Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Previous: Interview Questions Appendix
Thirteen chapters, an interview appendix, and a glossary later, here is where Data Engineering for Beginners lands.
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Previous: Building a Data Engineering Career
The appendix is a flashcard deck for interview prep. Nwokwu organized sample questions across five areas that match the interview stages from Chapter 13. I will retell the highlights here. Use this as a self-test before your next round.
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Previous: Cloud Services and Cost
Chapter 13 is the career chapter. Nwokwu shifts from teaching tools to helping you land a job. If you have been following this series, this is where the technical knowledge meets the job market.
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Previous: Cloud Data Engineering Basics
The second half of Chapter 12 answers two questions every data engineer eventually faces: how much of the stack do you want to manage yourself, and how do you keep the cloud bill from giving finance a heart attack?
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Previous: Big Data, Spark, and Hadoop
Chapter 12 opens with a history lesson that actually helps. Nwokwu walks from mainframes to on-prem servers to AWS in 2006, when cloud computing became real for most developers. The shift matters for data engineering because modern pipelines almost always run on someone else’s hardware.
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412, Wiley 2025)
Chapter 11 is where the book stops talking about “a lot of data” in the abstract and starts explaining how companies like Netflix and Google actually process it. Nwokwu’s argument is simple: once your data outgrows a single machine, you need distributed systems. Full stop.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Data governance sounds boring until you realize it is the reason you do not get fined millions or repeat your life story to every department that calls. Chapter 10 ties together security and quality from the previous chapters into one framework.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 9 opens with the 2016 Uber breach. Hackers found API keys in a private GitHub repo, accessed AWS, stole 57 million user records, and Uber paid $100,000 to cover it up. Weak access controls, no real-time monitoring, credentials sitting in plain sight. That story sets the tone for everything that follows.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 8 is a wake-up call. You can build the fanciest warehouse and the cleanest pipelines, but if the data itself is garbage, none of it matters. Nwokwu makes the case that data quality is not just a technical checkbox. It is a business strategy.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
The second half of Chapter 7 answers a question every data team hits eventually: how do you run live dashboards AND historical reports without building two completely separate systems? Then it gets practical with orchestration and a hands-on Airflow lab.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 7 is where the book stops talking about storage and starts talking about movement. Data pipelines are the plumbing. They collect, ingest, process, store, and serve data. Sometimes on a schedule, sometimes the second an event happens.
Chapter 6 of Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) is a big one. It moves beyond traditional databases into the storage systems that power modern analytics. The fictional bakery Dough & Delight runs through every example, which keeps things grounded.
The second half of Chapter 5 in Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) answers the question every data engineer eventually faces: your schema is clean, but your queries are slow. Now what?
Chapter 5 of Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) shifts from writing queries to designing the databases those queries run against. And honestly, this might be the most important chapter for long-term data engineering work.
The second half of Chapter 4 in Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) is where SQL goes from “filter a table” to “actually analyze data across tables.” This is the part that makes SQL feel like a real data engineering skill.
Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) picks up right where the database fundamentals chapter left off. Chapter 4 is where SQL stops being abstract and starts feeling like a real tool you will use every day as a data engineer.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 3 zooms in on storage. After the life cycle overview, Nwokwu focuses on databases, the tool data engineers spend a huge amount of time with. This chapter is dense but practical. It covers what databases are, how relational and NoSQL systems differ, and how to pick the right one for your project.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 2 is where Data Engineering for Beginners stops talking about data in general and starts defining the job. Nwokwu answers the question everyone asks: what does a data engineer actually do all day?
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
Chapter 1 of Data Engineering for Beginners opens with a line you have probably heard before: data is the new oil. Nwokwu does not just repeat the slogan. She breaks down what data actually means, where it came from, and why the type of data you are working with changes everything downstream.
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
I picked up Data Engineering for Beginners because I kept hearing about data engineering at work without fully understanding what the job actually looked like day to day. Analysts talk about dashboards. ML folks talk about models. But who builds the pipes that get clean data to them? That gap is exactly what this book tries to fill.