Choosing the Right Query Engine for Cloud-Native Geospatial Analytics

You’ve built the pipelines. Your data is in cloud-native formats. You have a lakehouse architecture that can govern petabytes of spatial data. Now what?

Now you need to actually query that data and extract insights from it. And this is where the rubber meets the road for cloud-native geospatial systems.

The query engine you choose determines whether your carefully crafted data architecture actually delivers on its promise of making geospatial data analytics-ready. It determines whether analysts can get answers in seconds or hours. It determines whether you can join GPS trajectories with weather data and business metrics in a single query, or whether you need to export data to multiple systems and stitch results together manually.

This is the layer where most geospatial systems succeed or fail. You can have the best data pipelines and the most elegant lakehouse architecture in the world, but if your query engine can’t efficiently process spatial operations at scale, none of that infrastructure matters.

The OLAP reality check

Let’s start with the uncomfortable truth: most modern query engines are OLAP (Online Analytical Processing) systems designed for business analytics, not geospatial analytics.

OLAP systems excel at aggregating large amounts of structured data quickly. They can sum sales by region, calculate average customer lifetime value, or identify trends in user behavior across millions of records. They’re optimized for queries that scan lots of data but return relatively small result sets.

This makes them excellent for certain types of geospatial analytics:

Spatial aggregations work well. Count the number of GPS pings per county. Calculate average property values by zip code. Sum delivery times by metropolitan area. These queries fit the OLAP pattern perfectly.

Temporal analysis works well. Time-series queries, seasonal trend analysis, and year-over-year comparisons are bread and butter for OLAP systems.

Joining spatial data with business data works well. Correlating customer locations with sales performance, analyzing foot traffic versus retail performance, or combining demographic data with location data – all natural fits for OLAP.

But OLAP systems struggle with operations that are fundamental to geospatial analysis:

Spatial topology is expensive. Finding all polygons that intersect with a given geometry, calculating buffers around points, or determining spatial relationships between complex shapes requires specialized algorithms and data structures that OLAP systems weren’t designed for.

Nearest neighbor queries don’t optimize well. Finding the closest hospital to each accident location, identifying the 10 nearest competitors to each store, or routing optimization problems require spatial indices that most OLAP systems don’t have.

Raster operations are not supported. Calculating NDVI from satellite imagery, performing image classification, or extracting elevation profiles from DEMs are completely outside the scope of what OLAP systems can do.

Coordinate system transformations are limited. Most OLAP systems assume spatial data is in a single coordinate system and don’t handle reprojection, datum transformations, or coordinate system validation.

This doesn’t make OLAP systems bad for geospatial – it just means you need to understand what they’re good at and what they’re not.

The query engine landscape

Let’s break down the major query engines and how they handle geospatial data:

DuckDB: The local powerhouse

DuckDB has become the darling of the analytics world for good reason. It’s fast, runs locally, and handles cloud-native formats natively. For geospatial, it’s particularly interesting.

Strengths:

• Native support for GeoParquet reading and spatial operations
• Excellent performance for local analysis and prototyping
• Integrates seamlessly with Python and R workflows
• Can query data directly from S3 without downloading
• Strong SQL interface with spatial functions

Limitations:

• Single-node architecture limits scalability
• Spatial function library is growing but still limited compared to PostGIS
• No built-in support for raster operations
• Limited spatial indexing compared to specialized spatial databases

Best use cases: Exploratory analysis, prototyping, small to medium datasets (up to hundreds of GB), local development workflows.

BigQuery: Google’s cloud-native approach

BigQuery has been investing heavily in geospatial capabilities and has some unique advantages for spatial analytics.

Strengths:

• Serverless scaling handles massive datasets automatically
• Native geography data type with spatial functions
• Excellent integration with Google Earth Engine for satellite imagery analysis
• Strong performance on aggregation queries across billions of spatial records
• Built-in visualization with Google Maps integration

Limitations:

• Geography data type is limited to WGS84 coordinate system
• Spatial functions are more limited than PostGIS
• No support for complex geometry types or topology operations
• Expensive for frequent small queries
• Limited support for cloud-native geospatial formats

Best use cases: Large-scale spatial aggregations, integration with Google Cloud ecosystem, web mapping applications that need Google Maps integration.

Snowflake: The enterprise favorite

Snowflake has been gradually adding spatial capabilities, though they’re still catching up to other platforms.

Strengths:

• Excellent data governance and security features
• Strong performance on complex joins between spatial and business data
• Native support for semi-structured data (JSON, Parquet)
• Automatic scaling and optimization
• Growing ecosystem of spatial functions

Limitations:

• Spatial capabilities are relatively new and limited
• No native support for cloud-native geospatial formats
• Spatial indexing is not as advanced as specialized systems
• Can be expensive for spatial workloads that require significant compute

Best use cases: Enterprise environments that need strong governance, combining spatial data with existing business intelligence workflows.

Amazon Redshift: The data warehouse approach

Redshift has spatial capabilities but they’re clearly an afterthought to the core data warehousing functionality.

Strengths:

• Mature platform with strong ecosystem integration
• Good performance on large aggregation queries
• Integration with AWS services
• Cost-effective for predictable workloads

Limitations:

• Limited spatial function library
• Poor performance on complex spatial operations
• No support for cloud-native geospatial formats
• Requires significant tuning for spatial workloads

Best use cases: Organizations heavily invested in AWS ecosystem that need basic spatial analytics capabilities.

Databricks SQL Warehouse: The unified analytics platform

Databricks has made significant investments in spatial capabilities, particularly around H3 indexing and Apache Sedona integration.

Strengths:

• Native H3 functions for discrete spatial analytics
• Integration with Apache Sedona for advanced spatial operations
• Excellent performance on large-scale spatial joins using H3 indexing
• Strong ML integration for spatial machine learning workflows
• Support for Delta Lake with spatial optimizations

Limitations:

• H3-centric approach sacrifices precision for performance
• Limited support for exact spatial operations
• Complex pricing model can make costs unpredictable
• Requires understanding of distributed systems concepts

Best use cases: Large-scale mobility analysis, spatial machine learning, organizations that can accept H3 approximations for performance gains.

Trino: The federated query engine

Trino (formerly Presto) provides an interesting approach by federating queries across multiple data sources.

Strengths:

• Can query multiple data sources in a single query
• Good performance on analytical workloads
• Open source with strong community
• Plugin architecture allows for custom geospatial extensions

Limitations:

• Spatial capabilities depend on connector implementations
• Performance can be inconsistent across different data sources
• Complex to set up and maintain
• Limited built-in spatial functions

Best use cases: Organizations with data spread across multiple systems, need to join spatial data from different sources.

Choosing the right engine for geospatial workloads

The choice of query engine should be driven by your specific use cases and data characteristics:

For exploratory analysis and prototyping

DuckDB is hard to beat. It’s fast, easy to set up, works with cloud-native formats, and integrates seamlessly with data science workflows. Start here for most geospatial analytics projects.

For large-scale spatial aggregations

BigQuery and Databricks SQL both excel at this, with different trade-offs. BigQuery if you need exact geometries and integration with Google services. Databricks if you can accept H3 approximations for better performance.

For enterprise BI integration

Snowflake makes sense if you’re already invested in their ecosystem and need to combine spatial data with existing business intelligence workflows. The spatial capabilities are growing rapidly.

For complex spatial operations

None of these OLAP systems are great at complex spatial operations. You’ll likely need to push complex spatial processing into your data pipelines using tools like Apache Sedona, GDAL, or PostGIS, and then use OLAP systems for the analytical queries on the results.

For real-time spatial queries

OLAP systems generally aren’t designed for low-latency queries. If you need real-time spatial query capabilities, you’ll likely need a separate operational system like PostGIS or Elasticsearch with spatial capabilities.

The processing vs. querying distinction

This brings up a crucial architectural point: there’s a difference between spatial data processing and spatial data querying.

Spatial data processing includes operations like:

• Coordinate system transformations
• Spatial joins between complex geometries
• Raster analysis and image processing
• Trajectory analysis and map matching
• Complex topology operations

These operations are computationally expensive and often need specialized algorithms. They belong in your data pipelines, not your query layer.

Spatial data querying includes operations like:

• Filtering by spatial predicates (within bounding box, intersects simple geometry)
• Aggregating by spatial regions
• Joining pre-processed spatial data with business data
• Simple distance calculations
• Visualization queries

These operations can be efficiently handled by modern OLAP systems, especially when the heavy spatial processing has already been done in the pipeline layer.

The emerging pattern

The pattern that’s emerging for scalable geospatial analytics is a clear separation between processing and querying:

Use specialized spatial processing tools (Apache Sedona, PostGIS, GDAL, custom algorithms) in your data pipelines to handle complex spatial operations, coordinate transformations, and format conversions.

Use OLAP query engines for analytical queries on the processed results. Store intermediate results in formats optimized for analytical queries (GeoParquet with spatial indexing, pre-calculated H3 indices, aggregated spatial summaries).

Use operational databases (PostGIS, Elasticsearch) for applications that need low-latency spatial queries or complex spatial operations in real-time.

This architecture leverages the strengths of each system type while avoiding their weaknesses.

What this means for your architecture

If you’re building a cloud-native geospatial system, plan for multiple query engines serving different use cases:

DuckDB for data scientists doing exploratory analysis and prototyping.

BigQuery/Snowflake/Databricks for analysts running business intelligence queries that combine spatial and non-spatial data.

PostGIS for applications that need real-time spatial queries or complex spatial operations.

Specialized tools in pipelines for heavy spatial processing that needs to happen before data reaches the query layer.

The key is recognizing that no single system does everything well, and that’s okay. The power comes from using each system for what it’s best at and integrating them effectively.

What’s next

Query engines are where your data architecture meets your users. But having great query performance is only valuable if people can actually access and use the insights.

In my next post, I’ll dive into the application layer – how to build interfaces, visualizations, and applications on top of cloud-native geospatial systems. How do you connect BI tools to spatial data? What are the options for web mapping on cloud-native architectures? And how do you build applications that can take advantage of the scale and flexibility you’ve built into your data platform?

The formats make it possible. The pipelines make it practical. The lakehouses make it governable. The query engines make it fast. The applications make it usable.