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.
The goal of this project is to create 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.
Data willbe loaded form two datasets to a staging area before being loaded to the analytics tables
The first dataset is a subset of real data from 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.
The log dataset consists of log files in JSON format generated by an 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 are partitioned by year and month.
Data from the Datases will be staged in the staging_events and the staging_songs
Using the song and event datasets, we'll need to create a star schema optimized for queries on song play analysis. This includes the following tables.
- 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
- 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, lattitude, longitude
- time - (timestamps of records in songplays broken down into specific units): start_time, hour, day, week, month, year, weekday