A music streaming startup, Sparkify, has grown their user base and song database and want to move their processes and data onto the cloud. 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, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team to continue finding insights into what songs their users are listening to.
The first dataset is a subset of the Million Song Dataset. Each file is in JSON format and contains metadata about a song and the artist of that song. The files are partitioned by the first three letters of each song's track ID. For example, here are file paths to two files in this dataset.
The second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. These simulate app activity logs from an imaginary music streaming app based on configuration settings.
The log files in the dataset you'll be working with are partitioned by year and month. For example, here are file paths to two files in this dataset.
Using the song and event datasets, you'll need to create a star schema optimized for queries on song play analysis. This includes the following tables. Fact Table
- songplays - records in event data associated with song plays i.e. records with page NextSong
- songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent
Dimension Tables
- users - users in the app
- user_id
- first_name
- last_name
- gender
- level
- songs - songs in music database
- song_id, title
- artist_id
- year
- duration
- artists - artists in music database
- artist_id
- name, location
- latitude
- longitude
- time - timestamps of records in songplays broken down into specific units
- start_time
- hour
- day
- week
- month
- year
- weekday