Project: Data Modeling with Postgres

Introduction

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.

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

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. 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.

{
    "artist": "Olivia Ruiz",
    "auth": "Logged In",
    "firstName": "Jahiem",
    "gender": "M",
    "itemInSession": 3,
    "lastName": "Miles",
    "length": 254.74567,
    "level": "free",
    "location": "San Antonio-New Braunfels, TX",
    "method": "PUT",
    "page": "NextSong",
    "registration": 1540817347796.0,
    "sessionId": 42,
    "song": "Cabaret Blanco",
    "status": 200,
    "ts": 1541300540796,
    "userAgent": "\"Mozilla\/5.0 (Windows NT 5.1) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/36.0.1985.143 Safari\/537.36\"",
    "userId": "43"
}

Schema for Song Play Analysis

Using the song and log datasets, a star schema has been developed that was optimized for song data analysis queries. The create_tables.py script creates the sparkifydb database and the following tables.

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

2. songs - songs in music database

   • song_id, title, artist_id, year, duration

3. artists - artists in music database

   • artist_id, name, location, latitude, longitude

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

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

To run the create_tables.py script, open a terminal window and run the following command:

python create_tables.py

ETL Pipeline

The etl.py script implements an ETL pipeline to extract data from the log files and inserts the data into the appropriate tables. To run the etl.py script, open a terminal window and run the following command:

python etl.py

The sql_queries.py script contains all of the required SQL queries and statements to create the tables and to insert data. The create_tables.py and etl.py scripts import the SQL, so the sql_queries.py script does not need to run directly.

Sample Queries

• Query the most active users:

SELECT user_id, COUNT(*) activity_cnt
FROM songplays
WHERE user_id IS NOT NULL
GROUP BY user_id
ORDER BY 2 DESC;

• Query the most common user level

SELECT level, COUNT(level) level_cnt
FROM songplays
WHERE user_id IS NOT NULL
GROUP BY level
ORDER BY 2 DESC;

• Query the most popular user agent:

SELECT user_agent, COUNT(*) agent_cnt
FROM songplays
WHERE user_agent IS NOT NULL
GROUP BY user_agent
ORDER BY 2 DESC;