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.
At some point, your data gets too big for one machine. That’s not a hypothetical. Netflix, Google, Amazon, they all hit that wall years ago. The question is: what do you do when a single server can’t keep up?
Chapter 5 is where things get interesting if you come from a traditional database background. We are leaving the nice structured world of BigQuery and entering the territory of raw files, distributed storage, and the Hadoop ecosystem. Welcome to the data lake.
Previous: Deep Look at MapReduce: How Hadoop Processes Data
In the last post, we looked at the basics of MapReduce. But in the real world, your data is rarely in one single file. You usually have a few different datasets that you need to combine. This is where things get a little more complex - and a lot more interesting.
Previous: SQL on Hadoop: Getting Started with Apache Hive
We’ve talked about Hive, but today we’re going under the hood. MapReduce is the engine that actually does the heavy lifting in Hadoop. Sridhar Alla’s third chapter is a deep look at how this framework takes a massive pile of data and turns it into something useful.
Previous: Setting Up Your Hadoop 3 Cluster: A Step-by-Step Guide
Now that we’ve got a cluster running, let’s talk about why we bother with all this complexity in the first place. Chapter 2 of Sridhar Alla’s book takes a step back to look at the big picture of data analytics.