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Mastering Hadoop 3

Mastering Hadoop 3

By : Wong, Singh, Kumar
5 (1)
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Mastering Hadoop 3

Mastering Hadoop 3

5 (1)
By: Wong, Singh, Kumar

Overview of this book

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.
Table of Contents (21 chapters)
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1
Section 1: Introduction to Hadoop 3
6
Section 2: Hadoop Ecosystem
10
Section 3: Hadoop in the Real World
16
Section 4: Securing Hadoop

YARN Resource Management in Hadoop

From the very beginning of Hadoop's existence, it has consisted of two major parts, the storage part, which is known as the Hadoop Distributed File System (HDFS), and the processing part, which is known as MapReduce. In the previous chapter, we discussed the Hadoop Distributed File System, its architecture, and its internals. In Hadoop version 1, the only job that can be submitted and executed to Hadoop is MapReduce. In the present era of data processing, real-time and near real-time processing are favored over batch processing. Thus, there is a need for a generic application executor and Resource Manager that can schedule and execute all types of applications, including MapReduce, in real time or near real time. In this chapter, we will learn about YARN and will cover the following topics:

  • YARN architecture 
  • YARN job...
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