Summary

In this chapter, we learnt how Solr can be used to churn out data for analytics purposes. We also understood big data and learnt how to use different faceting concepts, such as radius faceting and pivot faceting, for data analytics purposes. We saw some codes that can be used for generating graphs and discussed the different libraries available for this. We discussed that, with SolrCloud, we can build our own data warehouse and get graphs of not only historical data but also real-time data.

In the next chapter, we will learn about the problems that we normally face during the implementation of Solr on an e-commerce platform. We will also discuss how to debug such problems along with tweaks to further optimize the instance(s). Additionally, we will learn about semantic search and its implementation in e-commerce scenarios.