The following project is part of UDACITY - Data Engineer Nanodegree. It consist in creating an ETL from data sources stored in JSON to a PostgreSQL database.
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.
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.
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.
- Execute create_tables.py to: drop tables if exist, then create the tables again. (Caution, it will delete all existing data).
- Execute etl.py to run the etl.
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