Etl

• Mar 03, 2026
• software-engineering

Data Engineering With AWS Chapter 7 Part 2: Transforming Data - Optimization and Business Logic

This is post 12 in my Data Engineering with AWS retelling series.

In Part 1, we covered the generic data preparation transforms: converting to Parquet, partitioning, PII protection, and data cleansing. Those transforms work on individual datasets and do not need much business context. Now we get to the transforms that actually create business value. The ones that combine multiple datasets, add context, flatten structures, and produce the tables that analysts and dashboards consume.

• Feb 19, 2026
• software-engineering

Data Warehouses, Data Lakes, and Lakehouses - Data Engineering for Beginners (Ch.6)

Chapter 6 is where the book zooms out from “how to design one database” to “where does all this data actually live in a real company.” The answer: it depends on what you are trying to do with it.

• Feb 13, 2026
• software-engineering

What Is Data Engineering? Study Notes From Data Engineering With Python Ch 1

Chapter 1 of Data Engineering with Python by Paul Crickard starts with the basics. What is data engineering? What do data engineers actually do? And how is it different from data science?

• Feb 07, 2026
• software-engineering

Data Engineering With GCP Chapter 1: What Is Data Engineering Anyway?

Chapter 1 starts with a confession most of us in the data world can relate to. Adi Wijaya says he used to think data was clean. Neatly organized, ready to go. Then he actually worked with data in real organizations and realized most of the effort goes into collecting, cleaning, and transforming it. Not the fun machine learning part. The plumbing part.