This project focuses on analyzing music store data with SQL. The dataset has 11 tables: Employee, Customer, Invoice, InvoiceLine, Track, MediaType, Genre, Album, Artist, PlaylistTrack, and Playlist. This project intends to answer many questions and obtain important insights into the music store's operations by applying SQL queries to the dataset.
The "Music Store Data Analysis" project offers a comprehensive analysis of the music store's data to facilitate better decision-making, identify trends, and understand customer behavior. By leveraging SQL queries and data exploration, this project provides valuable answers to optimize inventory management, target marketing campaigns, and make informed business decisions.
The dataset for this project has 11 tables: Employee, Customer, Invoice, InvoiceLine, Track, MediaType, Genre, Album, Artist, PlaylistTrack, and Playlist, as well as their associations.
Q1. Who is the most senior employee based on job title?
SELECT * FROM EMPLOYEE
ORDER BY LEVELS DESC
LIMIT 1;Q2. Which countries have the most Invoices?
SELECT BILLING_COUNTRY, COUNT(*) AS Most_Invoices FROM INVOICE
GROUP BY BILLING_COUNTRY
ORDER BY Most_Invoices DESC;Q3. What are top 3 values of total invoice?
SELECT TOTAL FROM INVOICE
ORDER BY TOTAL DESC
LIMIT 3;Q4. Which city has the best customers?
- We would like to throw a promotional Music Festival in the city we made the most money.
- Write a query that returns one city that has the highest sum of invoice totals.
- Return both the city name & sum of all invoice totals.
SELECT BILLING_CITY, SUM(TOTAL) AS Invoice_Total FROM INVOICE
GROUP BY BILLING_CITY
ORDER BY Invoice_Total DESC
LIMIT 1;Q5. Who is the best customer?
- The customer who has spent the most money will be declared the best customer.
- Write a query that returns the person who has spent the most money.
SELECT C.CUSTOMER_ID, C.FIRST_NAME, C.LAST_NAME, SUM(I.TOTAL) AS TOTAL_AMOUNT
FROM CUSTOMER AS C
JOIN INVOICE AS I
ON C.CUSTOMER_ID = I.CUSTOMER_ID
GROUP BY C.CUSTOMER_ID
ORDER BY TOTAL_AMOUNT DESC
LIMIT 1;Q1. Write query to return the email, first name, last name, & Genre of all Rock Music listeners.
- Return your list ordered alphabetically by email starting with A.
SELECT DISTINCT CUS.EMAIL, CUS.FIRST_NAME, CUS.LAST_NAME
FROM CUSTOMER AS CUS
JOIN INVOICE AS INV ON CUS.CUSTOMER_ID = INV.CUSTOMER_ID
JOIN INVOICE_LINE AS INVL ON INV.INVOICE_ID = INVL.INVOICE_ID
WHERE INVL.TRACK_ID IN(
SELECT T.TRACK_ID
FROM TRACK AS T
JOIN GENRE AS G ON T.GENRE_ID = G.GENRE_ID
WHERE G.NAME LIKE 'Rock'
)
ORDER BY CUS.EMAIL;Q2. Let's invite the artists who have written the most rock music in our dataset.
- Write a query that returns the Artist name and total track count of the top 10 rock bands.
SELECT AR.NAME, COUNT(AR.ARTIST_ID) AS NUMBER_OF_SONGS
FROM TRACK AS TR
JOIN ALBUM AS AL ON TR.ALBUM_ID = AL.ALBUM_ID
JOIN ARTIST AS AR ON AL.ARTIST_ID = AR.ARTIST_ID
JOIN GENRE AS GE ON TR.GENRE_ID = GE.GENRE_ID
WHERE GE.NAME LIKE 'Rock'
GROUP BY AR.ARTIST_ID
ORDER BY NUMBER_OF_SONGS DESC LIMIT 10;Q3. Return all the track names that have a song length longer than the average song length.
- Return the Name and Milliseconds for each track.
- Order by the song length with the longest songs listed first.
SELECT NAME, MILLISECONDS
FROM TRACK
WHERE MILLISECONDS > (
SELECT AVG(MILLISECONDS) AS AVG_SONG_LENGTH
FROM TRACK
)
ORDER BY MILLISECONDS DESC;Q1. Find how much amount spent by each customer on artists?
- Write a query to return customer name, artist name and total spent.
WITH BEST_SELLING_ARTIST AS (
SELECT ART.ARTIST_ID, ART.NAME, SUM(IVL.UNIT_PRICE * IVL.QUANTITY) AS TOTAL_AMOUNT
FROM INVOICE_LINE AS IVL
JOIN TRACK AS TRK ON IVL.TRACK_ID = TRK.TRACK_ID
JOIN ALBUM AS ALB ON TRK.ALBUM_ID = ALB.ALBUM_ID
JOIN ARTIST AS ART ON ALB.ARTIST_ID = ART.ARTIST_ID
GROUP BY ART.ARTIST_ID
ORDER BY TOTAL_AMOUNT DESC LIMIT 1
)
SELECT CUS.CUSTOMER_ID, CUS.FIRST_NAME, CUS.LAST_NAME, BSA.NAME AS ARTIST_NAME, SUM(IVL.UNIT_PRICE * IVL.QUANTITY) AS AMOUNT_SPENT
FROM INVOICE AS INV
JOIN CUSTOMER AS CUS ON INV.CUSTOMER_ID = CUS.CUSTOMER_ID
JOIN INVOICE_LINE AS IVL ON INV.INVOICE_ID = IVL.INVOICE_ID
JOIN TRACK AS TRK ON IVL.TRACK_ID = TRK.TRACK_ID
JOIN ALBUM AS ALB ON TRK.ALBUM_ID = ALB.ALBUM_ID
JOIN BEST_SELLING_ARTIST AS BSA ON ALB.ARTIST_ID = BSA.ARTIST_ID
GROUP BY CUS.CUSTOMER_ID, BSA.NAME;Q2. We want to find out the most popular music Genre for each country.
- We determine the most popular genre as the genre with the highest amount of purchases.
- Write a query that returns each country along with the top Genre.
- For countries where the maximum number of purchases is shared return all Genres.
WITH POPULAR_GENRE AS (
SELECT CU.COUNTRY, COUNT(IL.QUANTITY) AS TOTAL_PURCHASES, GE.NAME AS TOP_GENRE, GE.GENRE_ID,
ROW_NUMBER() OVER(
PARTITION BY CU.COUNTRY ORDER BY COUNT(IL.QUANTITY) DESC
) AS ROW_NUM
FROM CUSTOMER AS CU
JOIN INVOICE AS IV ON CU.CUSTOMER_ID = IV.CUSTOMER_ID
JOIN INVOICE_LINE AS IL ON IV.INVOICE_ID = IL.INVOICE_ID
JOIN TRACK AS TR ON IL.TRACK_ID = TR.TRACK_ID
JOIN GENRE AS GE ON TR.GENRE_ID = GE.GENRE_ID
GROUP BY CU.COUNTRY, GE.NAME, GE.GENRE_ID
ORDER BY CU.COUNTRY, TOTAL_PURCHASES DESC
)
SELECT COUNTRY, TOP_GENRE, TOTAL_PURCHASES
FROM POPULAR_GENRE WHERE ROW_NUM = 1;Q3. Write a query that determines the customer that has spent the most on music for each country.
- Write a query that returns the country along with the top customer and how much they spent.
- For countries where the top amount spent is shared, provide all customers who spent this amount.
WITH CUSTOMER_WITH_COUNTRY AS (
SELECT CU.COUNTRY, CU.CUSTOMER_ID, CU.FIRST_NAME, CU.LAST_NAME, SUM(IV.TOTAL) AS AMOUNT_SPENT,
ROW_NUMBER() OVER(
PARTITION BY CU.COUNTRY ORDER BY SUM(IV.TOTAL) DESC
) AS ROW_NUM
FROM INVOICE AS IV
JOIN CUSTOMER AS CU ON IV.CUSTOMER_ID = CU.CUSTOMER_ID
GROUP BY CU.COUNTRY, CU.CUSTOMER_ID, CU.FIRST_NAME, CU.LAST_NAME
ORDER BY CU.COUNTRY, AMOUNT_SPENT DESC
)
SELECT COUNTRY, CUSTOMER_ID, FIRST_NAME, LAST_NAME, AMOUNT_SPENT
FROM CUSTOMER_WITH_COUNTRY
WHERE ROW_NUM = 1;The project repository is structured as follows:
├── data/ # Directory containing the dataset
├── queries/ # Directory containing SQL query files
└── README.md # Project README file
-
Clone the repository:
git clone https://github.com/kishlayjeet/Music-Store-Data-Analysis.git -
Import the dataset into your SQL database management system.
-
Run SQL queries located in the
queries/directory against the database to perform data analysis and generate insights.
Contributions to this project are welcome. If you have suggestions for improvements or find any issues, feel free to open a pull request or submit an issue in the repository.