Scaling spatial analytics has been challenging, but new cloud-native file formats are changing that. Discover how these formats, combined with modern cloud infrastructure, will integrate GIS with AI, enhancing spatial analytics capabilities for future applications.
For me personally, one of the most important developments over the past two years in geospatial has been the development of cloud native file formats. This has enabled several different things which will be outlined in the presentation.
- Faster analytics at a smaller storage footprint.
- The ability to integrate with the modern data stack.
- A full realization for geospatial in the Lakehouse architecture.
- Complete interoperability with existing open-source solutions.
- A framework to scale spatial analytics, machine learning, and geospatial AI.
These new file types have enabled spatial analytics for every possible scale, including locally on your computer, as well as for massive cloud analytics. They form the backbone of a modern cloud geospatial toolkit using open source frameworks. It will also enable the closer collaboration of data teams across organizations using this same infrastructure to work better and more closely with geospatial users and use cases.
This presentation will focus on why this matters, how users and organizations can start to move to this new framework and leverage it within their own organizations, as well as a case study around how this can be used to perform geospatial machine learning. We'll discuss how we have implemented the use of these different formats like CARTO, including GeoParquet and Cloud Optimized GeoTIFF. We'll also go over how we have worked with modern cloud providers and others in the geospatial space to push for the adoption of these file types, the different state of these technologies, and how it will be used in the future.
