Scaling Big Data with Hadoop and Solr

Learn exciting new ways to build efficient, high performance enterprise search repositories for Big Data using Hadoop and Solr

Overview

• Understand the different approaches of making Solr work on Big Data as well as the benefits and drawbacks
• Learn from interesting, real-life use cases for Big Data search along with sample code
• Work with the Distributed Enterprise Search without prior knowledge of Hadoop and Solr

In Detail

As data grows exponentially day-by-day, extracting information becomes a tedious activity in itself. Technologies like Hadoop are trying to address some of the concerns, while Solr provides high-speed faceted search. Bringing these two technologies together is helping organizations resolve the problem of information extraction from Big Data by providing excellent distributed faceted search capabilities.

Scaling Big Data with Hadoop and Solr is a step-by-step guide that helps you build high performance enterprise search engines while scaling data. Starting with the basics of Apache Hadoop and Solr, this book then dives into advanced topics of optimizing search with some interesting real-world use cases and sample Java code.

Scaling Big Data with Hadoop and Solr starts by teaching you the basics of Big Data technologies including Hadoop and its ecosystem and Apache Solr. It explains the different approaches of scaling Big Data with Hadoop and Solr, with discussion regarding the applicability, benefits, and drawbacks of each approach. It then walks readers through how sharding and indexing can be performed on Big Data followed by the performance optimization of Big Data search. Finally, it covers some real-world use cases for Big Data scaling.

With this book, you will learn everything you need to know to build a distributed enterprise search platform as well as how to optimize this search to a greater extent resulting in maximum utilization of available resources.

What you will learn from this book

• Understand Apache Hadoop, its ecosystem, and Apache Solr
• Learn different industry-based architectures while designing Big Data enterprise search and understand their applicability and benefits
• Write map/reduce tasks for indexing your data
• Fine-tune the performance of your Big Data search while scaling your data
• Increase your awareness of new technologies available today in the market that provide you with Hadoop and Solr
• Use Solr as a NOSQL database
• Configure your Big Data instance to perform in the real world
• Address the key features of a distributed Big Data system such as ensuring high availability and reliability of your instances
• Integrate Hadoop and Solr together in your industry by means of use cases

Approach

This book is a step-by-step tutorial that will enable you to leverage the flexible search functionality of Apache Solr together with the Big Data power of Apache Hadoop.

Who this book is written for

Scaling Big Data with Hadoop and Solr provides guidance to developers who wish to build high-speed enterprise search platforms using Hadoop and Solr. This book is primarily aimed at Java programmers who wish to extend the Hadoop platform to make it run as an enterprise search without any prior knowledge of Apache Hadoop and Solr.

Table of Contents

Chapter 1: Processing Big Data Using Hadoop and MapReduce
Chapter 2: Understanding Solr
Chapter 3: Making Big Data Work for Hadoop and Solr
Chapter 4: Using Big Data to Build Your Large Indexing
Chapter 5: Improving Performance of Search while Scaling with Big Data

Appendix A: Use Cases for Big Data Search
Appendix B: Creating Enterprise Search Using Apache Solr
Appendix C: Sample MapReduce Programs to Build the Solr Indexes

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