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Scaling Big Data with Hadoop and Solr, Second Edition

You're reading from Scaling Big Data with Hadoop and Solr, Second Edition Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr

Product type Paperback

Published in Apr 2015

Publisher

ISBN-13 9781783553396

Length 166 pages

Edition 1st Edition

Tools

Solr

Concepts

Big Data

Author (1):

Hrishikesh Vijay Karambelkar

View More author details

Table of Contents (8) Chapters

Preface

1. Processing Big Data Using Hadoop and MapReduce

2. Understanding Apache Solr FREE CHAPTER

3. Enabling Distributed Search using Apache Solr

4. Big Data Search Using Hadoop and Its Ecosystem

5. Scaling Search Performance

A. Use Cases for Big Data Search

Index

Summary

In this chapter, we discussed the need for Apache Hadoop to address the challenging problems faced by today's world. We looked at Apache Hadoop and its ecosystem, and we focused on how to configure Apache Hadoop, followed by running it. Finally, we created Hadoop clusters by using a simple set of instructions. The next chapter is all about Apache Solr, which has brought a revolution in the search and analytics domain.

The rest of the chapter is locked

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Authors (1)

Vijay Karambelkar

Vijay Karambelkar

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.

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