songplays datamart provide details about the musical taste of our customers and can help us to improve our recomendation system

Songplays User activity datamart

The following document describes the model used to build the songplays datamart table and the respective ETL process.

Table of Contents

• About
• Getting Started
• Data Model and Schema
• Deployment
• Built Using
• Authors

About

The songplays datamart provide details about the musical taste of our customers and can help us to improve our recomendation system.

This document describes the model of songplays table datamart on sparkify_app schema inside a container sparkify_postgres, and the Python code to load new data. The production directory and data must be simmilar to those in mnt/data/log_data and mnt/data/song_data paths in this repository.

🏁 Getting Started

First you need to have the right permissions to access the source files and write them into sparkify_app tables that generates the songplays datamart table. Contact the owners or your team leader for more information.

Data Model and Schema

Source files and owners

Prerequisites

To run this project first you need to install the Docker Engine for your operational system and Docker Compose.

After installing and configuring the Docker tools, download this repository and create a folder named postgres that will store all sparkify_postgres service data. To build the proper images and run the services, just execute the following command inside this repository:

docker-compose up

If the service runs successfully you should see something like this:

...
sparkify_python      | 28/30 files processed.
sparkify_python      | 29/30 files processed.
sparkify_python      | 30/30 files processed.
sparkify_python exited with code 0

You can also check the job by following these steps:

• Open your browser and access localhost:16543:

  • Enter with the following credentials to authenticate:
    • e-mail: [email protected]
    • password: sp4rk1fy

• After you log in, click on the Servers option at the upper corner on the left:

  • You will be asked to enter with the PostgreSQL credentials:
    • User: sparkifypsql
    • Password: p4ssw0rd

• Select the Query Tools under the Tools menu:

• Under the Query Editor, run the following query:

  SELECT * FROM sparkify_app.songplays WHERE song_id is NOT NULL and artist_id is NOT NULL;

  • You should get only 5 rows.

Microservice architecture

The following image represents the microservice architecture for this project:

Where:

• sparkify_python: runs all Python scripts and stores raw data.
• sparkify_postgres: runs Postgre and stores the database.
• sparkify_pgadmin: runs the pgAdmin tool to monitor the sparkify_postgres service.

⛏️ Built Using

• Dbeaver - Database tool.
• Docker Compose - Tool to run multi-container applications.
• Docker Engine - Container engine.
• pandas - Data analysis and data wrangling tool.
• pgAdmin - PostgreSQL tool.
• psycopg2 - Database adapter for Python.
• PostgreSQL - Reletional database management system.

✍️ Authors

• @lkellermann - Idea & Initial work