We’ve all heard the terms “Data Warehouse” and “Data Lake,” but do you actually know why we keep switching between them? In Chapter 4 of Big Data on Kubernetes, Neylson Crepalde gives a masterclass on how data architecture has evolved to keep up with the modern world.
If you want to run big data workloads on Kubernetes, you have to understand how the system is actually put together. It’s not just “magic magic cloud stuff”—it’s a carefully coordinated cluster of machines.
This is post 8 in my Data Engineering with AWS retelling series.
You have learned about data engineering principles, data architectures, the AWS toolkit, and data governance. Now comes the part where it all comes together. Chapter 5 is about designing an actual data pipeline. Not writing code yet. Just thinking. Planning. Drawing on a whiteboard.