Udactity Data Engineer Nanodegree Project: Data Modeling with Postgres

A startup called Sparkify wants to analyze the data they've been collecting on songs and user activity on their new music streaming app. The analytics team is particularly interested in understanding what songs users are listening to. Currently, they don't have an easy way to query their data, which resides 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.

They'd like a data engineer to create a Postgres database with tables designed to optimize queries on song play analysis, and bring you on the project. Your role is to create a database schema and ETL pipeline for this analysis. 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.

Datasets available

The song 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 log 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.

Program execution

• Execute the script to generate the database and its tables by executing python3 create_tables.py.
• Load the data and insert it to the database by executing python3 etl.py.

Schema Design

• The fact table songplays stores the records in log data associated with song plays i.e. records with page.
• The dimension table users stores the users in the app.
• The dimension table song stores the songs in the music database.
• The dimension table artists stores the artists the in music database.
• The dimension table time stores the timestamps of records in songplays broken down into specific units.