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.

This project intends to build 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.

Make sure that you have pyspark installed locally or in a virtualenv.

• Update dl.cfg with an IAM user credentials having access to read and write on S3.
• Run python etl.py on terminal to run the script.

To run the script on sample data only requires two more steps:

• In main function, comment S3 paths and uncomment local data paths below that.
• In process_log_data function, comment S3 path and uncomment local path (nesting dir levels are different in local data dir vs S3 bucket).

Note: To re-run locally, you need to remove content of output directory using rm -rf 'path/to/output/'.

Using the song and log datasets, a star schema is created and optimized for queries on song play analysis. This includes the following tables.

tbl_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

tbl_users - users in the app

user_id, first_name, last_name, gender, level

tbl_songs - songs in music database

song_id, title, artist_id, year, duration

tbl_artists - artists in music database

artist_id, name, location, lattitude, longitude

tbl_time - timestamps of records in songplays broken down into specific units

start_time, hour, day, week, month, year, weekday

dl.cfg: Contains IAM user credentials used by pyspark to read/write files on S3.

etl.py: Contains functions to process songs and logs data from S3 by loading in Spark dataframes, removing duplicates, manipulating columns and saving dataframes on S3 in parquet format.