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DEND - Data Modeling with PostgreSQL

Jun Zhu

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.

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

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

Schema for song play analysis

A star schema is employed to facilitate queries on song play analysis.

Fact table

  • songplays - records in log data associated with song plays, i.e. records with page NextSong.

    Columns: songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent

Dimension tables

  • users - users in the app.

    Columns: user_id, first_name, last_name, gender, level

  • songs - songs in music database.

    Columns: song_id, title, artist_id, year, duration

  • artists - artists in music database.

    Columns: artist_id, name, location, latitude, longitude

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

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

Installing dependencies

pip install pandas psycopg2

or

conda install pandas psycopg2

on MacOS M1.

Running Postgres locally

# Pull Postgres image from DockerHub.
docker pull postgres

# Start a new Postgres server.
docker run --name postgres-server -e POSTGRES_PASSWORD=student -e POSTGRES_USER=student -p 127.0.0.1:5432:5432 -d postgres

# Launch an existing Postgres container.
docker start <CONTAINER ID>

Getting started

Create tables in the sparkifydb database. The old tables will be dropped.

python create_tables.py

One can list all the tables using the following query

SELECT table_schema,table_name FROM information_schema.tables WHERE (table_schema='public');

Read and process files from song_data and log_data, and then load them into the tables.

python etl.py

One can also do the exploratory data analysis with the ETL pipeline in this notebook.