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🚀 Data Modeling With Apache Cassandra + Docker

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ERD project Sparkify
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Overview

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 this project, we create data modeling with Apacahe Cassandra and build ETL pipeline using python. Study Case : A startup in indonesia wants to analyze the data they have been collecting on songs and user csv on their new music streaming app. Currently, this startup collecting data log events in csv format and the analytics team is particularly interested in understanding what songs user are listening to.

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Song Dataset

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Songs dataset is a subset of [Million song dataset]((http://millionsongdataset.com/)

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Sample record:

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{\"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}
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Log Dataset

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Logs dataset is generated by Event Simulator

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Sample Record :

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{\"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\"}
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Schema

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Fact Table

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songplays - records in log data associated with song plays i.e. records with page NextSong

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songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent
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Dimension Tables

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user_session - users session in the app

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session_id,user_id,artist, firstname, iteminsession, lastname
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user_songs - user play songs

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song, user_id, firstname, lastname
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session_item - item in session

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session_id,iteminsession, artist, length, song
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Project Files

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sql_queries.py -> contains sql queries for dropping and creating fact and dimension tables. Also, contains insertion query template.

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create_tables.py -> contains code for setting up database. Running this file creates sparkify and also creates the fact and dimension tables.

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modeling-data.ipynb -> a jupyter notebook for testing.

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etl.py -> read and process file in event_data directory

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lib.py -> import library that used

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event_datefile_new.csv -> output etl process

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Environment

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Python 3.6 or above

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Apache Cassandra

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cassandra - Cassandra database adapter for Python

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How to run

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Run the drive program main.py as below.

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python main.py
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The create_tables.py and etl.py file can also be run independently as below:

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python create_tables.py \npython etl.py
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Reference:

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Cassandra

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Cassandra Documentation

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