Project: Data-Lake-with-Spark

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.
Building 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.
Testing the database and ETL pipeline by running queries by the analytics team from Sparkify and compare the results with their expected results.

Project Description

Using Spark and data lakes to build an ETL pipeline for a data lake hosted on S3.
Loading data from S3, process the data into analytics tables using Spark, and load them back into S3.
Deploying this Spark process on a cluster using AWS.

Project Datasets

Two datasets that reside in S3. Here are the S3 links for each:

Song data: s3://udacity-dend/song_data
Log data: s3://udacity-dend/log_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 an imaginary music streaming app based on configuration settings.
The log files in the dataset will 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

A star schema is required for optimized queries on song play queries.

Fact Table

• songplays - records in event 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
  

Project Template

Working on this project on your local computer, and then move on to the bigger dataset on AWS.
The project template includes 2 files:
etl.py- reads data from S3, processes that data using Spark, and writes them back to S3.
dl.cfg- contains the AWS credentials.