9 Best Hadoop Books of This Year – Start Learning Hadoop and Big Data
In this blog, we will see various best Hadoop books and what they offer us i.e. how we can increase our knowledge about Hadoop. This list of top Hadoop books is for the people who want to build a career in Big Data.
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Best Hadoop Books & Their Reviews
So, here is the list of best Hadoop books for beginners and experienced both. Also, you will see a short description of each Apache Hadoop book that will help you to select the best one.
1. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
by Tom White
It is currently in its fourth edition and has more than 750 pages. It is in some way “Hadoop Bible” where you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers who want to analyze datasets of any size. It is also good for administrators looking for setting up and running Hadoop clusters. It explains how things work and how different systems fit together.
One of the most popular guides which explains everything in a clear writing style. This book will teach you MapReduce from basic to a level where you can write your own applications. It includes fundamentals for Flume/Sqoop used in data transfers. This Hadoop book is the best guide for beginners.
2. Hadoop in Practice
by Alex Holmes
It has 500 jam-packed pages in its second edition. It has 85 examples jam-packed in Q & A format. These use cases will help you learn the ways of building and deploying specific solution suiting the requirements. It shares over a hundred different best practices and techniques for Big Data analysis. You will learn about using and integrating tools like Spark, Impala, MapReduce, and R.
This book addresses specific requirements like querying data using Pig and writing log file loader. With every use case, you will learn how to build a solution for each. As you go along you will find yourself becoming comfortable with Hadoop. The updated second version elaborates previous tutorials. It gives a detailed explanation of the same. It also presents the source code in a more optimized way. This book is not meant for beginners. One should have some basic knowledge about MapReduce and little Hadoop experience.
3. Big Data Analytics with Hadoop 3.0
by Sridhar Alla
It has 482 pages. One of the key features of this Hadoop book is that you can learn effective big data analytics on cloud. It teaches how to use big data tools such as R, Python, Spark, Flink etc and integrate it with Hadoop. It helps you explore real-world examples using Hadoop 3.
In this book of Hadoop, you will get to know new features of Hadoop 3.0 along with MapReduce, YARN, and HDFS. It will teach you how to perform Big Data Analytics in real-time using Apache Spark and Flink. You will learn to set up a Hadoop cluster on AWS Cloud. You will see how to perform analytics on AWS. This book is for those who want to perform data analytics. It will guide you to harness the powerful features of Hadoop 3.0. This book will be helpful for those who have basic conceptual knowledge of Java.
4. Hadoop Operations
by Eric Sammer
It is a 300-page book in its first edition. In this book, you will learn to set up and maintain a hefty and complex Hadoop cluster. This Apache Hadoop book will make you discover how to approach a task and perform it efficiently. There are chapters covering monitoring, maintenance, backups, troubleshooting etc.
This book covers what kind of difficulties one will face in the real world while working with Hadoop. It tells you what best practices you should adopt while solving bottleneck issues. It gives an overview of HDFS and MapReduce answering the question like why there exist and how they work. How to plan a Hadoop deployment from hardware to network settings. With all these details the book is for administrators.
5. MapReduce Design Patterns
by Donald Miner and Adam Shook
This book is of 272 pages in its first edition. It is a guide which tends to bring together important MapReduce patterns. These patterns will take less time and effort despite the industry, language or development framework you are using. The updated version of this book encapsulates a new version of Hadoop. It also contains newly available patterns such as transformations, join with secondary sort, external join etc. Apart from these it discusses MapReduce over HBase.
The goal of this Hadoop book is to fabricate projects which can scale with time and growing data. This book is for people having basic knowledge of Hadoop. This book enables you to master MapReduce algorithms. It contains ways to solve numerous Hadoop problems quickly.
6. Professional Hadoop Solutions
by Boris Lublinsky, Kevin T Smith, Alexey Yakubovich
It had 504 pages in its first edition. This book teaches us about the Hadoop framework and APIs integrated with it to solve problems encountered in production. We can learn MapReduce architecture, its components, and the MapReduce programming model. It teaches you Oozie and how to utilize it to integrate Hadoop implementations with other products.
This book of Hadoop is for those who want to learn how to make most of the extremely scalable analytics. It highlights the approaches to build massive hadoop-based applications. You will take a deep dive into making advanced enterprise solutions. It shows you how to design data which affects Hadoop implementations. This book will give you detailed coding examples in Java taken from applications successfully built and deployed.
7. Hadoop MapReduce v2 Cookbook
by Thilina Gunarathne
It has 293 pages in its second edition. This book has 90 different recipes for Big Data using Hadoop, HBase, YARN, Pig and many other tools. It contains practical examples of having a problem/solution approach. This book is for those already having experience in Hadoop.
You will learn how to install, configure and administer MapReduce program. It also familiarizes you with what’s new in MapReduce version 2. This book tells you how to solve MapReduce problems in the real world. It contains recipes which are very practical.
8. Hadoop for Dummies
by Dirk Deroos
It has 408 pages in the first edition. This Apache Hadoop book is for beginners (as the name suggests). It gives a decent understanding of Hadoop. This makes the value of Big Data & Hadoop comprehensible. It explains the origin of Hadoop, its functionality, benefits, and makes you comfortable dealing with its practical application. Also, it familiarizes you with Hadoop cluster, MapReduce, ecosystem and many operations with Hadoop.
This book walks you through Hadoop’s cost-effectiveness, functionality, and practical applications. It shows you how to program MapReduce, utilize design patterns and get your Hadoop cluster up and running in a quick and easy way. It shows the details of how to use Hadoop applications for data mining, web analytics, large-scale text processing, data science, and problem-solving
9. Hadoop in 24 Hours
by Sams Teach Yourself series
It has 488 pages in its first edition. This book explains everything from the enterprise environment to local server setup. This Hadoop book covers HDFS and various features of Hadoop. There are exercises for practicing MapReduce in Java. It also gives you a feel of Pig, Hive, and YARN
This book shows how to import data to Hadoop, and process it. It enables you to master MapReduce programming in Java. It also teaches you advanced MapReduce API concepts. You will learn to make the most of Apache Pig and Apache Hive. It shows you how to implement and administer YARN. It walks you through different Hadoop ecosystem components like Apache Ambari. We will learn to deal with Hadoop User Environment (HUE) by scaling, securing and troubleshooting it.
So, this was all about Hadoop Books. Hope you liked our explanation.
As such there are many Hadoop books in the market giving knowledge from beginners to intermediate to expert level. It is the reader who has to decide what level of learning he has to achieve. The reader will choose what aspect of Hadoop he wants to learn. It can be administration, programming or machine learning and so on.
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