Project - Data Lake

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The music streaming startup, Sparkify, has grown their user base and song database 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, builds 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.

Deployement

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

KEY=YOUR_AWS_ACCESS_KEY
SECRET=YOUR_AWS_SECRET_KEY

ETL Pipeline

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

   Columns: songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent

   Dimension Tables

   users - users in the app

   Columns: user_id, first_name, last_name, gender, level

   songs - songs in music database

   Columns: song_id, title, artist_id, year, duration

   artists - artists in music database

   Columns: artist_id, name, location, lattitude, longitude

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

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

3. Load it back to S3

   Write the data to partitioned parquet files in table directories on S3.