- Databricks eliminates data movement—bring compute to your petabytes instead of moving petabytes to compute
- Mosaic + Photon delivers 10-100x speedups on spatial joins compared to single-node processing
- The lakehouse pattern unifies your geospatial and business data without complex ETL pipelines
- Skip Databricks if: <100GB data, simple analysis, team lacks Spark skills, or budget is tight
Your organisation already has Databricks. The data engineering team uses it. The ML team uses it. Finance reports run on it. And now someone asks: "Can we run our geospatial workloads here too?"
The answer is usually yes—but not always. This post helps you understand when Databricks is the right choice for geospatial, and when simpler alternatives will save you time and money.
The Data Gravity Problem
Geospatial datasets are heavy. Satellite imagery for a single country runs into terabytes. Building footprints for major metros hit hundreds of gigabytes. Population rasters, administrative boundaries, infrastructure networks—it adds up fast.
The traditional approach: move data to the tool. Download from S3 to your laptop. Copy to a shared drive. Load into desktop GIS. Run the analysis. Export results. Upload somewhere else.
The Hidden Cost of Data Movement
TRANSFER TIME
100GB at 100Mbps = 2+ hours
EGRESS COSTS
$0.09/GB = $9 per 100GB move
DUPLICATION
Same data in 5 places = 5x storage
Data gravity is the principle that data attracts applications. It's cheaper and faster to bring compute to data than to move data to compute. This is Databricks' core value proposition for geospatial.
If your geospatial data already lives in cloud storage (S3, Azure Blob, GCS), and your organisation already uses Databricks for other workloads, you're paying for both anyway. Running geospatial on Databricks is incremental compute cost, not new infrastructure.
Lakehouse Architecture for Geospatial
The "lakehouse" is Databricks' term for unified analytics: one platform for data engineering, data science, and business intelligence. For geospatial, this means three things:
1No ETL to Separate Systems
Traditional pattern: Extract geospatial from PostGIS, transform, load to data warehouse, then join with business data. Lakehouse pattern: Your GeoParquet files sit next to your Parquet business data. Join directly. No intermediate pipeline.
2Same Tools for Spatial and Tabular
Your team already knows Spark SQL, Python, notebooks. Geospatial on Databricks uses the same interfaces. The spatial functions (ST_Contains, ST_Distance, ST_Buffer) feel like regular SQL functions because they are.
3Unified Governance
Unity Catalog manages access to geospatial data the same way it manages access to everything else. One set of permissions. One audit log. No special handling for spatial files.
The practical implication: if you're already a Databricks shop, adding geospatial is a library installation, not a platform migration.
Mosaic + Photon: The Performance Story
Mosaic is Databricks' open-source geospatial library. It provides the spatial functions. Photon is Databricks' vectorised query engine that makes those functions fast. Recent updates have made spatial joins up to 17x faster.
The combination delivers performance that single-node tools can't match:
| Operation | GeoPandas (1 node) | Mosaic + Photon (8 nodes) |
|---|---|---|
| Point-in-polygon (10M points, 50K polygons) | 45 min | 28 sec |
| Spatial join (building footprints to census) | 2+ hours | 3 min |
| Buffer + dissolve (295K water features) | 5+ hours | 1-2 min |
These aren't cherry-picked benchmarks. They're patterns we see repeatedly in production pipelines. The speedup comes from parallelisation—distributing work across nodes—combined with Photon's vectorised execution.
The H3 Optimisation
Mosaic uses H3 (Uber's hexagonal hierarchical spatial index) to partition data. This means spatial joins don't require full-table scans. The index prunes irrelevant partitions before the join even starts. For large datasets, this is often where most of the speedup comes from.
The Ecosystem Advantages
Beyond raw performance, Databricks brings ecosystem benefits that matter for enterprise geospatial:
Jobs API
Programmatic orchestration. Schedule pipelines, trigger on events, chain tasks with dependencies. No manual clicking.
Git Integration
Pull code directly from repos. Jobs run latest version automatically. No "forgot to git pull" production incidents.
Autoscaling
Spin up 8 nodes for the heavy spatial join, scale back to 2 for the I/O-bound steps. Pay for what you use.
Unity Catalog
Fine-grained access control. Column-level masking. Lineage tracking. Audit logs. Enterprise governance out of the box.
If your organisation already invested in these capabilities for non-spatial workloads, you get them for free when you add geospatial.
When Databricks Is NOT the Answer
Databricks adds complexity and cost. For some workloads, that overhead isn't justified. Here's when to use simpler alternatives:
Small Data (<100GB)
If your entire dataset fits in memory on a decent laptop, GeoPandas on a single machine is simpler, cheaper, and often faster (no cluster overhead). Don't distribute what doesn't need distributing.
Interactive Exploration
QGIS and ArcGIS Pro are purpose-built for visual exploration, digitising, and ad-hoc analysis. Databricks is for batch pipelines, not clicking around a map. Use the right tool for the job.
Team Lacks Spark Skills
Databricks assumes familiarity with distributed computing concepts. If your team is pure desktop GIS, the learning curve may outweigh the benefits. Consider training investment or simpler cloud options first.
Budget Constraints
Databricks licensing adds to cloud compute costs. For occasional workloads, serverless options (AWS Lambda + GeoPandas) or spot instances with Apache Sedona may offer better price-performance for specific query types.
The Honest Answer
Databricks is the right choice when you have: (1) large datasets that don't fit in single-node memory, (2) recurring pipelines that justify infrastructure investment, (3) existing Databricks usage in the organisation, and (4) team skills or training budget for Spark. If you're missing two or more of these, start simpler.
Decision Framework
Use this checklist to evaluate whether Databricks is right for your geospatial workloads:
Score 4-5: Databricks is likely a good fit. Start with a pilot project.
Score 2-3: Evaluate carefully. Consider the learning curve and alternatives.
Score 0-1: Start with simpler tools. Revisit when circumstances change.
Databricks isn't magic. It's infrastructure that makes sense when your data is large, your pipelines are recurring, and your organisation is already invested in the platform.
The lakehouse architecture eliminates the ETL tax of moving data between systems. Mosaic + Photon deliver distributed performance. The ecosystem provides enterprise governance out of the box.
But if your data fits on a laptop, or your team isn't ready for distributed computing, simpler tools will serve you better. The goal is solving the problem, not adopting the fanciest platform.
In Part 2, we'll cover the practical patterns that make Databricks geospatial work: the Volumes I/O gotcha, two-stage writes, and the memory management tricks that prevent 90% of production failures.
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