/Data-Warehouse-and-Dimensional-Modeling-for-Sparkify-Music-Streaming-App-using-PostgreSQL-and-Docker

Data modeling with Postgres and build an ETL pipeline using Python.

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#Data Warehouse and Dimensional Data Modeling for Sparkify Music Streaming App using PostgreSQL and Docker

ETL pipeline - Data Modeling with Postgres

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.

create a Postgres database with tables designed to optimize queries on song play analysis, my role is to create a database schema and ETL pipeline for this analysis.

Project Description

In this project, I'll complete data modeling with Postgres and build an ETL pipeline using Python [Docker Version]

Docker

Use Docker compose file to create two containers

  • Jupyter Notebook Setup
  • Initialize Postgress database server to connect to it.

file: docker-compose.yaml

Tasks

  • Define fact and dimension tables for a star schema for a particular analytic focus
  • Write an ETL pipeline that transfers data from files in two local directories into these analytical database tables in Postgres using Python and SQL.

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 file paths to two files in this dataset.

Example of song data files

    `song_data/A/B/C/TRABCEI128F424C983.json`
    `song_data/A/A/B/TRAABJL12903CDCF1A.json`

And below is an example of what a single song file, TRAABJL12903CDCF1A.json, looks like.

{"num_songs": 1, "artist_id": "ARJIE2Y1187B994AB7", "artist_latitude": null, "artist_longitude": null, "artist_location": "", "artist_name": "Line Renaud", "song_id": "SOUPIRU12A6D4FA1E1", "title": "Der Kleine Dompfaff", "duration": 152.92036, "year": 0}

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.

Example of log data files

    `log_data/2018/11/2018-11-12-events.json
     log_data/2018/11/2018-11-13-events.json`

Schema for Song Play Analysis

Fact Table

  1. 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

Dimension Tables

  1. users - users in the app
  • user_id, first_name, last_name, gender, level
  1. songs - songs in music database
  • song_id, title, artist_id, year, duration
  1. artists - artists in music database
  • artist_id, name, location, latitude, longitude
  1. time - timestamps of records in songplays broken down into specific units
  • start_time, hour, day, week, month, year, weekday

sparkifydb_erd

Steps

  1. Build SQL queries
  • Create Tables queries
  • Insert data into tables queries
  • Drop tables queries
  1. Develop ETL processes for each file
  • etl.ipynb notebook to develop ETL processes for each table. At the end of each table section, or at the end of the notebook, run test.ipynb to confirm that records were successfully inserted into each table.

  1. Build complete ETL pipeline

  2. test the result aginst defined queries

How to run the Project

  1. clone the repo
  2. cd to the project dir
  3. RUN docker-compose up
  4. install requirements
  5. Run create_tables.py
  6. Run etl.py
  7. test.ipynb to view and test the results.