Data Engineering Nano Degree Programm of Udacity - Project 3 -

Project: Data Warehouse

Introduction 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.

As their data engineer, you are tasked with 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. 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 results.

Project Description In this project, you'll apply what you've learned on data warehouses and AWS to build an ETL pipeline for a database hosted on Redshift. To complete the project, you will need to load data from S3 to staging tables on Redshift and execute SQL statements that create the analytics tables from these staging tables.

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 filepaths 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 filepaths 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.

Schema for Song Play Analysis Using the song and event datasets, you'll need to create a star schema optimized for 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
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 Template

To get started with the project, go to the workspace on the next page, where you'll find the project template. You can work on your project and submit your work through this workspace.

Alternatively, you can download the template files in the Resources tab in the classroom and work on this project on your local computer.

The project template 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.
Additional File: inf_as_code.py is where you can create your infrastructure as code with uncommenting function inside etl.py and clean resource as code again uncommenting lines inf_as_code() and inf_clean().inf_clean()

Project Steps

Below are steps you can follow to complete each component of this project.
Create Table Schemas
Design schemas for your fact and dimension tables
Write a SQL CREATE statement for each of these tables in sql_queries.py
Complete the logic in create_tables.py to connect to the database and create these tables
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.
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.
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

Implement the logic in etl.py to load data from S3 to staging tables on Redshift.
Implement the logic in etl.py to load data from staging tables to analytics tables on Redshift.
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.

Delete your redshift cluster when finished.