Data Lake with s3 and Pyspark

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Introduction

A music streaming startup, Sparkify, has grown their user base and song database even more and want to move their data warehouse to a data lake. Their data resides in S3, in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.

I am tasked with building an ETL pipeline that extracts their data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow their analytics team to continue finding insights in what songs their users are listening to.

Source Data

The datasets are extracted from an s3 buckekts. Here are the links for each of them

• Song data : s3://udacity-dend/song_data
• Log data : s3://udacity-dend/log_data

Both datasets have json extentions

Requirements

• Python3
• Spark
• AWS credentials

  Access_key_id = ***********

  Acess_secret_key = ********

NOTE!! Dont make this public, always hash your key and secret key when upoading to a public repository

Data Model/Schema

The table schema adopted here is the start table with Facts and dimension table as follows,

Dimension Tables

1. users - users in the app (resides in log database)

   • user_id, first_name, last_name, gender, level

2. songs - songs in music database (resides in song database)

   • songs_id, title, artist_id, year, duration

3. artists - artist in music database(resides in song database)

   • artist_id, name, location, latitude, longitude

4. time - timestamps of records in songplays broken down in units (resides in log database)

   • start_time , hour, day, week, month, year, weekday

Fact Table

1. songplays - records in log data assosicated with songs plays i.e records with page Next Song
   • songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent

Table Info

Save mode and duplicates

• Both dimension and fact tables were created with duplicate values dropped.
• songplays and users table were written with the save mode append
• songs , artist, time table were written with the save mode overwrite

Partitioned By

Three tables contained partitions

• songs table was partitioned by year and artist_id
• time table was partitioned by year and month
• songplays table was partitioned by year and month

Project Template

• etl.py reads data from an s3 bucket, processes the data using Spark, writes back to an s3 bucket.

• dl.cfg contains your AWS credentials

  NOTE! without this , you cant read or write to an S3 bucket

• README.md provides detailed description on your process