Data Modeling with Postgres

Database context

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.

The goal is to create a database schema and ETL pipeline for this analysis. Also be able to test the database created and ETL pipeline by running queries.

The project workspace content

An explanation of the files in the repository

test.ipynb displays the first few rows of each table to let you check your database.
create_tables.py drops and creates your tables. You run this file to reset your tables before each time you run your ETL scripts.
etl.ipynb reads and processes a single file from song_data and log_data and loads the data into your tables. This notebook contains detailed instructions on the ETL process for each of the tables.
etl.py reads and processes files from song_data and log_data and loads them into your tables. You can fill this out based on your work in the ETL notebook.
sql_queries.py contains all your sql queries, and is imported into the last three files above.

Database schema design and ETL pipeline

Schema for Song Play Analysis

Using the song and log datasets, you'll need to create a star schema optimized for queries on song play analysis. This includes the following tables.

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 2. 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

How to run the Python scripts

You will not be able to run test.ipynb, etl.ipynb, or etl.py until you have run create_tables.py at least once to create the sparkifydb database, which these other files connect to.

Run create_tables.py to create the database and tables.
Run test.ipynb to confirm the creation of your tables with the correct columns. Make sure to click "Restart kernel" to close the connection to the database after running this notebook.

Dataset and ETL pipeline strategy

The dataset samples are stored into /data/ folder, with:

data
|   log_data
|   song_data

log_data samples:

{
    "artist":null,
    "auth":"Logged In",
    "firstName":"Walter",
    "gender":"M",
    "itemInSession":0,
    "lastName":"Frye",
    "length":null,
    "level":"free",
    "location":"San Francisco-Oakland-Hayward, CA",
    "method":"GET",
    "page":"Home",
    "registration":1540919166796.0,
    "sessionId":38,"song":null,
    "status":200,
    "ts":1541105830796,
    "userAgent":"\"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_9_4) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/36.0.1985.143 Safari\/537.36\"",
    "userId":"39"
}

song_data samples:

{
    "num_songs": 1,
    "artist_id": "ARD7TVE1187B99BFB1",
    "artist_latitude": null,
    "artist_longitude": null,
    "artist_location": "California - LA",
    "artist_name": "Casual",
    "song_id": "SOMZWCG12A8C13C480",
    "title": "I Didn't Mean To",
    "duration": 218.93179,
    "year": 0
}

ETL pipeline:

based on these two dataset samples and the goal described, the following python functions were designed into the file etl.py.

process_song_file()
process_log_file()
process_data()

at the end, the final results for song play analysis:

songplay_id	start_time	user_id	level	song_id	artist_id	session_id	location	user_agent
0	2018-11-30 12:22:07	91	free	None	None	829	Dallas-Fort Worth-Arlington, TX	Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; WOW64; Trident/6.0)