Data-Warehouse-on-AWS

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. Building ETL pipeline that extracts data from S3, stages them in Redshift, and transform data into a set of dimensional tables for their analytics team.

In addition to the data files the project workspace includes four files:

• create_table.py is where you'll create your fact and dimension tables for the star schema in Redshift.
• etl.py is where you'll load data from S3 into staging tables on Redshift and then process that data into your analytics tables on Redshift.
• sql_queries.py is where you'll define you SQL statements, which will be imported into the two other files above.
• README.md is where you'll provide discussion on your process and decisions for this ETL pipeline.

Project Datasets

Their are two datasets that resides in 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

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.

Project Steps

Create Table Schemas

1. Design schemas for your fact and dimension tables
2. Write a SQL CREATE statement for each of these tables in sql_queries.py
3. Complete the logic in create_tables.py to connect to the database and create these tables
4. Write SQL DROP statements to drop tables in the beginning of create_tables.py if the tables already exist. This way, you can run create_tables.py whenever you want to reset your database and test your ETL pipeline.
5. Launch a redshift cluster and create an IAM role that has read access to S3.
6. Add redshift database and IAM role info to dwh.cfg.

[CLUSTER]
HOST=redshift-cluster-1.************.us-east-1.redshift.amazonaws.com
DB_NAME=dev
DB_USER=awsuser
DB_PASSWORD=**********
DB_PORT=5439

[IAM_ROLE]
ARN= arn:aws:iam::************:role/dwhRole

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

7. Test by running create_tables.py and checking the table schemas in your redshift database. You can use Query Editor in the AWS Redshift console for this.

Build ETL Pipeline

1. Implement the logic in etl.py to load data from S3 to staging tables on Redshift.
2. Implement the logic in etl.py to load data from staging tables to analytics tables on Redshift.
3. Test by running etl.py after running create_tables.py and running the analytic queries on your Redshift database to compare your results with the expected results.
4. Delete your redshift cluster when finished.

Staging tables

staging_songs

COLUMN	TYPE
num_song	int
artist_id	varchar
artist_latitude	decimal
artist_longitude	decimal
artist_name	varchar
song_id	varchar
title	varchar
duration	decimal
year	int

staging_events

COLUMN	TYPE
artist	varchar
auth	varchar
firstName	varchar
gender	varchar
itemInSession	int
lastName	varchar
length	decimal
level	varchar
location	varchar
method	varchar
page	varchar
registeration	varchar
sessionId	int
song	varchar
status	int
ts	timestamp
userAgent	varchar
userId	int

Star Schema

Fact table songplay

Dimension Tables users songs artists time

RedShift & ETL Context

Connection to server solution