Project 1: Data modeling with Postgres

This Udacity Data Engineering nanodegree project creates a postgres database sparkifydb for a music app, Sparkify. The purpose of the database is to model song and log datasets (originaly stored in JSON format) with a star schema optimised for queries on song play analysis.

Note: The whole exercise can be run in a docker container. See instruction below.

Schema design and ETL pipeline

The star schema has 1 fact table (songplays), and 4 dimension tables (users, songs, artists, time). DROP, CREATE, INSERT, and SELECT queries are defined in sql_queries.py. create_tables.py uses functions create_database, drop_tables, and create_tables to create the database sparkifydb and the required tables.

Extract, transform, load processes in etl.py populate the songs and artists tables with data derived from the JSON song files, data/song_data. Processed data derived from the JSON log files, data/log_data, is used to populate time and users tables. A SELECT query collects song and artist id from the songs and artists tables and combines this with log file derived data to populate the songplays fact table.

Song play example queries

Simple queries might include number of users with each membership level.

SELECT COUNT(level) FROM users;

Day of the week music most frequently listened to.

SELECT COUNT(weekday) FROM time;

Or, hour of the day music most often listened to.

SELECT COUNT(hour) FROM time;

A docker image

I've created a docker image postgres-student-image on docker hub, from which you can run a container with user 'student', password 'student', and database studentdb (the starting point for the exercise). You do not need to install postgres (it runs in the container).

To download the image, install docker which requires you to create a username and password. In a terminal, log into docker hub (you'll be prompted for your docker username and password)

docker login docker.io

Pull the image

docker pull onekenken/postgres-student-image

Run the container

docker run -d --name postgres-student-container -p 5432:5432 onekenken/postgres-student-image

The create_tables.py pre-defined connection conn = psycopg2.connect("host=127.0.0.1 dbname=studentdb user=student password=student") will now connect to the container.

To stop and remove the container after the exercise

docker stop postgres-student-container
docker rm postgres-student-container

kenhanscombe/project-postgres

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