Scaling Big Data with Hadoop and Solr - Second Edition

More Information
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
About

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.

Features
  • 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
Page Count 166
Course Length 4 hours 58 minutes
ISBN 9781783553396
Date Of Publication 26 Apr 2015

Authors

Hrishikesh 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.