Project: Data Lake

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.

As their data engineer, you are tasked with 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.

You'll be able to test your database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected result.

The dataset

• The dataset used where uploaded into the S3

Here are the S3 links for each:

• Song data: s3://udacity-dend/song_data
• Log data: s3://udacity-dend/log_data
• Log data json path: s3://udacity-dend/log_json_path.json

1. 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.

A single song file can look like this

{"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}

2. 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.

Data Schema

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 Structure

The project template includes three files:

• etl.py reads data from S3, processes that data using Spark, and writes them back to S3
• dl.cfg contains your AWS credentials
• README.md provides discussion on your process and decisions

Running the Project

• Add your AWS confiduration details to the dl.cfg config file. You might need to create a new IAM role with s3 full access and add the credentials to the dl.cfg file.
• Run the etl.py file