Scaling Big Data with Hadoop and Solr

More Information
Learn
  • 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
About

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.

Features
  • 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
Page Count 144
Course Length 4 hours 19 minutes
ISBN 9781783281374
Date Of Publication 25 Aug 2013

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.