Scaling Search with Big Data & Solr – Course Description

Course Overview

This 4-day, virtual course starts with Hadoop from the ground up, and covers MapReduce, HDFS - the Hadoop Distributed File System, cluster management, "the shuffle," etc., before continuing on to connecting it to Solr. We'll look at common use cases for generating search indexes from big data, typical patterns for the data processing workflow, and how to make it all work reliably at scale. We will explore in-depth an example of processing 1 billion records to create a faceted Solr search solution. Students learn how Solr can be used as a NoSQL solution, and how it compares to classic NoSQL projects such as Cassandra and HBase. The class will continue with techniques for scaling your Solr installation, how to identify bottlenecks in your Solr installation, how to monitor your installation, and how determine resource usage. We'll also cover various Solr architectures, their characteristics and use cases. We'll examine how to apply these to make appropriate tradeoffs to effectively scale your Solr installation.

Who Should Attend?

The class is for Solr developers who want to know how to leverage the flexible search functionality of Apache Solr and the Big Data processing of Apache Hadoop, to create the indexes for both general search and augmented data analytics. Lab exercises and real-world examples will be used to reinforce content.

Prerequisites

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Course Materials

Registration