The Spatial SQL Landscape in 2026: A Guide to 50+ Databases

There are over 50 spatial databases on the market right now. If you’re trying to pick the right one for your next project, that number alone is enough to induce decision paralysis.

But here’s the thing most of these tools fall into just six architectural categories. Once you understand those categories and what they’re optimized for, the choice narrows fast. This guide breaks down the entire spatial SQL landscape so you can stop evaluating tools you don’t need and start building with the ones you do.

Before we get into the tools, we need to talk about the two fundamental types of database work, because picking the wrong architecture for your workflow is the most expensive mistake you can make.

Transactional vs. Analytical: Two Different Jobs

Transactional workloads (OLTP) are the credit card swipe. A user taps their phone, a record gets inserted, a row gets retrieved. It’s fast, it’s targeted, and it operates on individual records. Think: a web app that geocodes an address and returns the nearest store location.

Analytical workloads (OLAP) are the big question. What’s the average property value within 500 meters of every transit stop in the city? What’s the total area of agricultural land that intersects a flood zone? These are massive spatial joins and aggregations across millions sometimes billions of rows. Not a single lookup. A full scan.

The tools that excel at one of these jobs are usually mediocre at the other. That’s not a flaw. It’s a design choice. Keep this distinction in mind as we walk through the six categories.

Relational Databases: The Transactional Standard

PostGIS is the gold standard, full stop. Built on PostgreSQL, it has the longest history of any spatial database, the deepest function library, and the richest ecosystem of extensions pgRouting for network analysis, pg_tileserv for vector tiles, and dozens more. If you’re building an application that needs to read and write spatial data reliably, PostGIS is where you start.

But PostGIS has a ceiling, and it’s architectural. Traditional relational databases couple compute and storage on the same machine. Need more processing power? You buy a bigger server. Need more disk? Same thing. You can’t independently scale one without the other, and you can’t distribute a workload across a hundred machines the way cloud-native systems can. For transactional app workloads, this is rarely a problem. For analytical workloads at scale, it becomes one.

Worth mentioning: SpatiaLite (the spatial extension for SQLite) calls itself the most popular database in the world because it ships on every smartphone. That’s true. But it’s a local, lightweight, embedded tool not something you’d run a production server on.

Best for: Application backends, transactional workloads, teams with deep SQL/PostGIS expertise.

Embedded Analytics: The Middle Ground

DuckDB is the tool that changed the game for local analytics. If PostGIS is the king of transactions, DuckDB is the answer for analysts who need speed without infrastructure.

DuckDB is an embedded analytical database think of it as what SQLite is for transactions, but built for heavy analytical queries. It runs as a single file on your laptop with zero configuration. No server, no Docker containers, no cluster management. Just fast columnar processing on whatever hardware you already have.

What makes it fast is its vectorized execution engine. Instead of reading every single cell one at a time, DuckDB processes data in chunks. For each chunk, it builds summary statistics min, max, count and if a query doesn’t need that chunk, it skips it entirely. The result is that analytical queries over millions of rows run in seconds on a MacBook.

SedonaDB is a new entry to this landscape in the last year. It is also an embedded analytical database that has many of the same advantages as DuckDB, but it is written in Rust and backed by Apache Datafusion, and that makes the spatial specific functions blazing fast. Best part is you can use the two together using GeoParquet files as the base.

The spatial extension for DuckDB is still maturing compared to PostGIS, but for exploratory analysis, format conversion, and crunching through GeoParquet files locally, it’s become an essential tool in the modern geospatial stack.

Best for: Data science on your laptop, exploratory spatial analysis, fast local processing of GeoParquet and other columnar formats.

Data Warehouses: The Costco of Data

Snowflake, BigQuery, and AWS Redshift are the enterprise data warehouses, and the easiest way to understand how they work is the Costco analogy.

Walk into a Costco and you’ll notice the store is organized by aisle canned goods in one section, frozen food in another, cleaning supplies in a third. You don’t wander the entire store to find what you need. You walk straight to the right aisle.

Data warehouses do the same thing through partitioning. Your data gets organized by date, region, category whatever makes sense for your queries. When you run a query, the engine only reads the partitions it needs and skips the rest. On a table with a billion rows partitioned by date, a query for last Tuesday’s data might only touch 0.3% of the total dataset.

The other major advantage over PostGIS is separation of compute and storage. Your data lives in cheap cloud storage (S3, GCS). Your compute spins up independently when you need it and shuts down when you don’t. With Snowflake, you pick a “T-shirt size” for your compute cluster Small, Medium, X-Large. With BigQuery, Google manages the sizing for you, giving it a serverless feel. Either way, you’re not paying for a beefy server sitting idle at 3 AM.

Spatial support in these platforms has improved significantly. BigQuery’s geography functions are solid for large-scale point-in-polygon and distance operations. Snowflake’s H3 integration makes hexagonal spatial indexing native. But neither matches PostGIS in function depth, and complex geometric operations can still be awkward.

Best for: Enterprise analytics teams already in the cloud, spatial aggregations over massive structured datasets, organizations that need managed infrastructure.

Distributed Systems & Spark: The Assembly Line

If a data warehouse is Costco, Spark is an assembly line. You take a massive job, break it into tiny pieces, send each piece to a different machine in a cluster, and reassemble the results at the end. It’s built for the jobs that are too big for any single machine to handle billions of geometries, continent-scale raster processing, multi-terabyte spatial joins.

Apache Sedona is the PostGIS of the Spark world. It’s the open-source framework that teaches Spark how to understand geometry. Sedona supports spatial SQL with familiar functions (ST_Contains, ST_Intersects, ST_Buffer), spatial indexing, and critically raster data processing. Raster support in SQL is rare. Most of the tools we’ve discussed are vector-only. Sedona handles both.

Wherobots is built by the creators of Sedona and takes the concept further. It’s a managed spatial compute platform that’s been optimized specifically for spatial workloads, running 20–60x faster than standard Spark on spatial operations because it understands spatial indexing natively rather than treating geometry as just another data type.

There’s an infrastructure advantage here that’s easy to miss. Wherobots and Sedona operate on a Lakehouse architecture they query data directly where it lives (GeoParquet files in S3, Cloud Optimized GeoTIFFs in cloud storage) without copying or moving it. When your compute runs in the same data center as your data say, AWS us-west-2 in Oregon where a huge volume of open geospatial data is stored the latency is near zero. You’re reading terabytes of raster imagery without a single file transfer.

Best for: Massive-scale spatial data engineering, raster + vector workflows in SQL, Lakehouse architectures built on open formats like Iceberg and GeoParquet.

Distributed Query Engines: One SQL to Rule Them All

Trino and PrestoDB came out of Facebook (Meta), born from a specific problem: data scattered across too many systems with no unified way to query it. These engines sit on top of your existing data sources your data lake, your warehouse, your relational database and let you write one SQL query that federates across all of them.

They’re excellent for querying data that’s already clean and structured. They are not designed for heavy ETL, complex spatial processing pipelines, or building applications. Think of them as the read layer across a messy data landscape.

Best for: Federated queries across multiple data sources, organizations with fragmented data infrastructure.

Real-Time & GPU: Speed at a Cost

Two specialized categories live at the extreme end of the performance spectrum.

GPU databases like Heavy.AI and Kinetica are staggeringly fast. Visualizing and querying billions of points in real time, interactively, with sub-second response that’s what GPUs enable. But the cost model is brutal. GPU hardware is expensive, and these systems typically need to be “always on,” which means you’re paying for that power 24/7 whether you’re using it or not. There’s no serverless option to spin down at midnight. For the right use case (defense, real-time surveillance, massive IoT streams), the cost is justified. For most teams, it’s not.

User-facing analytics engines like Apache Pinot solve a different problem entirely. Open Uber Eats and see “5 people nearby ordered this in the last hour” that’s Pinot. It’s built for millions of concurrent end users running simple, pre-defined queries simultaneously. It is not a tool for data scientists doing deep spatial analysis. It’s the serving layer for consumer-facing applications that need spatial context at massive concurrency.

Best for: GPU databases → real-time visualization of billions of records, defense/IoT. Apache Pinot → user-facing applications with millions of concurrent spatial queries.

The Verdict: Right Tool, Right Job

The spatial SQL landscape is large, but the decision tree is actually straightforward:

• Building an application? Start with PostGIS. It’s battle-tested, the ecosystem is unmatched, and every spatial developer knows it.
• Doing data science on your laptop? Grab DuckDB and SedonaDB. Zero setup, blazing fast on columnar data, and perfect for exploratory spatial analysis.
• Running enterprise analytics in the cloud? BigQuery or Snowflake will handle your scale with managed infrastructure and solid spatial support.
• Processing spatial data at massive scale especially raster? Apache Sedona and Wherobots are purpose-built for this. The Lakehouse architecture, native raster support, and Spark-scale distribution put them in a category that no other tool occupies.

The worst decision you can make isn’t picking the wrong tool from within a category. It’s picking the wrong category entirely forcing a transactional database to do analytical work, or spinning up a Spark cluster for a job DuckDB could handle on your laptop in three seconds.

Understand the architecture first. The tool choice follows.