With the accelerated generation and growth of data, the Hadoop market is poised and projected to grow over 28.9% by 2024. Hadoop lies at the core of the revolution ushered in by Big data. In this article, we will discuss the top 10 books to learn Hadoop with key information. Learning Hadoop has become almost essential given the nature and rate at which data is becoming central to businesses.
If you are looking for the best resources to learn Hadoop and the best books for Hadoop, here is your ideal solution.
We will go at length about the top 10 books to learn Hadoop and later talk about Hadoop in brief to give context. We will also dilate on the importance of Hadoop in the later part of the article. Let us now begin with the best books for Hadoop.
Hadoop: The Definitive Guide
Hadoop Beginner’s Guide
Hadoop Real-World Solutions Cookbook
Hadoop In Action
Hadoop In 24 Hours
Data Analytics With Hadoop
Hadoop For Dummies
Pro Apache Hadoop By Sameer Wadkar, Madhu Siddalingaiah, Jason Venner
Hadoop MapReduce v2 Cookbook By Thilina Gunarathne
Programming Hive: Data Warehouse and Query Language for Hadoop 1st Edition By Edward Capriolo, Dean Wampler
Let us get into the key information that each of the best books for Hadoop offers to the readers and learners.
This book teaches the reader how to create and maintain dependable, accessible, and distributed configurations while facilitating data management. It aids in the examination of datasets of all sizes and offers a variety of Hadoop-related tasks, including Parquet, Crunch, Spark, etc. Additionally, you will learn about the recent modifications to Hadoop and find case examples from diverse industries.
The tools and methods you learn in this book will enable you to approach big data with enthusiasm, create a complete infrastructure to meet your needs as your data expands, and comprehend the necessity of using Hadoop efficiently to address real-world issues. Additionally, you'll learn how to create apps, maintain the system, use extra tools to connect to other systems, etc.
In-depth explanations and code samples are provided in the Hadoop Real-World Solutions Cookbook. There are a number of recipes in each chapter that can be completed in any order that poses and then resolve technical problems. A recipe divides a complex problem into manageable, clear steps. Readers of this book will learn how to use the following tools and amongst many and also construct solutions:
The book starts off by applying the default Hadoop installation to a few simple tasks, including assessing changes in word frequency over a body of papers, to make the fundamental concepts of Hadoop and MapReduce easier to understand. The book keeps going through the fundamental ideas of MapReduce applications created with Hadoop, including a deep examination of framework elements, the application of Hadoop for a variety of data processing jobs, and a lot of Hadoop in-use examples.
Jeffrey Aven has extensively written this book by covering key concepts including the Hadoop platform, its interfaces, its essential ecosystem components, and related Big Data technology. It also demonstrates how to develop Hadoop solutions step-by-step and offers all samples for download. The book's contents are divided into time slots; it begins with a fundamental introduction to Hadoop and concludes with practical workshops.
Using design patterns and parallel analytical algorithms, you will learn how to create distributed data analysis jobs. The author has broadly discussed data management, data mining, and warehousing extensively. Readers will learn all these fundamental concepts coupled with the context of using HBase and Apache Hive. Finally, you will learn how to use Sqoop and Apache Flume to ingest data from relational databases and how to perform machine learning techniques like classification, clustering, and collaboration.
This book is your threshold to navigating through the Hadoop applications and usage. Readers will get familiarized with web analytics, data mining, personalization, large-scale text processing, data science, and problem-solving. It also teaches you how to make a business case for using Hadoop, navigate the Hadoop ecosystem, build and manage Hadoop applications and clusters, and improve the value of your Hadoop cluster by maximizing your investment in Hadoop while avoiding common pitfalls when building your Hadoop cluster, among other things.
This book contains all the information you need to set up your first Hadoop cluster, analyze your commercial and scientific data, and start reaping the benefits. You can learn to solve big-data issues the MapReduce technique, which involves splitting a major problem into smaller, more manageable ones that can be distributed across thousands upon thousands of nodes to quickly analyze large data volumes.
This book will teach you how to set up and manage Hadoop YARN, MapReduce v2, and HDFS clusters, as well as how to use Hive, HBase, Mahout, Nutch, and Pig with Hadoop v2 for solving big data problems quickly and effectively. It will also teach you how to use MapReduce-based applications to solve complex analytics problems, perform massive text data processing using Hadoop MapReduce, and deploy your clusters to cloud environments, among other things.
You will learn how to customize data formats and storage options, from files to external databases, learn Hive patterns you should use and anti-patterns you should avoid, integrate Hive with other data processing programs, load and extract data from tables—and use queries, grouping, filtering, joining, and other conventional query methods, etc. from this book, which essentially uses Hive to create, alter, and drop databases, tables, views, functions, and indexes.
Apache Hadoop is a framework that is completely open source and invented a new method for the distributed processing of enormous enterprise data sets. Hadoop provides distributed parallel processing of enormous volumes of data across cheap, industry-standard servers that both store and analyze the data, as opposed to relying on expensive, and distinct solutions. No data is too huge to use Hadoop.
A single master and several worker nodes make up a tiny Hadoop cluster. Although data-only and compute-only worker nodes are both feasible, a slave or worker node serves as both a DataNode and a TaskTracker. A secondary NameNode that can create snapshots of the NameNode's memory structures manages the Hadoop Distributed File System (HDFS) in a bigger cluster, limiting file-system corruption and minimizing data loss. The primary NameNode hosts the file system index.
Ability to quickly store and process large amounts of data of any form: That's a crucial factor to take into account given the continually growing data volumes and variety, particularly from social media and the Internet of Things (IoT).
Computing ability: Big data is processed quickly using Hadoop's distributed computing paradigm. You have more processing power the more computing nodes you employ.
Fault Tolerance: Processing of data and applications is safeguarded against hardware malfunction. To ensure that distributed computing does not fail, jobs are immediately routed to other nodes if one node goes down. All data is automatically kept in several copies.
Flexibility: Contrary to conventional relational databases, no preprocessing is necessary before saving the data. The amount of data you save is up to you, and you may decide how to use it later. Data that is not organized, such as text, photos, and videos, are included.
Low Cost: Large amounts of data can be stored using the open-source framework, which is free and runs on inexpensive hardware.
Scalability: It is characterized by easy system expansion by simply adding nodes that offer the scope of handling more data. It only requires little management.
Reading the top 10 books to learn Hadoop will broaden your knowledge and skills in Hadoop. To give you a grasp of the skills fundamental to Hadoop, we give a brief analysis of the professional Hadoop skills.
There are many different job categories, including data scientist, architect, administrator, and tester for Hadoop. Hadoop experts possess an analytical attitude that allows for learning, unlearning, and relearning. The ability to work with enormous amounts of data to derive business intelligence, the use of Hadoop for big data analytics, the proposal of data-driven strategies, knowledge of OOP languages like Java, C++, and Python, understanding of database theories, structures, categories, and best practices, and expertise in Hadoop installation, configuration, maintenance, and security are just a few of the skills of Hadoop professionals.
Professionals who work with Hadoop are familiar with tools like Apache Flume, Oozie, Phoenix, HBase, Hive, Pig, and so forth. Customer analytics, risk management, and operational intelligence are a few of the application cases for Hadoop. As a result, Hadoop experts, particularly those who possess Hadoop certifications, are in high demand all over the world.
That was all about the top 10 books to learn about Hadoop and a brief analysis of Hadoop applications and their importance.
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