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Sparkify is a startup company who provides music streaming services. They have JSON metadata on the songs in their app and user activity data in Amazon S3. The analytics team in Sparkify is interested in understanding what songs users are listening to.

To build a ETL pipeline for extracting data from s3, stage them on Redshift and transform them into dimensional tables for song play analysis.

Fact Table

songplays

• songplay_id (primary key), start_time (foreign key), user_id (foreign key), level, song_id (foreign key), artist_id (foreign key), session_id, location, user_agent

Dimension Tables

users

• user_id (primary key), first_name, last_name, gender, level

songs

• song_id (primary key), title, artist_id (foreign key), year, duration

artists

• artist_id (primary key), name, location, latitude, longitude

time

• start_time (primary key), hour, day, week, month, year, weekday

1. Create a Redshift cluster and update config.ini with your own credentials

2. Create the tables in your Redshift database by running following command in your terminal

python creat_tables.py

3. Stage, tranform and load the data to your Redshift database by running following command

python etl.py

4. Check whether the tables are loaded correctly by running following command

python test_tables.py

The codes are written by me and the sparkify data belongs to Udacity.