Project Data Warehouse

Project Overview

A music streaming startup, 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.

In this project, we will create an ETL pipeline to build a data warehouses hosted on Redshift.

Song Dataset

We will be working with two datasets that reside in S3.

Song Dataset:

It's a subset of real data from 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.

Sample Data:

{"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 are partitioned by year and month.

Sample Data:

{"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

Fact Table

songplays - records in event data associated with song plays. Columns for the table:

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

Dimension Tables

users

user_id, first_name, last_name, gender, level

songs

song_id, title, artist_id, year, duration

artists

artist_id, name, location, lattitude, longitude

time

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

How to Run

Use Redshift_Cluster_IaC.py from Data_Engineering_Projects to launch Redshift Cluster.

Setup Configurations

Setup the dwh.cfg file (File not added in this repository). File format for dwh.cfg

[CLUSTER]
HOST=''
DB_NAME=''
DB_USER=''
DB_PASSWORD=''
DB_PORT=5439

[IAM_ROLE]
ARN=<IAM Role arn>

[S3]
LOG_DATA='s3://udacity-dend/log_data'
LOG_JSONPATH='s3://udacity-dend/log_json_path.json'
SONG_DATA='s3://udacity-dend/song_data'

Create tables

$ python create_tables.py

Load Data

$ python create_tables.py

Reference: AWS Redshift Doc