In this project, I applied Data Modeling with Postgres and built 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 H5 format and the analytics team is particularly interested in understanding what songs users are listening to.
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}
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
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
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.py will read and process song_data
Python 3.9 or above
PostgresSQL 9.5 or above
psycopg2 - PostgreSQL database adapter for Python
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
