Scaling Big Data with Hadoop and Solr

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.