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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

Who this book is for

Scaling Big Data with Hadoop and Solr, Second Edition provides step-by-step guidance for any user who intends to build high-performance, scalable, enterprise-ready search application servers. This book will appeal to developers, architects, and designers who wish to understand Apache Solr/Hadoop and its ecosystem, design an enterprise-ready application, and optimize it based on their requirements. This book enables you to build a scalable search without prior knowledge of Solr or Hadoop, with practical examples and case studies.

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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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