Project: Data-Warehouse-with-Amazon R3/Redshift

Introduction

Sparkify has grown their user base and song database and want to move their processes and data onto the cloud. 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, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team to continue finding insights in what songs their users are listening 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 results.

Project Description

Work on data warehouses and AWS to build an ETL pipeline for a database hosted on Redshift. Need to load data from S3 to staging tables on Redshift and execute SQL statements that create the analytics tables from these staging tables.

Project Datasets

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,

{"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:

It 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.
For example,

{"artist": null, "auth": "Logged In", "firstName": "Walter", "gender": "M", "itemInSession": 0, "lastName": "Frye", "length": null, "level": "free", "location": "San Francisco-Oakland-Hayward, CA", "method": "GET","page": "Home", "registration": 1540919166796.0, "sessionId": 38, "song": null, "status": 200, "ts": 1541105830796, "userAgent": "\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/36.0.1985.143 Safari\/537.36\"", "userId": "39"}

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
  

Staging Table - which copy the JSON file inside the S3 buckets.

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

Project Template

The project template includes 4 files:
create_table.py- create/drop the fact and dimension tables for the star schema in Redshift.
etl.py- load data from S3 into staging tables on Redshift and then process that data into your analytics tables on Redshift.
sql_queries.py- define you SQL statements, which will be imported into the two other files above.
dhw.cfg - Configuration file used that contains info about Redshift, IAM and S3

ETL Pipeline

• Create a new IAM user in your AWS account
  
• 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
  
• Launch a RedShift cluster and create an IAM role that has read access to S3
  
• Add redshift database and IAM role info to dwh.cfg
  
• Load data from S3 to staging tables on Redshift-etl.py
  
• Load data from staging tables to analytics tables on Redshift-etl.py
  
• Delete your Redshift cluster

How to Run

The data source are provided at S3 Bucket and you only need to run the project for AWS Redshift Cluster

• Create tables- create_tables.py
• Execute ETL process- etl.py