Cloud Native Geospatial Analytics with Apache Sedona

Chapter 1. Introduction to Apache Sedona

The open source Apache Sedona project grew out of the need for a scalable geospatial analytics framework capable of working with large-scale spatial data. There’s a common saying in the data world that “spatial is special.” In other words, working with spatial data implies that due to the unique characteristics and complexities of spatial data, specialized techniques, tooling, and knowledge are required for effective analysis and interpretation of spatial data. While there is some validity to this perspective, it misses the more nuanced truth that many traditional best practices, techniques, tooling, and data formats from the data engineering and data science world are still perfectly relevant when working with geospatial data. However, there are some unique challenges and considerations that arise when working with spatial data.

In this chapter, we will discuss some of the challenges that commonly arise when working with geospatial data and provide an overview of the geospatial data ecosystem, including some of the gaps in tooling that led to the need for a scalable geospatial analytics framework like Apache Sedona.

We will also introduce how Apache Sedona addresses the challenges of working with geospatial data at scale and take a look behind the scenes at the basic architecture and components of Apache Sedona. At the end of this chapter, we should have a clearer understanding of the idea that “spatial is special” and evaluate if there ...