➔ 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.
➔ AWS Auxiliary Services for Data Engineering: Compute, Storage, ML, and More
Previous: AWS Analytics Services
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 3 Part 1 covered the core analytics services: Kinesis, Glue, Redshift, Athena, and friends. Those services don’t exist in a vacuum though. They need compute to run on, databases to pull data from, storage to land results, networking to keep things secure, and monitoring to know when something breaks.