AWS Event-Driven Data Lakehouse with PySpark ETL
Author: FocusCraftJob Helper Usecase
Status: Draft
Duration: 1 Week Deep Dive
Executive Summary
Design and implement a robust serverless data pipeline on AWS leveraging an event-driven architecture. This project focuses on ingesting large-scale data into an S3 data lake, transforming it with PySpark on AWS Glue, and loading it into an optimized relational database (RDS) for analytics. All infrastructure is provisioned through Infrastructure as Code (IaC).
Key Skills
- AWS
- SQL
- Data Modeling
- Database Design & Optimization
- Infrastructure as Code
Project Execution Log
Stage 1: Data Modeling and Relational Database Design
In this stage, we transitioned from raw data concepts to a structured, optimized relational database. We designed a comprehensive logical and physical data model, translating it into SQL DDL. We then leveraged Terraform to provision a secure and scalable AWS RDS PostgreSQL instance, completing the setup by applying our defined schema. This sets the foundation for storing and querying our transformed data efficiently.
Deliverables
Stage 2: Event-Driven Data Ingestion with S3, Lambda, and SQS
Deliverables
Stage 3: Large-Scale Data Transformation using PySpark on AWS Glue
Deliverables
Stage 4: Infrastructure Provisioning with AWS CloudFormation
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Stage 5: End-to-End Pipeline Testing, Optimization, and Validation
Deliverables