Data Modeling with Postgres

Overview

In this project, we apply Data Modeling with Postgres and build an ETL pipeline using Python. A startup wants to analyze the data they've been collecting on songs and user activity on their new music streaming app. Currently, they are collecting data in json format and the analytics team is particularly interested in understanding what songs users are listening to.

Song Dataset

Songs dataset is a subset of Million Song Dataset.

Sample Record :

{"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

Logs dataset is generated by Event Simulator.

Sample Record :

{"artist": null, "auth": "Logged In", "firstName": "Walter", "gender": "M", "itemInSession": 0, "lastName": "Frye", "length": null, "level": "free", "location": "San Francisco-Oakland-Hayward, CA", "method": "GET","page": "Home", "registration": 1540919166796.0, "sessionId": 38, "song": null, "status": 200, "ts": 1541105830796, "userAgent": "\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/36.0.1985.143 Safari\/537.36\"", "userId": "39"}

Schema

Fact Table

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

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, latitude, longitude

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

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

Project Files

sql_queries.py -> contains sql queries for dropping and creating fact and dimension tables. Also, contains insertion query template.

create_tables.py -> contains code for setting up database. Running this file creates sparkifydb and also creates the fact and dimension tables.

etl.ipynb -> a jupyter notebook to analyse dataset before loading.

etl.py -> read and process song_data and log_data

test.ipynb -> a notebook to connect to postgres db and validate the data loaded.

Environment

Python 3.6 or above

PostgresSQL 9.5 or above

psycopg2 - PostgreSQL database adapter for Python

How to run

Run the drive program main.py as below.

python main.py

The create_tables.py and etl.py file can also be run independently as below:

python create_tables.py 
python etl.py 

Reference:

Psycopg

PostgreSQL Documentation

Pandas Documentation