Data Modeling with PostgreSQL

Udacity Data Engineer Nanodegree Project

Purpose

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.

Project Description

In this project the fact and dimension tables for a star schema for a particular analytic focus are defined. Also an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL is written.

Available Datasets

Songs Library

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

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}

Songplays Log

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

And below is an example of what the data in a log file, 2018-11-12-events.json, looks like.

Schema for Song Play Analysis

Fact Table

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

users - users in the app

• user_id, first_name, last_name, gender, level

songs - songs in music database

• song_id, title, artist_id, year, duration

artists - artists in music database

• artist_id, name, location, latitude, longitude

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

• start_time, hour, day, week, month, year, weekday

Project Files

1. test.ipynb displays the first few rows of each table allowing to do a quick check on the database.
2. create_tables.py drops and creates tables.
3. etl.ipynb playground for reading and processing of a single file from song_data and log_data and loading the data into the tables. This notebook contains detailed instructions on the ETL process for each of the tables.
4. etl.py reads and processes files from song_data and log_data and loads them into the tables.
5. sql_queries.py contains all the SQL queries, it is imported into the last three files above.

Execution Steps

1. Run create_tables.py to create the database and tables.
2. Run etl.py to process the datasets.
3. Run test.ipynb to verify that the records were successfully inserted into each table.