/ Data Analysis

Containerized end-to-end analytics of Spotify data using Python

Containerized end-to-end analytics of Spotify data using Python

Spotify User Analytics

Containerized end-to-end analytics of Spotify data using Python, dbt, Postgres, and Metabase.

In this project, we will be analyzing our listening history, top tracks & artists, and genres from Spotify. Here are the tools that we will be using:

  • Python - Scraping data from Spotify API endpoints and saving files to CSV
  • Postgres - Our database wherein data will be stored into and queried from
  • dbt (Data Build Tool) - Data modeling tool to transform our data in staging to fact, dimension tables, and views
  • Metabase - Dashboarding tool to analyze our data
  • Docker - Containerizing our applications i.e. Postgres, dbt, and Metabase

Project Files

  • app
    • main.py - Our main ETL script that fetches data from the Spotify API endpoints and saves them to CSV
    • util.py - Utility helper file that contains a custom class SpotifyUtil
    • config_template.py - This is where we will store our credentials
  • dbt
    • models - Contains the sql scripts and schema.yml files that will be used when we run our transformations
    • dbt_entrypoint.sh - Script that will server as our entrypoint when running the dbt container
    • Dockerfile - Contains the commands to create the custom Docker image
    • dbt_project.yml - YAML file to configure dbt
    • packages.yml - YAML file for test dependencies
    • profiles.yml - YAML file to configure connection of dbt to postgres
  • metabase
    • metabase.db - Metadata database of Metabase for the dashboard
  • docker-compose.yml - YAML file to orchestrate Docker containers composition

Workflow

The diagram below illustrates the systems design and how the workflow will go.

dbt-spotify-analytics-1

Let's break this down into major steps

  • Setup
  • Get Spotify data
  • Build Docker containers
  • Transform, model, and load data to Postgres DB using dbt
  • Serve to Metabase dashboard

Setup

  • cd to this directory

  • Open a terminal, create a Python virtual environment using:

    Windows
    > python -m venv venv
    
    Mac/Linux
    $ make build
    
    

    then activate it by executing

    Windows:
    > venv\Scripts\activate.bat
    

    (For Windows) Install dependencies using:

    > python -m pip install -r requirements.txt
    
  • While dependencies are being installed, navigate to Spotify Developer Page and login

  • Create an app and note down the Client ID and Client Secret, make sure to add a redirect uri in Settings i.e. http://localhost:8888/callback/

  • Fill the details in config_template.py and rename it to config.py

Get Spotify data

  • Run the main Python script to fetch the data from Spotify using:

    Windows
    > python app\main.py
    
    Mac/Linux
    $ make run
    
  • While the script is running, it will redirect to a webpage that looks like the one below, and just click AGREE

token

p.s. follow [me](https://open.spotify.com/user/12139930362) for nice tunes! 😁

Build Docker containers

Now that we have the CSV files in the data folder, we can now build our Docker containers using this command:

docker-compose up

This command will build our dbt, postgres, and metabase containers. This will also run our data loading, transformations, and modeling in the background.

Transform, model, and load data to Postgres DB using dbt

During docker-compose, dbt runs the following commands

  • dbt init spotify_analytics: Creates the project folder
  • dbt debug: Checks the connection with the Postgres database
  • dbt deps: Installs the test dependencies
  • dbt seed: Loads the CSV files into staging tables in the database in postgres
  • dbt run: Runs the transformations and loads the data into the database
  • dbt docs generate: Generates the documentation of the dbt project
  • dbt docs serve: Serves the documentation on a webserver

Navigating to http://localhost:8080 to see the documentation, we can see the lineage graph, a DAG (Directed Acyclic Graph).

dbt_docs

This shows us how the CSV files have been transformed to the fact, dimension tables and views.

Serve to Metabase dashboard

Now that the data is loaded and transformed in our database, we may now view it in http://localhost:3000.
You may need to login, the credentials are

email: [email protected] 
password: password1

metabase_login

Then you can navigate through, play around, and analyze your data. Here are some sample screenshots of the dashboards

songplays

top_artists_tracks

GitHub

Comments