A music streaming startup, Sparkify, has grown their user base and song database even more and want to move their data warehouse to a data lake. Their data resides in S3, in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.

As their data engineer, you are tasked with building an ETL pipeline that extracts their data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow their analytics team to continue finding insights in what songs their users are listening to.

The repository is made up of the following files:

.
├── dl.cfg       # Configuration file containing AWS IAM credentials
├── etl.py       # Extracts data from S3 and processes using Spark
└── README.md

This project implements a star schema. songplays is the fact table in the data model, while users, songs, artists, and time are all dimensional tables.

• songplays - records in event data associated with song plays (records with page = NextSong)
  • start_time, userId, level, sessionId, location, userAgent, song_id, artist_id, songplay_id

• users - users of the Sparkify app.
  • firstName, lastName, gender, level, userId
• songs - collection of songs.
  • song_id, title, artist_id, year, duration
• artists - information about artists.
  • artist_id, artist_name, artist_location, artist_lattitude, artist_longitude
• time - timestamps of records in songplays, deconstructed into various date-time parts.
  • start_time, hour, day, week, month, year, weekday

1. Add appropriate AWS IAM Credentials in dl.cfg
2. Specify desired output data path in the main function of etl.py
3. Run etl.py