The book opens with a simple claim: data is the new oil. You’ve probably heard that phrase a hundred times. But Nwokwu doesn’t just drop the cliche and move on. She actually walks you through why that comparison holds up, starting from thousands of years ago.
I picked up “Data Engineering for Beginners” by Chisom Nwokwu (Wiley, 2026, ISBN: 9781394325412) a few weeks ago. I was looking for something that explains data engineering from scratch, without assuming you already know half the field. This book does exactly that.
Up until now in the book, we built BigQuery tables by hand, wrote queries in the console, and loaded data manually. That works for learning, but nobody does that in production. In production, you need things to run on their own, on schedule, without you babysitting them at 5 AM.
You probably watched Moneyball. Brad Pitt, baseball, small team beats the rich guys using numbers. Good movie. But what the movie really shows is something bigger. It shows what happens when you take data seriously. And that is basically what data science is about.
Chapter 3 of Laura Funderburk’s book is where the rubber meets the road. We stop talking theory and start looking at an actual framework you can use to build real NLP pipelines. That framework is Haystack 2.0 by a company called deepset.
This is post 9 in my Data Engineering with AWS retelling series.
You have your whiteboard architecture from Chapter 5. You know who your data consumers are and what they need. Now it is time to actually move data. Chapter 6 covers data ingestion – getting data from wherever it lives into your AWS data lake. This first part focuses on batch ingestion from databases and files. Part 2 covers streaming.
In Part 1 we loaded CSV files into BigQuery and built a simple warehouse from a MySQL export. Now the book throws a second scenario at us: bike-sharing data. More tables, daily batch loading, and a real question that every data engineer has to face sooner or later. How do you actually model your data so that business people can use it without calling you every five minutes?
I have been working in tech for over 20 years. Seen a lot of trends come and go. But data science is not a trend. It is here to stay. And honestly, I wanted a book that explains the whole thing from the ground up without assuming I already know everything.
Parts 1 and 2 of this chapter covered transformer architecture, the SLM/RLM split, context engineering strategies, and the Haystack + LangGraph hybrid architecture. Now Funderburk closes the chapter with two topics that every developer building LLM applications needs to understand: vector stores and the economics of inference.
In Part 1, we covered how transformers work and how models split into small language models (SLMs) and reasoning language models (RLMs). Now Funderburk shifts to a big question: how do you actually interact with these models in a reliable way?