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 me on the project.

My role is to create a database schema and ETL pipeline for this analysis. I'll be able to test the database and ETL pipeline by running queries given to me by the analytics team from Sparkify and compare my results with their expected results.

In this project, I will apply what I've learned on data modeling with Postgres and build an ETL pipeline using Python.

To complete the project, I need to define fact and dimension tables for a star schema for a particular analytic focus, and write an ETL pipeline that transfers data from files in two local directories into these tables in Postgres using Python and SQL.

Song Dataset

This 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 is a filepath to a file in this dataset.

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

This 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 I'll be working with are partitioned by year and month. For example, here is a filepath to a files in this dataset.

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

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

For the purpose of this project, I will create a star schema, due that it brings benefits such as:

1. Denormalized tables
2. Allows for simplified queries
3. Allows for fast aggregations

Using the songs and log datasets, I will create a star schema optimized for queries on song play analysis.

This includes the following tables:

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

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

There are three separate python script files:

• sql_queries.py: Has all statements to CREATE/ DROP tables, In addition it has a song SELECT query to help find song ID and artist ID based on the title, artist name and duration of the song.

• create_tables.py: This script stablishes connection to the database and CREATES/DROPS all tables based on information provided in sql_queries.py script.

• etl.py: Contains the ETL Pipeline to Extract data from JSON files, transforms the files and inserts the data into corresponding tables.

%sql postgresql://student:[email protected]/sparkifydb

'Connected: student@sparkifydb'