Scaling Big Data with Hadoop and Solr

This book is aimed at developers, designers, and architects who would like to build big data enterprise search solutions for their customers or organizations. No prior knowledge of Apache Hadoop and Apache Solr/Lucene technologies is required.

Understand, design, build, and optimize your big data search engine with Hadoop and Apache Solr

About This Book

Explore different approaches to making Solr work on big data ecosystems besides Apache Hadoop
Improve search performance while working with big data
A practical guide that covers interesting, real-life use cases for big data search along with sample code

What You Will Learn

Understand Apache Hadoop, its ecosystem, and Apache Solr
Explore industry-based architectures by designing a big data enterprise search with their applicability and benefits
Integrate Apache Solr with big data technologies such as Cassandra to enable better scalability and high availability for big data
Optimize the performance of your big data search platform with scaling data
Write MapReduce tasks to index your data
Configure your Hadoop instance to handle real-world big data problems
Work with Hadoop and Solr using real-world examples to benefit from their practical usage
Use Apache Solr as a NoSQL database

In Detail

Together, Apache Hadoop and Apache Solr help organizations resolve the problem of information extraction from big data by providing excellent distributed faceted search capabilities.

This book will help you learn everything you need to know to build a distributed enterprise search platform as well as optimize this search to a greater extent, resulting in the maximum utilization of available resources. Starting with the basics of Apache Hadoop and Solr, the book covers advanced topics of optimizing search with some interesting real-world use cases and sample Java code.

This is a step-by-step guide that will teach you how to build a high performance enterprise search while scaling data with Hadoop and Solr in an effortless manner.