“Big data” can sound like a buzzword, but behind it is a real and well-paid field: storing, moving and analysing data at a scale a single database can’t handle. If you already know SQL, you’re closer than you think. Data warehouses, pipelines and Spark all rely on SQL-style thinking.
These five books take you from the big picture to hands-on data engineering.
Disclosure: some links on this page are affiliate links. If you buy through them, SimplifyDatabase may earn a small commission at no extra cost to you. It helps keep the site free.
Who this list is for: SQL users who want to move towards data engineering, data warehouses or analytics platforms: understanding pipelines, warehouses, lakes and distributed processing, and how SQL fits into them.
Not you? If you mainly work with one business database and want to write better queries, the main SQL books list or the performance list will help you more right now.
Table of Contents
Quick Comparison
| Book | Level | Best for |
|---|---|---|
| Big Data: A Revolution That Will Transform How We Live, Work, and Think | Non-technical | Understanding why big data matters |
| Fundamentals of Data Engineering | Beginner to intermediate | The map of the whole data engineering field |
| The Data Warehouse Toolkit (3rd Edition) | Intermediate | Designing data warehouses and reporting models |
| Data Pipelines Pocket Reference | Intermediate | Building ELT pipelines in practice |
| Learning Spark (2nd Edition) | Intermediate | Processing large data sets with Apache Spark |
1. Big Data: A Revolution That Will Transform How We Live, Work, and Think
Authors: Viktor Mayer-Schönberger and Kenneth Cukier
This is the non-technical one. It explains, through stories and examples, how large data sets changed business, science and government, and the privacy questions that came with it.
Why I recommend it: It’s an easy, interesting read that gives you the “why” before you dive into the “how”. Good for managers, students and curious beginners.
Keep in mind: there’s no code, and it’s from 2013, so some examples feel dated.
2. Fundamentals of Data Engineering
Authors: Joe Reis and Matt Housley (O’Reilly)
If you’re wondering what data engineers actually do, start here. The book describes the data engineering lifecycle (generation, storage, ingestion, transformation and serving) and the “undercurrents” that run through it, like security, data management and orchestration. It focuses on principles rather than specific tools, so it won’t go out of date quickly.
Why I recommend it: It gives you a mental map of the whole field, so every new tool you meet has a place to fit.
Keep in mind: it’s conceptual. You’ll need other books or courses for hands-on practice.
3. The Data Warehouse Toolkit (3rd Edition)
Authors: Ralph Kimball and Margy Ross (Wiley)
The definitive guide to dimensional modelling: fact tables, dimension tables, star schemas and slowly changing dimensions. It walks through case studies from retail, finance, healthcare and more, showing how to model each one for reporting.
Why I recommend it: Star schemas are still the backbone of most reporting systems, Power BI models and data warehouses. This book teaches them better than anything else.
Keep in mind: it’s long and best used chapter by chapter, starting with the retail case study.
4. Data Pipelines Pocket Reference
Author: James Densmore (O’Reilly)
A short, practical guide to moving data from source systems into a warehouse. It covers extracting data from databases and APIs, loading it, transforming it with SQL, validating it, and orchestrating it all, with Python and SQL examples.
Why I recommend it: It turns pipeline theory into concrete steps you can copy at work, and it’s quick to read.
Keep in mind: it’s a pocket reference, so it stays brief on each topic.
5. Learning Spark (2nd Edition)
Authors: Jules S. Damji, Brooke Wenig, Tathagata Das and Denny Lee (O’Reilly)
Apache Spark is the most widely used engine for processing data that’s too big for one machine. This edition is written by people from Databricks and focuses on Spark 3, DataFrames and Spark SQL, so SQL users feel at home quickly. It also covers streaming, Delta Lake and machine learning pipelines.
Why I recommend it: It’s the friendliest way into Spark for people who already think in SQL.
Keep in mind: you’ll get more from it if you know a little Python or Scala.
A Suggested Reading Order
- Just curious: Big Data: A Revolution.
- Thinking about a data engineering career: Fundamentals of Data Engineering, then Data Pipelines Pocket Reference.
- Building reports or a warehouse: The Data Warehouse Toolkit.
- Data no longer fits in one database: Learning Spark.
Frequently Asked Questions
Do I need to learn Python for data engineering?
For most data engineering jobs, yes, alongside SQL. SQL does most of the transformation work in warehouses, while Python is used for pipelines, automation and tools like Spark. Strong SQL is the best foundation, and it’s what this site focuses on.
Which book should a SQL developer read first?
Fundamentals of Data Engineering gives the clearest overview of the whole field and how the pieces fit together. If your goal is reporting and warehouses specifically, The Data Warehouse Toolkit is the classic guide to dimensional modelling.
Is The Data Warehouse Toolkit still relevant with cloud warehouses?
Yes. Cloud warehouses changed how data is stored and paid for, but facts, dimensions and star schemas are still the most common way to model data for reporting.
Is big data only for very large companies?
The big-data tools are, mostly. But the ideas, such as pipelines, warehouses and separating reporting from live systems, help any company whose reports are slowing down the main database.
Start With Our Free Big Data Guides
Get the basics first with what is big data, the five Vs of big data, data lakes vs data warehouses vs lakehouses and big data vs data science vs data analytics.
Want the basics before you pick a book? Our free guides explain the core ideas behind data engineering with SQL examples: OLTP vs OLAP, star schema vs snowflake schema and ETL vs ELT.
More book picks are in the SQL Books menu: beginner SQL books, database design books and our in-depth book reviews.