Key takeaways:
- GeoParquet complements existing GIS formats, making it ideal for analytics and cloud-native workflows while operational GIS systems remain in place.
- This format is ideal for read-heavy, analytics-focused use cases, especially when spatial data needs to live in object storage and integrate with modern data platforms.
- Successful adoption is automated using repeatable FME workflows to validate, transform, and publish GeoParquet without disrupting existing processes.
If you work in GIS, you’ve probably noticed GeoParquet popping up with increasing frequency. It’s often positioned as the future of geospatial and the missing link between GIS and analytics platforms.
For many GIS teams, that creates a familiar tension. You don’t want to ignore an emerging standard that’s clearly gaining traction, but you also don’t want to migrate years of spatial data just because something is trending.
This post looks at how GeoParquet is being adopted in real-world organizations, including where it fits and how teams are introducing it without disrupting existing GIS workflows. You’ll find practical guidance on modernizing spatial data pipelines while keeping current systems intact.
What is GeoParquet?
GeoParquet is an open geospatial specification aligned with Open Geospatial Consortium (OGC) standards. It extends Apache Parquet, a columnar, analytics-optimized file format, by defining a standardized way to store geometries (such as points, lines, and polygons), coordinate reference systems, and spatial metadata. Storing spatial information within Parquet files enables tools to reliably interpret the format’s spatial columns alongside non-spatial data.
Why is the GeoParquet format important?
Spatial data is no longer confined to desktop GIS and spatial databases. It’s increasingly showing up in cloud data platforms, analytical workflows, and machine learning pipelines, where traditional GIS formats often feel heavy or awkward because they weren’t designed for distributed query engines or object storage.
Parquet is efficient to query, inexpensive to store, and widely supported across modern data stacks. GeoParquet bridges a gap by allowing GIS data to participate in analytics without repeated reshaping or extraction.
When is it a good fit?
GeoParquet is well-suited to read-heavy, analytics-focused use cases such as reporting, exploratory analysis, feature engineering, and cross-team data sharing. It works particularly well when spatial data lives in object storage and needs to be queried alongside non-spatial datasets in cloud-native environments.
That said, GeoParquet is not a replacement for operational GIS formats. Desktop editing, transactional updates, multi-user workflows, and spatial databases still depend on formats and systems like File Geodatabase, GeoPackage, and PostGIS. GeoParquet is optimized for access and analysis, not for frequent edits or transactional rules.
Step-by-step GeoParquet migration workflow
In practice, GeoParquet migration is a repeatable workflow that runs as part of an automated system as source data changes. That’s why teams tend to implement automated pipelines rather than ad hoc scripts or manual export steps.
For automated data integration workflows, you’ll use FME. FME supports reading and writing GeoParquet, making it possible to connect to data wherever it lives and prepare it for analytics-ready use. Common preparation steps include normalizing coordinate systems and schemas, validating and repairing geometries, checking attributes, and generating validation reports. Once written to GeoParquet, the workflow can be automated to run on demand, on a schedule, or in response to events as upstream data changes.
Here is a step-by-step tutorial on Reading and Writing Partitioned GeoParquet in FME, in which we use FME to read OpenStreetMap data and convert it into GeoParquet for publishing to S3, both with and without partitioning. The result demonstrates how GeoParquet enhances performance and data flexibility. It also demonstrates how partitioning can add value: when reading the GeoParquet output back using FME, the non-partitioned version shows a performance improvement in read time, while the partitioned version shows how cloud-native data can be organized by attribute to enable targeted data access, which is ideal for workflows that only require specific feature types.
Conclusion
GeoParquet is part of the present reality of cloud-native geospatial work. Used thoughtfully, it makes spatial data easier to analyze, share, and integrate with modern data platforms, without forcing teams to abandon established GIS tools and workflows. Using FME, you can integrate, validate, and automate your migration workflow for seamless GeoParquet adoption.
When treated as an addition to a broader GIS data modernization strategy, GeoParquet becomes a practical bridge between GIS, analytics, and enterprise systems. With the right automation and validation in place, teams can adopt it incrementally and confidently, modernizing how spatial data is used without disrupting what already works.
