GeoParquet: The Fast, Open, and Scalable Way to Store Geospatial Data
- GeoWGS84
- Aug 11
- 3 min read
There has never been a greater demand for effective, scalable, and interoperable data formats in the quickly changing geospatial data ecosystem. For decades, the GIS community has relied on traditional formats like Shapefile, GeoJSON, and even GeoPackage. However, as cloud-native geospatial processes have grown exponentially, these formats frequently find it difficult to match the performance, scalability, and interoperability requirements of contemporary applications.
An innovative approach to effectively store vector geospatial data is GeoParquet, an open, columnar, and cloud-friendly data format. GeoParquet, which is based on the Apache Parquet standard, offers open standards compliance, enormous scalability, and analytical speed to geographic processing and storage.
What is GeoParquet?
Geospatial vector data can be encoded in Apache Parquet files using the GeoParquet specification. For high-performance analytical queries in big data settings, Parquet is a columnar storage format. GeoParquet adds the following to Parquet:
Geometry Encoding: The representation of geometry objects in Well-Known Binary (WKB).
Spatial Metadata: CRS (Coordinate Reference System), geometry types, and other spatial properties are described by GeoJSON-based metadata.
Schema consistency allows for smooth interoperability by guaranteeing that spatial columns adhere to a standard.
Making geographic datasets cloud-native, quick to read, and compatible with several analytical ecosystems, including Apache Arrow, DuckDB, Spark, and PostGIS, is the aim.
Why GeoParquet is a Game-Changer
Performance Through Columnar Storage
Columnar storage arranges data according to columns rather than rows, in contrast to row-based formats (like Shapefile). This makes it possible for:
Only the columns that are required are read into memory in vectorized queries.
Effective compression: columns with comparable data compress more effectively.
In big data pipelines, batch processing speed is essential for geospatial analytics.
A geographic query that merely requires timestamps and coordinates, for example, will not read characteristics like land cover or population density.
Cloud-Native and Scalable
AWS S3, Azure Blob Storage, and Google Cloud Storage are among the object storage platforms for which GeoParquet is optimized. This makes it ideal for distributed computing frameworks and serverless GIS designs like:
Spark by Apache
Dask
BigQuery
A snowflake
Petabyte-scale geospatial processing is made possible by the ability to segment data according to spatial tiles, time intervals, or thematic qualities.
Interoperability Across the Data Stack
GeoParquet easily integrates with contemporary analytical ecosystems because it is based on Parquet:
Python: Dask, Fiona, PyArrow, and GeoPandas -The Geopandas
Trino, Athena, and DuckDB are SQL engines.
GIS Tools: GDAL, QGIS (via plugins),
Cloud warehouses: Snowflake, Redshift, and BigQuery
The ETL bottleneck that frequently occurs when transferring geographic data between analytical tools is removed by this compatibility.
Open and Extensible
The Open Geospatial Consortium (OGC) and community members are responsible for maintaining the open specification GeoParquet. This guarantees:
Lack of vendor lock-in
Open and honest government
Flexibility for upcoming requirements for spatial data (e.g., temporal-spatial characteristics, 3D geometries)
Technical Structure of a GeoParquet File
A normal GeoParquet file consists of:
Geometry Column: A binary column containing WKB data.
Geo Metadata: Found under the geo key in the key-value metadata of the Parquet file.
Attributes: Standard Parquet columns are used to record all other vector attributes.
CRS Information: Employing PROJ strings or EPSG codes in metadata.
Example Metadata Snippet:
{
"version": "1.0.0",
"primary_column": "geometry",
"columns": {
"geometry": {
"encoding": "WKB",
"geometry_types": ["Polygon"],
"crs": {
"type": "name",
"properties": { "name": "EPSG:4326" }
}
}
}
}
When to Use GeoParquet
The best uses for GeoParquet are:
Cloud data lakes with enormous vector datasets.
Geospatial time-series analytics (such as satellite image footprints)
Geospatial pipelines that are cross-platform
Dashboards with high performance that query real-time geographical data
Preprocessing machine learning for location-based models
GeoParquet serves as a link between contemporary big data analytics and conventional GIS, making it more than just another geographical format. The next generation of quick, scalable, and interoperable geospatial processes is made possible by the combination of open standards, columnar efficiency, and cloud-native architecture.
For more information or any questions regarding the GeoParquet, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
(A GeoWGS84 Corp Company)
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