Project: Data Lake

Introduction

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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.

This project builds 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.

Project Datasets

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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 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

And below is an example of what the data in a log file, 2018-11-12-events.json, looks like.

Schema for Song Play Analysis

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Fact Table

1. 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 2. users - users in the app - user_id, first_name, last_name, gender, level

3. songs - songs in music database

   • song_id, title, artist_id, year, duration

4. artists - artists in music database

   • artist_id, name, location, lattitude, longitude

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

   • start_time, hour, day, week, month, year, weekday

ETL Pipeline

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1. Reading the song and log data from S3 and processing that data using Spark to the 5 tables above.
2. Writing them back to another sparkify S3.

Files in Project

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• etl.py reads data from S3, processes that data using Spark, and writes them back to S3
• dl.cfgcontains your AWS credentials (Not in the GitHub because of the private information)
• README.md provides discussion on your process and decisions

How to Run the Project

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• Run only etl.py to write the all tables on S3.

ER Diagram

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