A startup called Sparkify wants to analyze the data they've been collecting on songs and user activity on their new music streaming app. The analytics team is particularly interested in understanding what songs users are listening to. Currently, they don't have an easy way to query their data, which resides 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 purpose of this project is to define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.
Using the song and log datasets, the projects wants a star schema optimized for queries on song play analysis. This includes the following tables.
songplays - records in log 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, latitude, longitude
time - timestamps of records in songplays broken down into specific units
start_time, hour, day, week, month, year, weekday
There are so many NULL in Song and Artist Dimension.
