Apache Hadoop 3 Quick Start Guide

Vijay Karambelkar, Hrishikesh

ISBN 10: 1788999835 / ISBN 13: 9781788999830

Verlag: Packt Publishing, 2018

Beschreibung

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.

Bestandsnummer des Verkäufers 448329808

Bibliografische Details

• Titel: Apache Hadoop 3 Quick Start Guide
• Verlag: Packt Publishing
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 1788999835
• ISBN-13: 9781788999830
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem

Key Features

• 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

Book 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.

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

Who this book is 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.

Table of Contents

1. Hadoop 3.0 - Background and Introduction
2. Planning and Setting Up Hadoop Clusters
3. Deep Dive into the Hadoop Distributed File System
4. Developing MapReduce Applications
5. Building Rich YARN Applications
6. Monitoring and Administration of a Hadoop Cluster
7. Demystifying Hadoop Ecosystem Components
8. Advanced Topics in Apache Hadoop

Über die Autorin bzw. den Autor

Hrishikesh Vijay Karambelkar is an innovator and an enterprise architect with 16 years of software design and development experience, specifically in the areas of big data, enterprise search, data analytics, text mining, and databases. He is passionate about architecting new software implementations for the next generation of software solutions for various industries, including oil and gas, chemicals, manufacturing, utilities, healthcare, and government infrastructure. In the past, he has authored three books for Packt Publishing: two editions of Scaling Big Data with Hadoop and Solr and one of Scaling Apache Solr. He has also worked with graph databases, and some of his work has been published at international conferences such as VLDB and ICDE.