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

by Hrishikesh Vijay Karambelkar

Overview

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.

Product Details

ISBN-13:	9781788994347
Publisher:	Packt Publishing
Publication date:	10/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	220
File size:	5 MB

About the Author

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.

Table of Contents

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