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Apache Hadoop 3 Quick Start Guide

By : Hrishikesh Vijay Karambelkar

Buy this Book

Apache Hadoop 3 Quick Start Guide

By: Hrishikesh Vijay Karambelkar

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Hadoop 3.0 - Background and Introduction

How it all started

What Hadoop is and why it is important

How Apache Hadoop works

Hadoop 3.0 releases and new features

Choosing the right Hadoop distribution

Summary

Planning and Setting Up Hadoop Clusters

Technical requirements

Prerequisites for Hadoop setup

Running Hadoop in standalone mode

Setting up a pseudo Hadoop cluster

Planning and sizing clusters

Setting up Hadoop in cluster mode

Diagnosing the Hadoop cluster

Summary

Deep Dive into the Hadoop Distributed File System

Technical requirements

How HDFS works

Key features of HDFS

Data flow patterns of HDFS

HDFS configuration files

Hadoop filesystem CLIs

Working with data structures in HDFS

Summary

Developing MapReduce Applications

Technical requirements

How MapReduce works

Configuring a MapReduce environment

Understanding Hadoop APIs and packages

Setting up a MapReduce project

Deep diving into MapReduce APIs

Compiling and running MapReduce jobs

Streaming in MapReduce programming

Summary

Building Rich YARN Applications

Technical requirements

Understanding YARN architecture

Key features of YARN

Configuring the YARN environment in a cluster

Working with YARN distributed CLI

Deep dive with YARN application framework

Building and monitoring a YARN application on a cluster

Summary

Monitoring and Administration of a Hadoop Cluster

Roles and responsibilities of Hadoop administrators

Planning your distributed cluster

Resource management in Hadoop

High availability of Hadoop

Securing Hadoop clusters

Performing routine tasks

Summary

Demystifying Hadoop Ecosystem Components

Technical requirements

Understanding Hadoop's Ecosystem

Working with Apache Kafka

Writing Apache Pig scripts

Transferring data with Sqoop

Writing Flume jobs

Understanding Hive

Using HBase for NoSQL storage

Summary

Advanced Topics in Apache Hadoop

Technical requirements

Hadoop use cases in industries

Advanced Hadoop data storage file formats

Real-time streaming with Apache Storm

Data analytics with Apache Spark

Summary

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Data analytics with Apache Spark

Apache Spark offers a blazing fast processing engine based out of Apache Hadoop. It provides in-memory cluster processing of the data, thereby providing analytics at high speeds. Apache Spark evolved in AMPLab (U. C. Berkeley) in 2009 and it was made open source through the Apache Software Foundation. Apache Spark is based out of YARN. Following are key features of Apache Spark:

Fast: Due to in-memory processing capability, Spark is fast in processing
Multiple language support: You can write Spark programs in Java, Scala, R, and Python
Deep analytics: It provides truly distributed analytics, which includes machine learning, streaming data processing, and data querying
Rich API support: It provides a rich API library for interaction in multiple languages
Multi-processing engine support: Apache Spark can be deployed on MapReduce, YARN, and Mesos...