Data Compression
Modern Geospatial Storage

Geospatial Data Compression & Modern Storage Formats

A focused playbook for engineering teams running spatial data at scale. Migrate legacy Shapefiles and GeoJSON to columnar, cloud-native formats like GeoParquet and FlatGeobuf, and tune the storage layer for predictable cost and query latency.

Practical guides for GIS data engineers, Python backend developers, and cloud architects — covering ZSTD level selection, coordinate precision, row-group sizing, space-filling-curve partitioning, Zarr chunking for raster, PMTiles archives for delivery, schema mapping, metadata preservation, incremental change-data-capture runs, and querying GeoParquet in place with DuckDB, Athena, Trino and Sedona under a predictable storage budget.

Formats Compression Pipelines Query Engines

What's inside

Four connected tracks and 84 guides. Start with format trade-offs — including cloud-native tile archives and Cloud-Optimized GeoTIFF — layer compression, coordinate precision and indexing on top, automate the migration end-to-end with production-grade pipelines that convert only what changed, then query the result in place with cloud SQL engines from a laptop to a Spark cluster.

Storage Fundamentals & Format Comparison

Modern geospatial storage architecture: WKB, CRS handling, columnar vs streaming formats, cloud-native tile archives, and the trade-offs between GeoParquet, FlatGeobuf, PMTiles, COG, GeoJSON, and Shapefile.

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Compression, Chunking & Spatial Indexing

Tune ZSTD levels, size row groups, cut coordinate precision to the accuracy the data has, chunk raster with Zarr, and partition with quadtree / Hilbert curves so spatial queries stay fast at scale.

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Data Conversion & Migration Pipelines

Batch convert legacy Shapefile / GeoJSON to GeoParquet with Python: schema mapping, metadata preservation, parallel execution, validation, fallback routing, and incremental change-data-capture runs that rewrite only what moved.

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Start here

New to cloud-native geospatial storage? These are the most-referenced guides — one strong entry point into each track. Read them in order to go from format choice to a tuned, automated migration.

  1. 1
    Comparing GeoParquet vs FlatGeobuf Performance

    Pick the right cloud-native format: columnar GeoParquet for analytics vs streaming FlatGeobuf for feature-by-feature reads.

  2. 2
    ZSTD Compression Levels for Geospatial Data

    Choose a ZSTD level that balances file size, write cost, and query latency for vector and raster workloads.

  3. 3
    Building Batch Conversion Pipelines with Python

    Automate Shapefile and GeoJSON migration to GeoParquet with schema mapping, parallel execution, and validation.

  4. 4
    Querying GeoParquet with the DuckDB Spatial Extension

    Read GeoParquet straight from object storage with predicate pushdown — no database load step, no data movement.

Why Shapefiles fail at scale Row group sizing for Parquet Cloud cost & storage lifecycle

Newest guides

The five newest areas on the site, each with its own in-depth guides: delivery formats for maps, the encoding layer beneath compression, chunked storage for multidimensional raster, pipelines that convert only what changed, and spatial joins that outgrow a single machine.