data_lake_with_spark

Using Spark to build an ETL pipeline for a data lake hosted on S3.

Introduction

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.

Purpose of the project

The purpose of this project is to build ETL pipeline that extracts the data from S3, processes and transforms them using Spark, and loads the data back into S3 as a set of fact and dimensional tables. This will allow Sparkify's analytics team to study insights in what songs their users are listening to.

Database schema design

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

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
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

How to run

Run the etl.py. It extracts data from S3, process and tramsforms the data using Spark, and load them to parquet files into S3.

Available Data

Song Dataset The first dataset is a subset of real data from the Million Song Dataset. Each file is in JSON format and contains metadata about a song and the artist of that song. The files are partitioned by the first three letters of each song's track ID. For example, here are filepaths to two files in this dataset.

song_data/A/B/C/TRABCEI128F424C983.json
song_data/A/A/B/TRAABJL12903CDCF1A.json
And below is an example of what a single song file, TRAABJL12903CDCF1A.json, looks like.

{"num_songs": 1, "artist_id": "ARJIE2Y1187B994AB7", "artist_latitude": null, "artist_longitude": null, "artist_location": "", "artist_name": "Line Renaud", "song_id": "SOUPIRU12A6D4FA1E1", "title": "Der Kleine Dompfaff", "duration": 152.92036, "year": 0} Log Dataset The second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. These simulate app activity logs from a music streaming app based on specified configurations.

The log files in the dataset you'll be working with are partitioned by year and month. For example, here are filepaths to two files in this dataset.

log_data/2018/11/2018-11-12-events.json
log_data/2018/11/2018-11-13-events.json

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