What this book covers
Chapter 1, Processing Big Data Using Hadoop and MapReduce, introduces you to Apache Hadoop and its ecosystem, HDFS and MapReduce. You will also learn how to write MapReduce programs, configure Hadoop clusters, configuration files, and administrate your cluster.
Chapter 2, Understanding Apache Solr, introduces you to Apache Solr. It explains how you can configure the Solr instance, how to create indexes and load your data in the Solr repository, and how you can use Solr effectively to search. It also discusses interesting features of Apache Solr.
Chapter 3, Enabling Distributed Search using Apache Solr, takes you through various aspects of enabling Solr for a distributed search, including with the use of SolrCloud. It also explains how Apache Solr and Big Data can come together to perform a scalable search.
Chapter 4, Big Data Search Using Hadoop and Its Ecosystem, explains the NoSQL and concepts of distributed search. It then explains how to use different algorithms for Big Data search, and includes covering shards and indexing. It also talks about integration with Cassandra, Apache Blur, Storm, and search analytics.
Chapter 5, Scaling Search Performance, will guide you in improving the performance of searches with Scaling Big Data. It covers different levels of optimization that you can perform on your Big Data search instance as the data keeps growing. It discusses different performance improvement techniques that can be implemented by users for the purposes of deployment.
Appendix, Use Cases for Big Data Search, discusses some of the most important business cases for high-level enterprise search architecture with Big Data and Solr.
