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Mastering Hadoop 3
Mastering Hadoop 3

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

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Mastering Hadoop 3

Journey to Hadoop 3

Hadoop has come a long way since its inception. Powered by a community of open source enthusiasts, it has seen three major version releases. The version 1 release saw the light of day six years after the first release of Hadoop. With this release, the Hadoop platform had full capabilities that can run MapReduce-distributed computing on Hadoop Distributed File System (HDFS) distributed storage. It had some of the most major performance improvements ever done, along with full support for security. This release also enjoyed a lot of improvements with respect to HBASE.

The version 2 release made significant leaps compared to version 1 of Hadoop. It introduced YARN, a sophisticated general-purpose resource manager and job scheduling component. HDFS high availability, HDFS federations, and HDFS snapshots were some other prominent features introduced in version 2...

Hadoop origins and Timelines

Hadoop is changing the way people think about data. We need to know what led to the origin of this magical innovation. Who developed Hadoop and why? What problems existed before Hadoop? How has it solved these problems? What challenges were encountered during development? How has Hadoop transformed from version 1 to version 3? Let's walk through the origins of Hadoop and its journey to version 3.

Origins

In 1997, Doug Cutting, a co-founder of Hadoop, started working on project Lucene, which is a full-text search library. It was completely written in Java and is a full-text search engine. It analyzes text and builds an index on it. An index is just a mapping of text to locations, so it...

Overview of Hadoop 3 and its features

The first alpha release of Hadoop version 3.0.0 was on 30 August 2016. It was called version 3.0.0-alpha1. This was the first alpha release in a series of planned alphas and betas that ultimately led to 3.0.0 GA. The intention behind this alpha release was to quickly gather and act on feedback from downstream users.

With any such releases, there are some key drivers that lead to its birth. These key drivers create benefits that will ultimately help in the better functioning of Hadoop-augmented enterprise applications. Before we discuss the features of Hadoop 3, you should understand these driving factors. Some driving factors behind the release of Hadoop 3 are as follows:

  • A lot of bug fixes and performance improvements: Hadoop has a growing open source community of developers regularly adding major/minor changes or improvements...

Hadoop logical view

The Hadoop Logical view can be divided into multiple sections. These sections can be viewed as a logical sequence, with steps starting from Ingress/Egress and ending at Data Storage Medium.

The following diagram shows the Hadoop platform logical view:

We will touch upon these sections as shown in the preceding diagram one by one, to understand them. However, when designing any Hadoop application, you should think in terms of those sections and make technological choices according to the use case problems you are trying to solve. Without wasting time, let's look at these sections one by one:

  • Ingress/egress/processing: Any interaction with the Hadoop platform should be viewed in terms of the following:
    • Ingesting (ingress) data 
    • Reading (Egress) data 
    • Processing already ingested data

These actions can be automated via the use of...

Hadoop distributions

Hadoop is an open-source project under the Apache Software Foundation, and most components in the Hadoop ecosystem are also open-sourced. Many companies have taken important components and bundled them together to form a complete distribution package that is easier to use and manage. A Hadoop distribution offers the following benefits:

  • Installation: The distribution package provides an easy way to install any component or rpm-like package on clusters. It provides an easy interface too.
  • Packaging: It comes with multiple open-source tools that are well configured to work together. Assume that you want to install and configure each component separately on a multi-node cluster and then test whether it's working properly or not. What if we forget some testing scenarios and the cluster behaves unexpectedly? The Hadoop distribution assures us that...

Points to remember

We provided a basic introduction to Hadoop and the following are a few points to remember:

  • Doug Cutting, the founder of Hadoop, started the development of Hadoop at Nutch based on a Google research paper on Google File System and MapReduce.
  • Apache Lucene is a full-text open-source search library initially written by Doug Cutting in Java.
  • Hadoop consists of two important parts, one called the Hadoop Distributed File System and the other called MapReduce.
  • YARN is a resource management framework used to schedule and run applications such as MapReduce and Spark.
  • Hadoop distributions are a complete package of all open source big data tools integrated together to work with each other in an efficient way.

Summary

In this chapter, we covered Hadoop's origins and how Hadoop evolved over time with more performance-optimized features and tools. We also covered a logical view of the Hadoop platform in detail and understood its different layers. Hadoop distribution was also covered, to help you understand which distribution you should choose. We described the new features available in Hadoop version 3 and will discuss these in more detail in upcoming chapters.

In the next chapter, we will cover HDFS and will walk you through the HDFS architecture and its component in detail. We will go much deeper into the internals of HDFS and HDFS high availability features. We will then look into HDFS read-write operations and how the HDFS caching and federation service works.

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Key benefits

  • Get to grips with the newly introduced features and capabilities of Hadoop 3
  • Crunch and process data using MapReduce, YARN, and a host of tools within the Hadoop ecosystem
  • Sharpen your Hadoop skills with real-world case studies and code

Description

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.

Who is this book for?

If you want to become a big data professional by mastering the advanced concepts of Hadoop, this book is for you. You’ll also find this book useful if you’re a Hadoop professional looking to strengthen your knowledge of the Hadoop ecosystem. Fundamental knowledge of the Java programming language and basics of Hadoop is necessary to get started with this book.

What you will learn

  • Gain an in-depth understanding of distributed computing using Hadoop 3
  • Develop enterprise-grade applications using Apache Spark, Flink, and more
  • Build scalable and high-performance Hadoop data pipelines with security, monitoring, and data governance
  • Explore batch data processing patterns and how to model data in Hadoop
  • Master best practices for enterprises using, or planning to use, Hadoop 3 as a data platform
  • Understand security aspects of Hadoop, including authorization and authentication
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Length: 544 pages
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Language : English
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Table of Contents

20 Chapters
Section 1: Introduction to Hadoop 3 Chevron down icon Chevron up icon
Journey to Hadoop 3 Chevron down icon Chevron up icon
Deep Dive into the Hadoop Distributed File System Chevron down icon Chevron up icon
YARN Resource Management in Hadoop Chevron down icon Chevron up icon
Internals of MapReduce Chevron down icon Chevron up icon
Section 2: Hadoop Ecosystem Chevron down icon Chevron up icon
SQL on Hadoop Chevron down icon Chevron up icon
Real-Time Processing Engines Chevron down icon Chevron up icon
Widely Used Hadoop Ecosystem Components Chevron down icon Chevron up icon
Section 3: Hadoop in the Real World Chevron down icon Chevron up icon
Designing Applications in Hadoop Chevron down icon Chevron up icon
Real-Time Stream Processing in Hadoop Chevron down icon Chevron up icon
Machine Learning in Hadoop Chevron down icon Chevron up icon
Hadoop in the Cloud Chevron down icon Chevron up icon
Hadoop Cluster Profiling Chevron down icon Chevron up icon
Section 4: Securing Hadoop Chevron down icon Chevron up icon
Who Can Do What in Hadoop Chevron down icon Chevron up icon
Network and Data Security Chevron down icon Chevron up icon
Monitoring Hadoop Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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SuchRave Mar 14, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Its a wonderful book, giving details about overall ecosystem very well. Kudos!
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