Project 3: Song Play Analysis with S3 and Redshift

Introduction

In this project, we will help one music streaming startup - Sparkify, to move their user and song database processes to the cloud. To reach that, we build an ETL pipeline to extracts their data from AWS S3 (data storage), stages tables on AWS Redshift (data warehouse with columnar storage), and execute SQL statements to create the analytics tables from these staging tables.

Datasets

Datasets used in this project are provided in two public S3 buckets.

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 file paths 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 file paths 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.

The Redshift service is where data will be ingested and transformed, using COPY command we will access to the JSON files inside the buckets and copy their content to our staging tables.

Database Schema

We have two staging tables which copy the JSON file inside the S3 buckets.

Staging Tables

staging_songs - info about songs and artists
staging_events - actions done by users (which song are listening, etc.. )

A star schema was designed to optimize queries on song play analysis. This includes the following tables.

Fact Table

songplays - records in event data associated with song plays i.e. records with page NextSong

Dimension Tables

users - users in the app
songs - songs in music database
artists - artists in music database
time - timestamps of records in songplays broken down into specific units

The database schema is shown as follows

Data Warehouse Configurations and Setup steps:

Create a new IAM user in your AWS account
Give it AdministratorAccess and Attach policies
Use access key and secret key to create clients for EC2, S3, IAM, and Redshift.
Create an IAM Role that makes Redshift able to access S3 bucket (ReadOnly)
Create a RedShift Cluster and get the DWH_ENDPOIN(Host address) and DWH_ROLE_ARN and fill the config file.

ETL Pipeline

Created tables to store the data from S3 buckets.
Loading the data from S3 buckets to staging tables in the Redshift Cluster.
Inserted data into fact and dimension tables from the staging tables.

Project Structure

create_tables.py - This script will drop old tables (if exist) ad re-create new tables.
etl.py - This script executes the queries that extract JSON data from the S3 bucket and ingest them to Redshift.
sql_queries.py - This file contains variables with SQL statement in String formats, partitioned by CREATE, DROP, COPY and INSERT statement.
dhw.cfg - Configuration file used that contains info about Redshift, IAM and S3

How to Run

Create tables by running create_tables.py.
Execute ETL process by running etl.py.