PySpark Data Lake

Data Lake with PySpark and AWS 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.

You need 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.

Project Datasets

You'll be working with two datasets that reside in S3. Here are the S3 links for each:

Song data: s3://udacity-dend/song_data

• 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 data: s3://udacity-dend/log_data

• 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 an imaginary music streaming app based on configuration settings.

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

Schema for Song Play Analysis

Tables

• Fact Table: songplays: attributes referencing to the dimension tables
• Dimension Tables: users, songs, artists and time table

ETL Pipeline

1. Configure and load AWS credentials (dl.cfg):

AWS_ACCESS_KEY_ID     = YOUR_AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY = YOUR_AWS_SECRET_ACCESS_KEY
INPUT_DATA            = s3://<BUCKET_NAME>/
OUTPUT_DATA           = s3://<BUCKET_NAME>/output_data/
INPUT_SONG_DATA       = s3://<BUCKET_NAME>/song_data/*/*/*/*.json
INPUT_LOG_DATA        = s3://<BUCKET_NAME>/log_data/*/*/*.json

2. Run ETL Script using command line python3 etl.py Steps:

• Read Sparkify data from S3
  • song_data: s3://udacity-dend/song_data
  • log_data: s3://udacity-dend/log_data
• Process the data using Apache Spark.
  • Transform the data and create the dimensional tables
• Load data back into S3