6 Data Analytics

In the first five chapters of this book, we’ve used Fusion to create some useful and engaging search tools. Our focus has been on data discovery; finding the most relevant documents in the corpus and surfacing them to a data consumer. This is definitely the most common use of search and the one that most people think of when they think of search. However, search engines also offer some features for data analysis and delivery that are very compelling for search engineers and data analysts alike. In this chapter, we’ll take a look at some of the ways search features, specifically facet engines, can be used for data analysis. We'll also look at specific analytics features Fusion brings to bear and how they can all be used to draw insights and decision points from large volumes of data.

6.1           Facets for data analysis

6.1.1                   Search-based analytics vs. document search

6.1.2                   Employing facets for visualizations

6.2           Timeseries partitioning

6.2.1                   Create a music streaming analytics app

6.2.2                   Analytics dashboard

6.3           Geospatial data

6.3.1                   Types of geospatial data

6.3.2                   Indexing geospatial data

6.3.3                   Creating a geospatial data delivery app

6.4           Fusion SQL

6.4.1                   Starting Fusion SQL and Jupyter

6.4.2                   Using Fusion's Jupyter service

6.4.3                   Indexing relational data in Fusion SQL

6.4.4                   Virtual tables

6.4.5                   Access Fusion SQL using JDBC

6.5           Summary