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.

Project Overview

In this project, I applied what I learned on data modeling with Postgres and build an ETL pipeline using Python. To complete the project, I defined fact and dimension tables for a star schema for a particular analytic focus, and wrote an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.

PreRequisites

Python ~3.7
PostgresSQL

Important pip packages used in this project

Project Datasets

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, "art

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. 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 log 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 log data associated with song plays i.e. records with page NextSong

Dimension Tables

users - users in the app

songs - songs in music database

artists - artists in music database

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

How to run?

Run create_tables.py to create your database and tables.
Run etl.py to build an ETL pipeline.

If you're running into issues:

contact me on twitter

harshsahu97/Data-Modeling-with-Postgres