Best Hadoop Books 2024

Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale 4th Edition

by Tom White will teach you everything you need to know about Hadoop. Tom White has been an Apache Hadoop committer since February 2007, and is a member of the Apache Software Foundation. This book will help start you of at the fundamental concepts of Hadoop. It then moves on to Hadoop’s new features and projects. This Hadoop book is well over 700 pages containing Hadoop features and uses. This is easy to understand for beginners and advanced programmers who want to work with Big Data. Systems Administrators will also find great value in this book to setup Hadoop clusters. This is the best Hadoop book in 2023.

Learn basic elements like Mapradius, HDFS and Yarn
Explore the map in depth with the app development steps
Configure and maintain a Hadoop cluster running HDFS and Maps on YARN
Discover two data formats: Avro for serializing data and Perquite for nested data
Use data integration tools like Flume (for data streaming) and Scoop (for bulk data transfer)
Understand how high-level data processing tools like Pig, Hive, Crunch and Spark work with Hadoop
Explore the HBase Distribution Database and Zoo Distribution Configuration Services

Hadoop in 24 Hours, Sams Teach Yourself

by Jeffrey Aven will help you learn how to deploy each important component of a Hadoop platform in your local environment or in the cloud, as well as how to build a fully functional Hadoop cluster and use it with real-world programs and datasets. Each short, simple session builds on the previous ones, allowing you to learn Hadoop’s principles and extend it to meet your specific requirements. Sams Teach Yourself’s Apache Hadoop in 24 Hours covers all of this and more:

Hadoop and the Hadoop Distributed File System: An Overview (HDFS)
Data is imported into Hadoop and processed there.
Using advanced MapReduce API principles and mastering basic MapReduce Java programming
Using Apache Pig and Apache Hive to their full potential
YARN implementation and administration
Making use of the entire Hadoop ecosystem
Apache Ambari for Hadoop Cluster Management
Using Hadoop in the User Environment (HUE)
Hadoop environments: scaling, securing, and troubleshooting
Integrating Hadoop into a business
Hadoop deployment in the cloud
Apache Spark: Getting Started

Step-by-step instructions guide you through frequent problems, concerns, and tasks; Q&As, Quizzes, and Exercises help you gain and test your knowledge; “Did You Know?” hints provide insider information and shortcuts; and “Watch Out!” alerts warn you about potential dangers. By the end of the course, you’ll be able to use Apache Hadoop to handle a wide range of Big Data problems.

Hadoop in Action

by Chuck Lam teaches readers how to use Hadoop and write MapReduce programs. The target readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will take the reader from obtaining a copy of Hadoop to setting it up on a cluster and writing data analysis programs. The book begins by making the basic idea of ​​Hadoop and MapReduce easier to understand by applying the default Hadoop installation to some easy-to-follow tasks, such as analyzing word frequency changes in a body of documents. The book continues with the basics of MapReduce applications developed with Hadoop, including an in-depth look at the components of the framework, using Hadoop for a variety of data analysis tasks, and many examples of Hadoop in action.

Hadoop in Action will explain how to use Hadoop and introduce MapReduce design patterns and programming practices. MapReduce is a complex idea both conceptually and in implementation, and Hadoop users are challenged to learn all the buttons and levers to run hadoop framework. This book takes you beyond the Hadoop execution mechanisms, teaching you how to write meaningful programs in a MapReduce framework. This Hadoop book assumes that the reader will have a basic understanding of Java, as most of the code samples will be written in Java. Knowledge of basic statistical concepts (eg, histogram, correlation) will help the reader to appreciate more advanced data processing examples. You will maintain reliable scalable distributed systems. This is the best Hadoop book for beginners in 2023.

Hadoop in Practice: Includes 104 Techniques 2nd Edition

by Alex Holmes provides more than 100 proven and instantly useful techniques to help you conquer big data using Hadoop. This new revised edition covers changes and new features in the base Hadoop architecture, including MapReduce 2. The new chapters cover YARN and the integration of Kafka, Impala, and Spark SQL with Hadoop. You’ll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new releases recently. In short, it’s the most convenient and up-to-date Hadoop coverage available anywhere.

It’s always a good time to improve your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 proven and instantly useful techniques for analyzing flows in real time, moving data safely, automatically learning, managing large-scale clusters, and taming big data with Hadoop. This completely revised edition covers changes and new features in the Hadoop core, including MapReduce 2 and YARN. You will learn about best practices for integrating Spark, Kafka, and Impala with Hadoop, and you will get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, it is the most practical and up-to-date Hadoop coverage available. Readers should know a programming language such as Java and have a basic understanding of Hadoop. This book contains:

Fully updated for Hadoop 2
How to write YARN applications
Integrate real-time technologies like Storm, Impala and Spark
Predictive analysis using Mahout and RR
Readers should know a programming language such as Java and have a basic understanding of Hadoop.

Expert Hadoop Administration: Managing, Tuning, and Securing Spark, YARN, and HDFS (Addison-Wesley Data & Analytics Series)

Hadoop Chief Administrator Sam R. Alapati brings together the knowledge of approval for creating, configuring, securing, managing, and optimizing clusters of Hadoop production in any environment. The interaction combines action-based counseling with carefully studied explanations of problems and solutions to draw on his experience with large-scale Hadoop administration. It covers an unrivaled range of topics and provides an unrivaled collection of practical examples.

You will:
Understand Hadoop architecture from an administrator’s perspective
Create simple, complete distribution clusters
Run Mapredius and Spark applications in a Hadoop cluster
Manage and secure Hadoop data and high availability
Work with HDFS commands, file permissions and storage management
Remove data and use YARN to allocate resources and work schedules
Manage workflow with Oz and Hugh
Protect, monitor, record and customize Hadoop
Hadoop benchmarking and problem solving

Complete and up-to-date Apache Hadoop Administration and Reference Manual.

Mastering Hadoop 3: Big data processing at scale to unlock unique business insights

by Chanchal Singh, Manish Kumar is a complete guide to mastering the most advanced Hadoop 3 concepts. Discover new Hadoop features and capabilities. Hadoop 3 Compress and process data using the tools of a few hosts within the Map Mapredeus, Yarn and Hadoop ecosystems. Improve your Hadoop skills with case studies and real-world code Apache Hadoop is one of the most popular solutions among the big data solutions for distributed storage and large amount of data processing. With Hadoop 3, Apache promises to provide a high-performance, more error-tolerant and highly efficient large data processing platform with a focus on improved scalability and enhanced efficiency. You will:

Gain a deeper understanding of distributed computing using Hadoop 3
Develop enterprise-level applications using Apache Spark, Flink, etc.
Create scalable, high-performance Hadoop data pipelines with data protection, monitoring and administration
Find batch data processing models and how to model data in Hadoop
Maximum practice for companies planning or planning to use Hadoop 3 as a data platform
Understand the security aspects of Hadoop, including approval and authentication

Data Analytics with Hadoop: An Introduction for Data Scientists

by Benjamin Bengfort and Jenny Kim is a practical guide shows you why the Hadoop ecosystem is perfect for the job. Ready to use statistics and machine learning techniques on big data sets? This practical Hadoop guide shows you why the Hadoop ecosystem is suitable for work. Instead of deploying, operating, or developing software that is typically related to distributed computing, focus on the data warehousing strategies and data workflows provided by Hadoop that can create this structure that can produce higher orders.

Understand the basic concepts behind Hadoop and cluster computing
Use parallel design models and analytic algorithms to create distributed data analysis work
Discover data management, search, and storage for distribution using Apache Hive and HBS
Use Scoop and Apache Flume to inject data from relational databases
Program complex Hadoop application and spark application with Apache Pig and Spark data frames
Implement machine learning techniques such as classification, clustering, and collaborative filtering with MLBs from Spark

Hadoop Real-World Solutions Cookbook – Second Edition

by Tanmay Deshpande gives readers an overview of learning and mastering big data through recipes. The book not only clarifies most of the big data tools on the market, but also provides best practices for using them. The book provides recipes based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout, and many other ecosystem tools. This real-world solutions cookbook is packed with practical recipes that you can apply to your own everyday problems. Each chapter provides detailed recipes that can be easily referenced. This book provides detailed practices on the latest technologies, such as YARN and Apache Spark. Readers can consider themselves big data experts at the end of this book. You will:

Installation and maintenance of the Hadoop 2.X cluster and its ecosystem.
Write advanced card reduction programs and understand design patterns.
Advanced data analysis using Hive, Pig and Map Reduce programs.
Import and export data from various sources using Sqoop and Flume.
Data storage in various file formats such as text, sequential, parquet, ORC and RC files.
Principles of machine learning with libraries like Mahout
Batch and Stream Data Processing with Apache Spark