The Sparkify Music Streaming Analysis project focuses on creating a NoSQL database and an ETL pipeline for Sparkify, a music streaming startup. Sparkify aims to analyze the data collected from its new music streaming app, covering songs and user activities.
In this project, we designed and built a NoSQL database using Apache Cassandra and created an ETL pipeline with Python to populate the database.
The NoSQL database serves to answer specific queries related to song play data. It consists of tables for the following queries:
- Retrieve artist, song title, and song length from music app history when sessionId = 338 and itemInSession = 4.
- Retrieve artist name, sorted song list (by itemInSession), and user details (first and last name) for userId = 10 and sessionId = 182.
- Retrieve all user names (first and last) from the music app history who listened to the song 'All Hands Against His Own'.
The ETL (Extract, Transform, Load) process reads data from files in the data folder, denormalizes it to match the Apache Cassandra tables, and creates a new CSV file named event_datafile.csv.
This project comprises the following files and directories:
event_data: Raw data collected from Sparkify's music streaming app.Sparkify.ipynb: Jupyter Notebook containing database creation and ETL code.event_datafile.csv: Denormalized CSV file derived from theevent_data, ready for inserting data into Apache Cassandra tables.Images: Screenshot showing the data in theevent_datafile.csvandcqlshoutput.
To execute the project, simply run the Sparkify.ipynb Jupyter Notebook to perform validation and sample queries.
Feel free to explore the dataset and analyze the music streaming behavior on Sparkify's platform.