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Project 2: Data Modeling with Apache Cassandra

Note: The whole exercise can be run in a docker container. See instruction below.

This Udacity Data Engineering nanodegree project creates an Apache Cassandra database sparkifyks for a music app, Sparkify. The purpose of the NoSQL database is to answer queries on song play data. The data model includes a table for each of the following queries:

  1. Give me the artist, song title and song's length in the music app history that was heard during sessionId = 338, and itemInSession = 4

  2. Give me only the following: name of artist, song (sorted by itemInSession) and user (first and last name) for userid = 10, sessionid = 182

  3. Give me every user name (first and last) in my music app history who listened to the song 'All Hands Against His Own'

Data pre-processing, ETL pipeline, and data modeling

The data are stored as a collection of csv files partitioned by date. The ETL pipeline and data modeling are written in a single jupyter notebook, Project_1B_Project_Template.ipynb.

ETL copies data from the date-partitioned csv files to a single csv file event_datafile_new.csv which is used to populate the denormalized Cassandra tables optimised for the 3 queries above. The 3 tables in the model are named after the song play query they are created to solve:

  1. songinfo_by_session_by_item includes artist, song title and song length information for a given sessionId and itemInSessionId.

  2. songinfo_by_user_by_session includes artist, song and user for a given userId and sessionId.

  3. userinfo_by_song includes user names for a given song.

The example queries are returned as pandas dataframes to facilitate further data manipulation.


Run in a Docker container

With docker installed, pull the latest Apache Cassandra image and run a container as follows:

docker pull cassandra

docker run --name cassandra-container -p 9042:9042 -d cassandra:latest

This will allow you to develop the data model (i.e., work through the jupyter notebook), without altering the provided connection code which connects to the localhost with default port 9042.

from cassandra.cluster import Cassandra

cluster = Cluster(['127.0.0.1'])
session = cluster.connect()

To stop and remove the container after the exercise

docker stop cassandra-container
docker rm cassandra-container