A startup called Sparkify want to analyze the data they have been collecting on songs and user activity on their new music streaming app. The analytics team is particularly interested in understanding what songs users are listening to. The aim is to create a Postgres Database Schema and ETL pipeline to optimize queries for song play analysis.
Link: Data_Modeling_with_Postgres
In this project, I would be applying Data Modeling with Apache Cassandra and complete an ETL pipeline using Python. I will build a Data Model around my queries that I want to get answers for. For my use case, I want below answers:
- Get details of a song that was herad on the music app history during a particular session.
- Get songs played by a user during particular session on music app.
- Get all users from the music app history who listened to a particular song.
Link: Data_Modeling_with_Apache_Cassandra
Project is related to application of Data warehouse and AWS to build an ETL Pipeline for a database hosted on Redshift. I Will need to load data from S3 to staging tables on Redshift and execute SQL Statements that create fact and dimension tables from these staging tables to create analytics.
Use Redshift IaC script: Redshift_IaC_README
Link: Data_Warehouse_with_AWS
In this project, I will build a Data Lake on AWS cloud using Spark and AWS EMR cluster. The data lake will serve as a Single Source of Truth for the Analytics Platform. I will write spark jobs to perform ELT operations that picks data from landing zone on S3 and transform and stores data on the S3 processed zone.
Link: Data_Lake_with_Spark
In this project, I will orchestrate the Data Pipeline workflow using an open-source Apache project called Apache Airflow. I will schedule our ETL jobs in Airflow, create project related custom plugins and operators and automate the pipeline execution.