A music streaming startup, Sparkify, has grown their user base and song database wants 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.
Building 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.
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, lattitude, longitude
time- timestamps of records in songplays broken down into specific units
start_time, hour, day, week, month, year, weekday
etl.py- The script that reads song_data and load_data from S3, transforms them to create five different tables, and writes them to partitioned parquet files in table directories on output S3.
dl.cfg contains AWS credentials to access S3 buckets