Location intelligence has become essential for enterprise success. To compete, you need to know not just what’s happening, but also where and why.
As Databricks extends its platform with native geospatial capabilities through Spatial SQL, you can now integrate high-quality location data – like trusted datasets and Private Spatial APIs from Precisely – directly into your analytics workflows.
The result? A more scalable, flexible way to unlock deep, powerful insights from location data.
The Challenge of Scaling Geospatial Intelligence
Enterprises are increasingly moving spatial workloads to the cloud, but traditional GIS systems often fall short – struggling with scale, flexibility, and integration.
Private Spatial APIs from Precisely solve these challenges through cloud-native spatial processing that integrates with modern data platforms like Databricks. Here’s what that unlocks:
- Ultra-scalable spatial analytics across all major cloud environments, including AWS, Azure, and GCP, and hybrid deployments with OpenShift and Rancher
- Flexible deployment via Kubernetes-based HELM charts for on-prem, bare metal, and hybrid environments
- Easy enrichment with Precisely’s industry-leading datasets for property, risk, demographics, and more
For leaders, this shift means location data can finally operate at the same scale and speed as the rest of the enterprise stack – putting spatial insight in the hands of more teams, not just GIS specialists.
Powering Spatial SQL in Databricks
Spatial SQL adds native geospatial functions that let your teams run spatial analysis directly on the Databricks Data Lakehouse. Paired with Precisely APIs and data products, this enables powerful new capabilities:
1. Cloud-native spatial processing
Private Spatial APIs let enterprises move legacy spatial workloads to the cloud while maintaining performance and control. They support core functions like spatial joins, buffers, overlays, and routing – all within Databricks notebooks or workflows.
Example: Identify high-risk properties within flood zones
2. Enriching data for business intelligence
Thousands of attributes across property, consumer, and geographic dimensions can be found in Precisely datasets. These can be combined with enterprise data in Databricks to answer critical questions:
- Which customers are in high-risk areas?
- What’s the demographic profile of our top-performing regions?
- Where should we expand next?
Example: Enrich customer data with property attributes
3. Operational intelligence at scale
Across industries, organizations are leveraging these capabilities to succeed in their top use cases.
- Insurance: Risk scoring and underwriting using enriched property and hazard data
- Retail: Site selection based on foot traffic, demographics, and competition in the area
- Telecom: Network planning using routing APIs and population density overlays
- Government: Urban planning and emergency response optimization
Exploring Spatial SQL Functions in Databricks
Building on these capabilities, Databricks Spatial SQL offers a rich set of geospatial functions that let teams run spatial analysis directly within SQL queries – without needing external GIS tools or complex ETL pipelines. Here are some of the highlights:
ST_Point(latitude, longitude)
Creates geometry point objects from latitude and longitude coordinates. It’s the foundational building block for spatial analysis, enabling location-based joins, proximity searches, and more.
Example: Convert customer addresses into geospatial points for mapping and analysis.
ST_Contains(geometryA, geometryB)
Checks whether one geometry (typically a polygon) contains another (usually a point). This is essential for identifying whether a location falls within a defined boundary – like a flood zone, sales territory, or service area.
Example: Determine which customers are located within high-risk flood zones.
ST_Intersects(geometryA, geometryB)
Returns true if two geometries share any portion of space. This is useful for identifying overlapping areas – think properties intersecting with hazard zones or delivery routes crossing service boundaries.
Example: Find properties that intersect with wildfire risk zones.
ST_Distance(geometryA, geometryB)
Calculates the distance between two geometries. This is critical for proximity analysis, like finding the nearest store, competitor, or infrastructure asset.
Example: Rank customers by proximity to retail locations.
ST_Buffer(geometry, radius)
Creates a buffer zone around a geometry. This is useful for impact analysis, such as identifying all assets within a certain radius of a new development or incident.
Example: Identify all customers within 5 miles of a new store.
Together, these functions empower your data teams to perform advanced geospatial analytics directly on the Databricks Data Lakehouse. That means there’s no longer a need for specialized spatial analytics software.
When you combine this with Private APIs and data enrichment products from Precisely, you go even further:
- Real-time spatial decision-making
- Simple enrichment of enterprise data with additional attributes
- Operational insights that drive revenue and reduce risk
By making these functions native, Databricks turns geospatial from a specialized workflow into a common analytical skill – letting business analysts ask location-aware questions directly, without needing GIS expertise.
Taking the Next Steps
With all this in mind, the natural next question is: how do we get started? The good news is that it’s easier than you might think. Here’s how your teams can start leveraging powerful location intelligence today:
- Deploy Precisely Private Spatial APIs in your cloud or Databricks workspace
- Use Databricks Spatial SQL to query and analyze spatial data
- Enrich your data with curated attributes from Precisely
- Bring it all to life by visualizing results in dashboards or notebooks
With this foundation in place, your teams can move from experimenting with geospatial data to operationalizing it – driving faster, location-aware decisions across the business.
Looking Ahead: What’s Next in Spatial Intelligence?
The trajectory is clear: geospatial insight is moving from the edges of the business to its center.
Private APIs from Precisely are built to work in today’s big data environments, with support for Spark 3.5, EMR Serverless, and Databricks 14+. What’s next?
- Location dashboards for richer visualization
- Broader support for spatial SQL
- AI-powered agents that surface insights automatically
By combining location intelligence with Databricks’ scale, you’re able to make smarter, faster, and more location-aware decisions – whether that’s to protect revenue through risk mitigation or drive growth through strategic expansion.
It’s all about putting geospatial intelligence to work in a way that’s practical, scalable, and focused on the business outcomes that matter most.
Explore how leading organizations are already applying these insights and see where you can start in our eBook: Top 5 Business Challenges Solved with Location Intelligence.
