dokisri/data-modeling-with-apache-cassandra

This will model event data to create a non-relational database and ETL pipeline for a music streaming app. We will define queries and tables for a database built using Apache Cassandra.

Data-Modeling-with-Apache-Cassandra

This will model event data to create a non-relational database and ETL pipeline for a music streaming app. We will define queries and tables for a database built using Apache Cassandra.

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 analysis team is particularly interested in understanding what songs users are listening to. Currently, there is no easy way to query the data to generate the results, since the data reside in a directory of CSV files on user activity on the app.

They'd like a data engineer to create an Apache Cassandra database which can create queries on song play data to answer the questions, and wish to bring you on the project. Your role is to create a database for this analysis. You'll be able to test your database by running queries given to you by the analytics team from Sparkify to create the results.

Project Overview

In this project, we will apply what we've learned on data modeling with Apache Cassandra and complete an ETL pipeline using Python. To complete the project, we will need to model our data by creating tables in Apache Cassandra to run queries. As part of the ETL pipeline that transfers data from a set of CSV files within a directory to create a streamlined CSV file to model and insert data into Apache Cassandra tables.

Datasets

For this project, you'll be working with one dataset: event_data. The directory of CSV files partitioned by date. Here are examples of filepaths to two files in the dataset:

event_data/2018-11-08-events.csv event_data/2018-11-09-events.csv

Project Template

To get started with the project, go to the workspace on the next page, where you'll find the project template (a Jupyter notebook file). You can work on your project and submit your work through this workspace.

The project template includes one Jupyter Notebook file, in which:

you will process the event_datafile_new.csv dataset to create a denormalized dataset
you will model the data tables keeping in mind the queries you need to run
you have been provided queries that you will need to model your data tables for
you will load the data into tables you create in Apache Cassandra and run your queries

Project Steps

Below are steps you can follow to complete each component of this project.

Modeling your NoSQL database or Apache Cassandra database

Design tables to answer the queries outlined in the project template
Write Apache Cassandra CREATE KEYSPACE and SET KEYSPACE statements
Develop your CREATE statement for each of the tables to address each question
Load the data with INSERT statement for each of the tables
Include IF NOT EXISTS clauses in your CREATE statements to create tables only if the tables do not already exist. We recommend you also include DROP TABLE statement for each table, this way you can run drop and create tables whenever you want to reset your database and test your ETL pipeline
Test by running the proper select statements with the correct WHERE clause

Build ETL Pipeline

Implement the logic in section Part I of the notebook template to iterate through each event file in event_data to process and create a new CSV file in Python
Make necessary edits to Part II of the notebook template to include Apache Cassandra CREATE and INSERT statements to load processed records into relevant tables in your data model
Test by running SELECT statements after running the queries on your database