➔ Data Modeling for Redshift, DynamoDB, and Data Lakes on AWS
Previous: Data Stores and Lifecycle
Book: AWS Certified Data Engineer Associate Study Guide Authors: Sakti Mishra, Dylan Qu, Anusha Challa Publisher: O’Reilly Media ISBN: 978-1-098-17007-3
Data modeling is one of those topics that sounds academic until you get it wrong in production. Then it becomes very real, very fast. This section of Chapter 5 covers data modeling strategies for three different AWS services: Amazon Redshift, Amazon DynamoDB, and S3 data lakes. Each has its own rules, trade-offs, and gotchas.
➔ Data Engineering Fundamentals: Databases, Spark, Data Lakes, and AWS Basics
Previous: Certification Essentials
Book: AWS Certified Data Engineer Associate Study Guide Authors: Sakti Mishra, Dylan Qu, Anusha Challa Publisher: O’Reilly Media ISBN: 978-1-098-17007-3
Chapter 2 is called “Prerequisite Knowledge for Aspiring Data Engineers.” It covers the foundation you need before touching any AWS service. Databases, big data concepts, processing frameworks, data lakes, warehouses, ETL patterns, CI/CD, and AWS basics.
If you already work in tech, a lot of this will feel like a refresher. It’s nice to have it all in one place though. Some of these topics honestly deserve more attention than people give them during cert prep.