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.

In this project, we will build an ETL pipeline for a data lake hosted on S3. We will load data from S3, process the data into analytics tables using Spark, and load them back into S3. We will deploy this Spark process on a cluster using AWS.

File dl.cfg is not provided here. File contains :

KEY=YOUR_AWS_ACCESS_KEY
SECRET=YOUR_AWS_SECRET_KEY

If you are using local as your development environemnt - Moving project directory from local to EMR

 scp -i <.pem-file> <Local-Path> <username>@<EMR-MasterNode-Endpoint>:~<EMR-path>

Running spark job (Before running job make sure EMR Role have access to s3)

spark-submit etl.py --master yarn --deploy-mode client --driver-memory 4g --num-executors 2 --executor-memory 2g --executor-core 2

1. Read data from S3

   • Song data: s3://udacity-dend/song_data
   • Log data: s3://udacity-dend/log_data

   The script reads song_data and load_data from S3.

2. Process data using spark

   Transforms them to create five different tables listed below :

   Fact Table

   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

   Dimension Tables

   users - users in the app Fields - user_id, first_name, last_name, gender, level

   songs - songs in music database Fields - song_id, title, artist_id, year, duration

   artists - artists in music database Fields - artist_id, name, location, lattitude, longitude

   time - timestamps of records in songplays broken down into specific units Fields - start_time, hour, day, week, month, year, weekday

3. Load it back to S3

   Writes them to partitioned parquet files in table directories on S3.