Sparkify Cloud Data Warehouse with AWS RedShift

Project Brief:

A music streaming startup, Sparkify, has grown its user base and song database and wants to move its 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, I am 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 into what songs their users are listening to. I'll be able to test my database and ETL pipeline by running queries given to me by the analytics team from Sparkify and compare my results with their expected results.

DataSets

I'll be working with two datasets that reside 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.

Example of song data files

    `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 activity logs from a music streaming app based on specified configurations. The log files in the dataset you'll be working with are partitioned by year and month.

Example of log data files

    `log_data/2018/11/2018-11-12-events.json
     log_data/2018/11/2018-11-13-events.json`

Schema for Song Play Analysis

Fact Table

1. songplays - records in log 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

2. users - users in the app

• user_id, first_name, last_name, gender, level

3. songs - songs in music database

• song_id, title, artist_id, year, duration

4. artists - artists in music database

• artist_id, name, location, latitude, longitude

5. time - timestamps of records in songplays broken down into specific units

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

Steps

1. Build SQL queries
   

• Redshift Cluster Dimensional Modeling Create and Insert Tables queries
• Staging Tables Quieres Create and Insert
• SQL-To-SQL ETL Quieries
• Drop tables queries

2. Develop ETL processes for each file
   

• Develop the complete ETL process in SQL in the sql_quieries.py file

1. Moving The data from S3 to Staging Tables

2. Extract, transform, and load data from the staging table to the main analytical repo tables

3. test the result against defined queries

How to run the Project

1. clone the repo
2. cd to the project dir
3. Setup your configuration file
4. install requirements
5. Run create_redshift_cluster.py
6. Run create_tables.py
7. Run etl.py