Mastering Location Data: Geospatial Magic Meets Databricks Power

Ever used Google Maps to find your way around? That’s geospatial data in action! It’s information tied to a place on Earth-like where your favorite ice-cream shop is, where roads go, where cities are expanding, and how places change over time, just to name a few.

GIS, or Geographic Information Systems, takes this data and turns it into smart maps and tools that help people make better decisions. From choosing the safest route for a delivery truck to planning where to build a new hospital or identifying areas at risk of floods or urban heat islands, GIS helps us understand where things happen and how we can use that insight to make better decisions.

Geospatial experts often use tools like FME or ArcGIS to look at maps and analyze location data. They usually keep their data in databases like Postgres or Oracle Spatial, and write code in SQL or Python using libraries like PostGIS, GeoPandas, GDAL, or PDAL to get the job done.

But today, we’re dealing with way more data than before. That’s where platforms like Databricks come in. It’s a modern tool that can handle huge amounts of data, run complex workflows faster, and work alongside the tools geospatial folks already use. Think of it as a powerful new teammate for your geospatial projects.

Where should you begin your journey into geospatial data on Databricks? The good news is that RevoData is offering a specialized training session focused entirely on using Databricks for geospatial workflows. This session will guide you through the essentials of working with Databricks. We’ll also look at how Databricks works together with other geospatial tools like FME, ArcGIS, and Postgres. Whether you’re just getting started or looking to optimize your current processes, this training will help you understand the core principles and practical applications of geospatial data integration within the Databricks ecosystem.

You’ll explore the benefits of migrating your geospatial workflows to Databricks, leveraging its modern lakehouse architecture that merges scalable storage with lightning-fast analytics. We’ll walk through the key Python and Spark libraries that enable efficient and flexible spatial data processing, helping you unlock Databricks’ full potential. By the end of the session, you’ll have a clear understanding of when and how to make the shift, and the tools you’ll need to get there.

This training is designed to be hands-on and practical, with exercises that guide you through real-world applications. We’ll work with a variety of geospatial data types – including vector data (like topographic maps and point clouds), raster data (such as aerial imagery and netCDF files), and even graph-based data – to solve meaningful geospatial problem.

Here’s a quick sneak peek at the hands-on training cases: