We're excited to announce a significant expansion of Felt's enterprise capabilities with the launch of SQL Queries. As the fastest way to build spatial applications, maps, and dashboards, Felt has become the cornerstone of spatial operations for leading organizations. Our native database connectors have driven widespread enterprise adoption, and today we're responding to our users' most pressing need: direct SQL query capabilities for streamlined spatial analysis.

Reduce time-to-insight: execute complex spatial analyses in seconds

With Felt SQL Queries, you can now efficiently query just the data you need directly from your cloud database. Run a simple operations, or execute complex spatial analysis across multiple data tables with unprecedented efficiency. Our implementation eliminates the need for complex GUI interactions, allowing your team to perform sophisticated analyses with concise SQL expressions.

Image

Image

Here are just some of the types of SQL analysis you can run with Felt’s new SQL Queries to virtually eliminate redundant data processing:

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

SELECT 
    a.region_name,
    b.sales_data,
    b.geometry
FROM regions a
JOIN sales_points b
ON ST_Within(b.geometry, a.geometry);

SELECT 
    region_id,
    COUNT(*) as location_count,
    SUM(revenue) as total_revenue,
    ST_Union(geometry) as combined_geometry
FROM business_locations
GROUP BY region_id;

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

SELECT 
    a.*,
    b.demographic_data,
    b.market_potential
FROM store_locations a
LEFT JOIN market_data b
ON ST_Intersects(a.geometry, b.geometry);

SELECT * 
FROM market_areas
WHERE ST_Area(geometry) > 5000
AND population_density > 1000;

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

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

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

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

SELECT 
    customer_id,
    store_id,
    ST_Distance(c.geometry, s.geometry) as distance
FROM customers c
CROSS JOIN LATERAL (
    SELECT id as store_id, geometry
    FROM stores
    ORDER BY geometry <-> c.geometry
    LIMIT 1
) s;

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

Supports enterprise-wide spatial data utilization

Our SQL Queries implementation delivers immediate value through seamless integration with your existing data infrastructure and the power of cloud-based collaboration.

• Cloud Database Compatibility: Native support for major cloud databases including BigQuery, Redshift, Databricks, Snowflake, and PostgreSQL
• 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
• 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.

We’ve also included some key workflow shortcuts to increase efficiency across your team. Recent queries are retained for easy reuse and users can always check a map layer’s initial query.

Image

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.

Request Enterprise Consultation