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 have hired a data engineer to create a Postgres database with tables designed to optimize queries on song play analysis, and bring you on the project. As part of this I built a database schema and ETL pipeline for this analysis.
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 file paths 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.
python create_tables.py python etl.py - test.ipynb displays the first few rows of each table to let you check your database.
- create_tables.py drops and creates your tables. You run this file to reset your tables before each time you run your ETL scripts.
- etl.ipynb reads and processes a single file from song_data and log_data and loads the data into your tables. This notebook contains detailed instructions on the ETL process for each of the tables.
- etl.py reads and processes files from song_data and log_data and loads them into your tables.
- sql_queries.py contains all your sql queries, and is imported into the last three files above.
Using the song and log datasets, I created a star schema optimized for queries on song play analysis. This includes the following tables.
- songplays - records in log data associated with song plays
- 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
Pipeline is split into two scripts and a validation notebook;
- create_tables.py which builds the databases required
- run create_tables.py before running etl.py to reset your tables.
- etl.py runs the data pipeline to build the fact and dimension tables
- Run test.ipynb to confirm your records were successfully inserted into each table.