Project - Data Lakes with Spark

Project Context and Purpose

A music streaming startup, Sparkify, has grown its user base and song database and want to move its processes and data onto the cloud. Its 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.

As their data engineer, I am tasked with building an ETL pipeline that extracts the data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow the analytics team to continue finding insights in what songs the users are listening to.

Project Description

In this project, I will apply Spark and data lakes to build an ETL pipeline for a data lake hosted on S3. To complete the project, I will need to load data from S3, process the data into analytics tables using Spark, and load them back into S3. I'll deploy this Spark process on a cluster using AWS.

Dataset

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 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

And below is an example of what part of a single log file, 2018-11-01-events.json, looks like.

{"artist":"Black Eyed Peas","auth":"Logged In","firstName":"Sylvie","gender":"F","itemInSession":0,"lastName":"Cruz","length":214.93506,"level":"free","location":"Washington-Arlington-Alexandria, DC-VA-MD-WV","method":"PUT","page":"NextSong","registration":1540266185796.0,"sessionId":9,"song":"Pump It","status":200,"ts":1541108520796,"userAgent":"\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit\/537.77.4 (KHTML, like Gecko) Version\/7.0.5 Safari\/537.77.4\"","userId":"10"}
{"artist":null,"auth":"Logged In","firstName":"Ryan","gender":"M","itemInSession":0,"lastName":"Smith","length":null,"level":"free","location":"San Jose-Sunnyvale-Santa Clara, CA","method":"GET","page":"Home","registration":1541016707796.0,"sessionId":169,"song":null,"status":200,"ts":1541109015796,"userAgent":"\"Mozilla\/5.0 (X11; Linux x86_64) AppleWebKit\/537.36 (KHTML, like Gecko) Ubuntu Chromium\/36.0.1985.125 Chrome\/36.0.1985.125 Safari\/537.36\"","userId":"26"}

Project Structure

Local data folder

• ./data/log_data/: log_data on local drive for pipeline development
• ./data/song_data/: song_data on local drive for pipeline development In addition to the data files, the project workspace includes four files:
• etl.py reads data from S3, processes that data using Spark, and writes them back to S3.
• dl.cfg contains your AWS credentials (you need to create your own dl.cfg)
• notebook-local-data.ipynb is a jupyter notebook which was used to develop workflow
• README.md provides discussion on your process and decisions.

Schema for Song Play Analysis

Fact Table - songplays

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

Dimension Table - users

    user_id,
    first_name,
    last_name,
    gender,
    level

Dimension Table - songs

    song_id, 
    title, 
    artist_id, 
    year, 
    duration

Dimension Table - artists

    artist_id, 
    name, 
    location, 
    lattitude, 
    longitude

Dimension Table - time

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

Run Project Locally

Install Spark, set up local Apache Spark environment