eddiecp426 / udacity-sparkify-nosql-db Goto Github PK

Sparkify (fake company) is back at it again and wants to continue to analyze the data they've been collecting on music and user activity on a new music streaming app. This time, the data team is interested in specific queries. Sparkify wants to create a NoSQL Database using Apache Cassandra to optimize for fast reads and writes. In this project, I created a keyspace with tables to answer simple query questions.

• Pandas
• Apache Cassandra (CQL)

The data is in the form of csv files where they will all be processed by a function that will create tables and insert data.

2018-11-01-events.csv

Since joins are not optimized for NoSQL Databases, 3 Seperate tables will answer 3 different 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

SELECT artist, song_title, song_length FROM song_details WHERE session_id = 338 AND item_in_session = 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

SELECT artist_name, song_title, user_first_name, user_last_name FROM artist_details WHERE user_id = 10 AND session_id = 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'

SELECT user_first_name, user_last_name FROM user_details WHERE song_title = 'All Hands Against His Own';

Project_1B.ipynb - Running this notebook iterates through each event file in event data to procress and creates a new CSV file. This file also creates denormalized tables and runs Sparkify's queries.

event_datafile_new.csv - This file contains a clean CSV ready for tables creation.