Book Image

Apache Hadoop 3 Quick Start Guide

By : Hrishikesh Vijay Karambelkar
Book Image

Apache Hadoop 3 Quick Start Guide

By: Hrishikesh Vijay Karambelkar

Overview of this book

Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)

Demystifying Hadoop Ecosystem Components

We have gone through the Apache Hadoop subsystem in detail in previous chapters. Although Hadoop is extensively known for its core components such as HDFS, MapReduce and YARN, it also offers a whole ecosystem that is supported by various components to ensure all your business needs are addressed end-to-end. One key reason behind this evolution is because Hadoop's core components offer processing and storage in a raw form, which requires an extensive amount of investment when building software from a grass-roots level.

The ecosystem components on top of Hadoop can therefore provide the rapid development of applications, ensuring better fault-tolerance, security, and performance over custom development done on Hadoop.

In this chapter, we cover the following topics:

  • Understanding Hadoop's Ecosystem
  • Working with Apache Kafka
  • Writing...