7. Analytics Using Complex Data Types
This chapter covers how to make the most of your data by analyzing complex and alternative data types. While data is typically thought of as numbers, in the real world, it frequently exists in other formats: text, dates and times, and latitude and longitude. In addition to these specialty data types, other data types provide the context regarding sequential or non-predeterministic attributes. The goal of this chapter is to show how you can use SQL and analytics techniques to produce insights from these other data types.
By the end of this chapter, you will be able to perform descriptive analytics on time series data using
datetime. You will use geospatial data to identify relationships, then extract insights from complex data types (that is, arrays, JSON, and JSONB) and perform text analytics.