Udacity Data Engineering Nanodegree | Project 1 | Data Modeling with Postgresql
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.
Project Description In this project, you'll apply what you've learned on data modeling with Postgres and build an ETL pipeline using Python. To complete the project, you will 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.
For the purpose of this project we are required to create a database schema and ETL pipeline that makes sense for our use case and allows the analytics team to answer questions related to what songs users listen to, perhaps when they listen and further it is possible to perform customer or 'user' segmentation and gain further insight into the users and the songs they listen to.
Here we can see our star schema and can see the fact and dimension table relations.
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.
If you would like to look at the JSON data within log_data files, you will need to create a pandas dataframe to read the data. Remember to first import JSON and pandas libraries.
df = pd.read_json(filepath, lines=True)
For example, df = pd.read_json('data/log_data/2018/11/2018-11-01-events.json', lines=True) would read the data file 2018-11-01-events.json.
data folder= Contain ours json files, from 2 seperate directories
create_tables.py= Creates tables if they do not exist, and drops tables if they already exist. This allows you to reset your tables so ETL scripts run as they should.
ERD.jpeg= visualization of our star schema and highlights primary keys for each dimension table, with the fact table in the center.
etl.ipynb= This file is used to build and develop the ETL process using data found in both song_data and log_data and loads the data accordingly into the appropriate tables.
etl.py= The etl.ipynb file acting as a test run and development stage leading up to implementing the etl.py file, where a larger set of data is processed in accordance with etl.ipynb
sql_queries.py= This file contains all the SQL queries required for our project. The create_tables.py, etl.ipynb and etl.py files all access the sql_queries.py file in order to run queries.
test.ipynb= This file allows us to run and test to make sure our queries run as they should.
- Open Terminal and run create_tables.py (! python create_tables.py)
- Execute etl.py in Terminal (! python etl.py)