Hadoop is quickly becoming a staple in big data. It’s a huge framework spanning many different technologies that help with distributed data storage and data analysis.
But with such a large topic it can be intimidating to even think about starting.
That’s why I curated this post filled with the top 20 best books covering Hadoop from beginner topics to advanced studies. And if you’re trying to learn about Hadoop’s related tools you might enjoy our articles covering books for Hive, HBase, and Apache Spark.
If you’re just getting started with Hadoop then my #1 suggestion would be Hadoop: The Definitive Guide. It’s currently in its 4th edition updated for the latest version of Hadoop. Over 800 pages you’ll learn the fundamental concepts and tools that make Hadoop the best big data management/storage platform.
Data processing gets a lot easier once you understand Hadoop’s capabilities. The goal of Hadoop in Action is to teach Hadoop and MapReduce to help anyone from project leads to sysadmins analyze big data.
Early chapters take a snail’s pace approach by covering all the fundamentals of Hadoop, why it exists, and what it’s good for. You’ll also learn about SQL DB’s and MapReduce, along with the basics of setting up a Hadoop environment.
Later chapters cover exercises that help you learn MapReduce jobs and best programming practices. This book is meant for newbies with no Hadoop experience but it’s almost necessary to have some experience in programming. If not you’ll move a bit slower through the exercises.
The book is pretty large spanning 350 pages and the exercises are all on point. However it was first published in 2010 and there hasn’t been an update since, so if you’re looking for something brand new you should look elsewhere.
Here’s a much more recent title also published by the folks at Manning. Hadoop in Practice comes with 500 jam-packed pages sharing well over a hundred different techniques, tutorials, and best practices for Hadoop and big data analysis.
You’ll learn all about Hadoop and the many tools you can use including YARN, Spark, Impala, and of course MapReduce.
The updated second edition expands many of the previous tutorials to include better explanations and optimized source code. This updated version also has newer expansions to Hadoop tools like Sqoop and Storm.
This is a very helpful guide but it’s not for complete beginners. You should have some existing knowledge of MapReduce and preferably some Hadoop experience.
If you need real practical studies to improve your skills then Hadoop in Practice could be an excellent resource.
Complete beginners often want a soft and easy guide into Hadoop. Since Hadoop is such a technical framework this can be tough to find.
But Hadoop in 24 Hours is an incredible book to start with. It’s published by the Sams Teach Yourself series which is known for quality guides on web development & programming.
This book covers everything about Hadoop from an enterprise environment to a local server setup. Each exercise covers a specific feature and they all progress naturally in difficulty.
You’ll learn all about the Hadoop environment and the HDFS. You’ll also learn about Java’s MapReduce through practical exercises that force you to study proper syntax.
I’m also happy to see many exercises that utilize related open source tools like Pig, Hive, and YARN.
Totaling 500 pages and dozens of quality exercises I would absolutely recommend this book for newcomers diving into Hadoop for the first time.
Hadoop: The Definitive Guide is currently in its 4th edition focusing on the latest release of Hadoop. This is by far the most popular guide because it covers everything in a clear writing style and it’s been around for so long.
The book pushes just beyond 750 pages which is a big undertaking. The author Tom White has been an official committer to the Hadoop repo since early 2007 so he has over a decade’s worth of experience working with the platform.
With this guide you’ll learn absolutely everything about Hadoop. This includes the fundamentals like MapReduce and Sqoop/Flume for data transfers. You’ll learn all about MapReduce starting from the very basics and eventually teaching you how to create your own application.
If you’re a complete beginner and you need a starting point then Hadoop: The Definitive Guide is the perfect choice. It does have a fairly complex level of detail so it helps if you’re already familiar with big data and/or programming in another language.
But if you’re up for a challenge then this book can(and will) teach you everything you need to know.
Data Analytics with Hadoop is geared towards data scientists and sysadmins who need to organize big data in a Hadoop environment.
You’ll learn all about data management and data mining using custom database tools like HBase. The book also gets into detail with Spark and MLlib, both of which can make a huge impact on the speed of your data analysis.
This book can feel pretty broad since it covers so many tools and follows so many different workflows. But it’s also a great example of how data analysis can be run strictly through a Hadoop environment.
You do not need any prior experience in Hadoop but it does help. What you really need is experience working with big data and a level of comfort analyzing big datasets.
If you work in a production environment and need to understand the complexities of Hadoop then Hadoop Operations is a must-have resource.
This book covers the minor nuances you’ll face working with Hadoop in a real-world scenario. It’s about 300 pages long and covers plenty of ground on launching, configuring, and maintaining a detailed Hadoop system.
By working through the examples in this guide you’ll learn what works, what doesn’t, and how to differentiate the best practices for your setup.
You’ll learn how to properly manage resources across data clusters and multiple databases. You’ll also learn about monitoring and solving bottleneck issues. The final chapters get into security and maintaining a safe data backup procedure.
Bottom line this book is incredible if you’re ready for it. Novices and hobbyists may learn a bit from this book, but it’s really suited for practical exercises in a real-world setting.
Hadoop comes with a handful of pre-installed APIs and resources that every developer needs to learn. Professional Hadoop Solutions offers a guide to the most common features and how they should be used in practice.
All code in the book comes in Java and XML for extensions/add-ons. You’ll learn about all the different APIs for data processing and automation.
The book is pretty lengthy at 500 pages so it’s not a simple read. Each subject is covered in great detail so this is not your typical beginner’s book.
I’d recommend this to a data scientist or enterprise/sysadmin architect who needs to learn all the possibilities with Hadoop. This can also work well as a reference guide if you need to look up a specific API call or find solutions to certain problems.
When Hadoop 2.x was first released it was followed by a wave of related programming books. Hadoop 2 Quick-Start Guide is one of the better choices covering all the newest features and explaining how the Hadoop environment works.
This is meant to be a true quick start guide. You do not need any real experience with Hadoop to get started, although it certainly helps as you work through these exercises.
The early chapters cover all the basics of MapReduce and Hadoop’s many tools. But you’ll quickly move onto exercises that teach many of Hadoop 2.x’s newest features.
I’d recommend this book more for people who already know Hadoop 1.x and want a guide to the 2.x environment. This is also OK for beginners, but I personally think Hadoop: The Definitive Guide is better for a complete novice.
I should start by saying the Dummies series is rarely great with technical topics. They tend to skim a lot and really don’t dig into the heart of the subject.
Many of these problems ring true for Hadoop For Dummies. However it does have a big upside with easy readability.
Anyone can pick up this book and walk away with a respectable understanding of Hadoop and its many tools. This framework can seem very complex and the Hadoop environment isn’t easy to understand at first. If you’re having trouble grasping the core purpose of Hadoop then this guide can help.
However I do not recommend this guide as a technical overview. The exercises are too basic and they simply don’t cover enough ground to compete with other books in this list.
It’s still a nice resource for a non-technical person who’s getting frustrated with the complexity of Hadoop.
Big datasets are great but they’re not useful unless you can visualize your findings. That’s where Learning Hunk comes into play.
The Hunk platform is built into Splunk Analytics which is a powerful data visualization tool. You should have some prior experience with Splunk before picking up this title, and you can find some resources for learning Splunk in our related guide.
Hunk is not inherently a confusing tool. It does however offer many different workflows and techniques for exploring your data and pulling out data based on filters.
If you find any interesting patterns or need to showcase this data in a presentation you can also use Hunk to create diagrams and charts from scratch.
This is one of the best tools for project managers and Learning Hunk is basically the definitive guide on the subject. It’s a short book with only 150 pages but it’ll cover everything you need to know.
The YARN cluster management tool came with the release of Hadoop 2. It’s a fairly new tool and it’s also one of the more powerful tools for managing real-time data applications.
With YARN Essentials you get a full walkthrough of all the features with live examples. This book totals 176 pages full of excellent advice for new users. YARN administration can be a tricky subject but this book handles it well.
Even experienced Hadoop admins can learn a lot from this book. If you’ve never touched YARN or simply don’t know where to go with it you can find some direction and learn along the way.
Just keep in mind this is not a Hadoop-specific book. You do need some comfort installing & managing Hadoop before you move onto YARN.
By now any beginner must be confused about all the different features and tools built into Hadoop. With Pro Apache Hadoop you’ll find clarity with detailed breakdowns of all the major tools and technologies in a Hadoop environment.
This may sound like a guide for advanced users, but it’s actually a beginner’s guide made to bring you to a professional level.
You’ll learn all about MapReduce, why it’s needed, what it does, and why it’s so damn important. The authors also cover the newer HDFS and many of the new features in Hadoop 2.
I didn’t find the exercises all that enticing but I did find the writing quality to be superb. This is an excellent book for confused beginners who need a bird’s eye view of the Hadoop environment.
Every systems administrator should be concerned about security. It’s not always the DevOps and NetSec teams that should be experts in secure systems.
That’s why I highly recommend Hadoop Security to any serious Hadoop user.
This book covers all the best practices and shares some great tips for securing your big data setups. You’ll learn about common pitfalls and how to avoid them when launching a new project.
Since there are so many different tools and moving parts in Hadoop it can be difficult to keep them all secure. This is especially true if you’re building custom extensions for tools like Hive or Pig.
But Hadoop Security can help you learn both practical security measures and workflows to always keep your eyes peeled for security flaws.
The Apache Sqoop tool is a transfer mechanism for moving big data over to Hadoop from relational databases. This can be much more technical than a NoSQL DB so it’s a handy tool.
But if you don’t know much about Sqoop then you won’t know how to use it. The Apache Sqoop Cookbook comes with dozens of code snippets and recipes for solving common Sqoop problems.
You’ll learn through example using many different database engines like MySQL, Oracle, and PostgreSQL. I was hoping for an MSSQL example but it’s not hard to extrapolate the same principles.
Recipes range from very basic to quite advanced including solutions for multi-database transfers, migrations over to HBase, and automated data backups.
This is not a book for a beginner so you really need to understand Hadoop and a basic workflow. But when you’re ready to master Sqoop this book can be your one and only resource.
The biggest aspect of sorting data through Hadoop is the query. Impala is a processing system that can improve and optimize your queries for parallel/clustered environments.
You can learn everything you need to know about Impala with the O’Reilly book Getting Started with Impala.
It’s only 152 pages but it’s also one of the more detailed guides out there. Impala is not a super complex system to setup. However it is complex to master and fully integrate into your Hadoop workflow.
In this book you’ll learn how to optimize your queries and how to scale them for tremendous data sets. Each chapter has a series of exercises focusing on real-world scenarios like multi-billion row data tables.
There’s also a lot more to the Impala workflow with table joins and the merging of relational data into non-relational DB engines.
You should already have some level of comfort working with Hadoop before starting this book. Thankfully you don’t need to know much about big data queries but you should know enough about relational databases to write SQL commands.
The MapReduce framework is surprisingly powerful and it’s used in many fields like statistics and big data. Learning MapReduce isn’t too hard. But creating an optimized application from scratch certainly isn’t easy.
In MapReduce Design Patterns you’ll learn how to structure example projects that’ll help you better understand MapReduce syntax. The goal is to design projects that can scale with big datasets in the future.
Your design patterns will change based on the project and the end goal. Thankfully this book covers many common scenarios including beginner pitfalls to avoid.
MapReduce is one of the must-learn tools for Hadoop administration. This book will not only help you understand MapReduce in action, but also help you write quality code that scales and makes sense in bigger applications.
There is no final end game with Hadoop. You’ll constantly be learning new tricks and techniques to create more performant applications that scale easier and reduce server loads.
But the most important phase is the initial planning and architecting of the system. Hadoop in the Enterprise: Architecture looks at Hadoop from the very beginning to help you architect powerful systems without going back to rearrange anything.
This book is only 315 pages but it’s possibly the most detailed and complex book out there for program architects. Hadoop thrives in enterprise environments so this title naturally offers solutions and examples based on real-world scenarios.
You must have plenty of experience working with Hadoop and some larger data sets. If you’ve architected at least one Hadoop environment from scratch then you will learn a lot from this guide.
Each chapter is full of practical examples to various problems that you can extrapolate into your own work.
There is no natural process to become a Hadoop admin. You slowly learn different features and techniques which over time culminate into a mass of knowledge.
But if you want a quicker route check out Expert Hadoop Administration. The book totals 848 pages so it’s the largest guide on this subject that you can find.
If you have any interest to work in big data then this book is an incredible read. The author Sam Alapati has years of experience working as a Hadoop administrator so his writing is incredibly accurate.
Sam covers a variety of advanced topics like building custom clusters, performance, scalability, and security measures within your applications. He also covers data encryption and monitoring/logging with a variety of tools.
You should already have some experience with Hadoop and preferably an intermediate-level comfort with big data.These exercises will help you build a stable workflow that can hold up in any enterprise environment.
From user encryption to data migration and machine learning, this book really has it all. The Hadoop Real-World Solutions Cookbook offers dozens of recipes over 290 pages.
Each recipe has a clear goal and it’s formatted in a problem/solution structure. You’ll learn lots of great tips for the MapReduce framework and how to import/export various datasets on the HDFS.
In fact most of this book offers solutions that are built on different Hadoop tools. You don’t need to know how to use many of them to get by.
Although I will admit this cookbook works best for a generalist who wants to learn a bit about everything. I’d only recommend this as a desk reference for beginner-to-intermediate Hadoop users who want to learn a broad range of topics.
Currently in its 2nd edition the Hadoop MapReduce v2 Cookbook is the best MapReduce cookbook to help you solve any problem. It comes with over 90 different recipes for big data using Hadoop, HBase, YARN, Pig, and many other related tools.
The recipes are incredibly practical and they can apply to almost any situation. I’m a big fan of cookbooks since they teach through practical examples using the problem/solution paradigm.
You should already have solid experience with Hadoop before picking up this guide. Most recipes are written for a knowledgeable programmer who just wants to solve MapReduce problems.
I think this book works best as a desk reference for MapReduce functions. But you can learn a lot by working through these recipes one by one.
Anyone diving into Hadoop should expect a long arduous road ahead. There’s so much to learn and you never really learn everything.
But everyone has to start somewhere and the best place to start is Hadoop: The Definitive Guide. It’s super lengthy and offers crystal-clear instructions for using the Hadoop framework.
If you’re an intermediate-to-advanced user looking to up your Hadoop game then I’d recommend copies of Hadoop Security and Expert Hadoop Administration.
With so many different tools and frameworks there are many different paths to walk.
Start with the basics first and then slowly branch out to related tools. This is the best way to learn any big data platform and if you practice with practical exercises you’ll have a much easier time retaining the information.