Today we’re announcing a significant expansion of Felt's enterprise capabilities with the launch of SQL queries. Felt has always been the fastest way to build maps, apps and dashboards for organizations reliant on spatial insights, and now you can write custom queries to your database directly from Felt. Whether you are on the Engineering, GIS or Data Team – you can now efficiently deliver data across your organization with just a few lines of code.

Reduced time-to-insight: execute complex spatial analyses with SQL queries

Felt SQL queries eliminate the need for mutli-step GUI interactions, enabling you to efficiently query just the data you need directly from your cloud database. Run a simple operation, or execute complex spatial analysis across multiple data tables. Felt will complete your custom query, run it through our Upload Anything pipeline and return a performant data layer for the team to reference.

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Felt handles all your operations, analysis, geocoding, and Tippecanoe tiling directly, avoiding expensive queries and the need to store geometries. Explore some of the operations you can now run with Felt SQL queries in the table below.

Example SQL Operations now Available

SELECT a.*, b.*
FROM service_areas a
JOIN market_zones b
ON ST_Intersects(a.geometry, b.geometry);

SELECT a.name, b.name
FROM points a
JOIN polygons b
ON ST_Within(a.geometry, b.geometry);

SELECT 
    location_id,
    ST_Buffer(geometry, 5000) as service_area
FROM distribution_centers;

SELECT 
    region_name,
    ST_Centroid(geometry) as center_point
FROM service_regions;

SELECT 
    a.id, b.id,
    ST_Distance(a.geometry, b.geometry) as distance
FROM points a
CROSS JOIN points b
WHERE a.id != b.id;

SELECT 
    territory_id,
    ST_Area(geometry::geography) as area_sqm
FROM market_territories;

SELECT 
    route_id,
    ST_Length(geometry::geography) as length_meters
FROM delivery_routes;

SELECT 
    region_id,
    ST_Union(geometry) as combined_geometry
FROM parcels
GROUP BY region_id;

SELECT 
    ST_Intersection(a.geometry, b.geometry) as overlap
FROM zone_a a
CROSS JOIN zone_b b;

SELECT 
    id,
    ST_Transform(geometry, 4326) as wgs84_geom
FROM locations;

SELECT 
    region_id,
    ST_Envelope(ST_Collect(geometry)) as bbox
FROM market_regions
GROUP BY region_id;

SELECT 
    cluster_id,
    ST_ConvexHull(ST_Collect(geometry)) as boundary
FROM points
GROUP BY cluster_id;

SELECT 
    points.*
FROM customer_points points
JOIN service_areas areas
ON ST_Contains(areas.geometry, points.geometry);

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Workflow enhancements for enterprise-wide data utilization

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Felt SQL queries deliver immediate value through seamless integration with your existing data infrastructure and the power of cloud-based collaboration. Plus, we've added a few enhancements to speed up data workflows across your organization:

• Robust Cloud Database Compatibility: Native support for major cloud databases including BigQuery, Redshift, Databricks, Microsoft SQL Server, Snowflake, and PostGIS.
• Intelligent Query Management: Advanced autocomplete library and preview capabilities ensure optimal compute efficiency.
• Real-time Data Synchronization: Optional live updates eliminate manual refresh cycles, ensuring teams always work with current data.
• Recent Query Reference Windows: We've made it easy to create new layers that build off existing queries.
• Direct-to-End-User Delivery: Because Felt is entirely on the web, your results can be shared instantly with your end users via a link.

These workflow enhancements ensure that anyone on the team can jump into a project and deliver results quickly.

Take the Next Step

Are you ready to transform your organization's spatial data capabilities? Start your enterprise trial today or connect with our solutions team to discuss your specific needs. Connect with our solutions team to discuss your specific needs. Book a demo here.