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🚀 Data Modeling With PostgresSQL

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ERD project SparkifyDB
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Overview

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In this project, we create data modeling with postgres and build ETL pipeline using python.\nStudy Case : A startup in indonesia wants to analyze the data they have been collecting on songs and user activity on their new music streaming app.\nCurrently, this startup collecting data in json 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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users - users in the app

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user_id, first_name, last_name, gender, level
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songs - songs in music database

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song_id, title, artist_id, year, duration
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artists - artists in music database

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artist_id, name, location, latitude, longitude
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time - timestamps of records in songplays broken down into specific units

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start_time, hour, day, week, month, year, weekday
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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 sparkifydb and also creates the fact and dimension tables.

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etl.ipynb -> a jupyter notebook to analyse dataset before loading.

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etl.py -> read and process song_data and log_data

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test.ipynb -> a notebook to connect to postgres db and validate the data loaded.

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Environment

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Python 3.6 or above

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PostgresSQL 9.5 or above

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psycopg2 - PostgreSQL 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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Source

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Reference:

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Psycopg

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PostgreSQL Documentation

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Pandas Documentation

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