Apache Hadoop 3 Quick Start Guide: Learn about big data processing and analytics

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Planning and Setting Up Hadoop Clusters

In the last chapter, we looked at big data problems, the history of Hadoop, along with an overview of big data, Hadoop architecture, and commercial offerings. This chapter will focus on hands-on, practical knowledge of how to set up Hadoop in different configurations. Apache Hadoop can be set up in the following three different configurations:

Developer mode: Developer mode can be used to run programs in a standalone manner. This arrangement does not require any Hadoop process daemons, and jars can run directly. This mode is useful if developers wish to debug their code on MapReduce.
Pseudo cluster (single node Hadoop): A pseudo cluster is a single node cluster that has similar capabilities to that of a standard cluster; it is also used for the development and testing of programs before they are deployed on a production cluster. Pseudo...

Key benefits

Set up, configure and get started with Hadoop to get useful insights from large data sets

Work with the different components of Hadoop such as MapReduce, HDFS and YARN

Learn about the new features introduced in Hadoop 3

Description

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.

Who is this book for?

Aspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.

What you will learn

Store and analyze data at scale using HDFS, MapReduce and YARN

Install and configure Hadoop 3 in different modes

Use Yarn effectively to run different applications on Hadoop based platform

Understand and monitor how Hadoop cluster is managed

Consume streaming data using Storm, and then analyze it using Spark

Explore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and Kafka

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