) AS album_rank

FROM artist a

JOIN album b ON a.id = b.artist_id

JOIN streams c ON b.id = c.album_id)

SELECT artist_name, album_name, streams

FROM artist_streams_cte

WHERE album_rank = 1

“`

In this query, we create a CTE that joins the “artist,” “album,” and “streams” tables, then uses the RANK function to assign a rank to each album based on the number of streams. We then reference that CTE to find the most streamed album for each artist.

Conclusion

In conclusion, Common Table Expressions (CTEs) are a great way to simplify complex SQL queries, improve code readability, and reduce the need for subqueries. In the music streaming industry, companies can leverage data to understand their users’ behavior better, improve their recommendation systems, and sign new artists based on their popularity on the platform.

Using CTEs, we can easily analyze music streaming platform data to gain valuable insights into user behavior and the artists and albums that are the most popular. In this article, we explored two topics related to analyzing data in the music streaming industry.

We began by discussing the basics of Common Table Expressions (CTEs) in SQL Server, including their syntax and use cases. We then applied CTEs to several examples related to music streaming platforms, such as finding average daily streams, calculating average pay per song, and finding the most streamed album for each artist.

Through these examples, we highlighted the importance of data analysis in the music streaming industry and how it can be used to better understand user behavior and popular artists on the platform. Takeaways from this article include the importance of using CTEs to simplify complex queries, the benefits of analyzing data to improve services, and the value of leveraging data to make informed decisions in business operations.

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