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new 3dd3abe8fd [DOCS] Add Apache Sedona 2025 Year In Review blog post
(#2591)
3dd3abe8fd is described below
commit 3dd3abe8fdb6592c3d5587649a53fc2866408a86
Author: Jia Yu <[email protected]>
AuthorDate: Wed Jan 14 00:52:50 2026 -0700
[DOCS] Add Apache Sedona 2025 Year In Review blog post (#2591)
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+---
+date:
+ created: 2026-01-11
+links:
+ - Release notes: https://sedona.apache.org/latest/setup/release-notes/
+ - SedonaDB: https://sedona.apache.org/sedonadb/
+ - SpatialBench: https://sedona.apache.org/spatialbench/
+ - Apache Parquet and Iceberg native geo type:
https://wherobots.com/blog/apache-iceberg-and-parquet-now-support-geo/
+authors:
+ - jia
+title: "Apache Sedona 2025 Year in Review"
+---
+
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+
+2025 was a milestone year for **Apache Sedona**. We made major progress in
distributed spatial analytics on Spark, Flink, and Snowflake, launched a new
single-node engine called SedonaDB, and pushed forward benchmarking and open
geospatial data standards.
+
+This post summarizes the most important highlights from the Apache Sedona
ecosystem in 2025.
+
+<!-- more -->
+
+## Apache Sedona Ecosystem Releases in 2025
+
+Apache Sedona shipped four releases from January 2025 to January 2026: 1.7.1,
1.7.2, 1.8.0, and 1.8.1. In the same year, the Sedona ecosystem expanded in two
major ways: we introduced SedonaDB for fast single-machine analytics and
SpatialBench to make spatial performance comparisons reproducible.
+
+- Apache Sedona releases: Ongoing improvements across distributed engines and
integrations (Spark, Flink, Snowflake). See the release notes for details.
+- SedonaDB: A new single-node spatial engine built for interactive analytics
and developer workflows.
+- SpatialBench: A benchmark suite designed to standardize how we evaluate
spatial SQL performance across engines.
+
+Release notes:
[https://sedona.apache.org/latest/setup/release-notes/](https://sedona.apache.org/latest/setup/release-notes/)
+
+## Distributed Engines Highlights
+
+Across SedonaSpark, SedonaFlink, and SedonaSnow, 2025 brought major usability
improvements, broader SQL coverage, and better support for modern open
geospatial data formats:
+
+* GeoPandas API on SedonaSpark: Write GeoPandas-style code, but run it on
Spark through Sedona, so familiar workflows like spatial joins (`sjoin`),
buffering, distance, and coordinate system transforms can scale beyond a single
machine. Learn more: [GeoPandas API for Apache
Sedona](../../tutorial/geopandas-api.md).
+* GeoStats for clustering, outliers, and hot spots: Built-in tools for common
spatial statistics workflows on DataFrames, including DBSCAN clustering, Local
Outlier Factor (LOF), and Getis-Ord Gi/Gi* hot spot analysis. Learn more:
[Stats module](../../api/stats/sql.md).
+* Faster SedonaSpark to GeoPandas conversion with GeoArrow: Convert query
results to GeoPandas more efficiently using Arrow/GeoArrow, such as
`geopandas.GeoDataFrame.from_arrow(dataframe_to_arrow(df))`. Learn more:
[GeoPandas + Shapely interoperability](../../tutorial/geopandas-shapely.md).
+* STAC catalog reader: Load STAC collections from local files, S3, or HTTPS
endpoints using `sedona.read.format("stac")`, and apply time/area filters early
so you read less data. Supports authenticated STAC APIs too. Learn more: [STAC
catalog with Apache Sedona and
Spark](../../tutorial/files/stac-sedona-spark.md).
+* More built-in data sources: Easier ingestion from formats people use in
practice, including GeoPackage and OSM PBF (OpenStreetMap). Learn more:
[SedonaSQL / DataFrame I/O tutorial](../../tutorial/sql.md).
+* Vectorized UDFs (Python): A faster way to run Python UDFs by processing data
in batches using Apache Arrow, including geometry-aware UDFs with Shapely or
GeoPandas GeoSeries. Learn more: [Spatial vectorized UDFs (Python
only)](../../tutorial/sql.md).
+* More functions across engines: Function coverage kept expanding across
Spark, Flink, and Snowflake. For example: ST_ApproximateMedialAxis,
ST_StraightSkeleton, ST_Collect_Agg, and ST_OrientedEnvelope. See the function
catalogs for [SedonaSpark SQL](../../api/sql/Overview.md), [SedonaFlink
SQL](../../api/flink/Overview.md), and [SedonaSnow
SQL](../../api/snowflake/vector-data/Overview.md).
+
+## SedonaDB: A New Single-Node Spatial Engine
+
+One of the biggest developments in 2025 was the introduction of SedonaDB, a
new analytics engine designed for geospatial data on a single machine.
+
+SedonaDB was announced in September 2025 and represents a new direction for
the Sedona project family. It is written in Rust and built on Apache Arrow and
DataFusion, enabling fast columnar execution with a lightweight deployment
model.
+
+SedonaDB shipped two releases in 2025: 0.1.0 (initial release) and 0.2.0
(major expansion).
+
+The initial 0.1.0 release introduced the core engine with native geometry and
geography types, built-in spatial indexing, and optimized spatial joins and
nearest-neighbor queries, with Python and SQL interfaces and a zero-setup,
embedded-style experience.
+
+SedonaDB 0.2.0, released in December 2025, rapidly expanded the engine with
broader spatial SQL coverage including raster, native support for reading GDAL
and OGR compatible formats, GeoParquet 1.1 write support with bounding box
metadata, Python UDF support, and initial raster data type support.
+
+Blog posts:
+
+* [Introducing
SedonaDB](https://sedona.apache.org/latest/blog/2025/09/24/introducing-sedonadb-a-single-node-analytical-database-engine-with-geospatial-as-a-first-class-citizen/)
+* [SedonaDB 0.2.0
Release](https://sedona.apache.org/latest/blog/2025/12/01/sedonadb-020-release/)
+
+## SpatialBench: Standardizing Spatial Performance Evaluation
+
+Another major milestone in 2025 was the introduction of SpatialBench, a
benchmark suite designed specifically for spatial SQL workloads.
+
+Traditional database benchmarks often miss the patterns that matter most in
geospatial analytics, such as spatial joins, distance filters, and spatial
aggregations. SpatialBench was created to address this gap.
+
+SpatialBench provides:
+
+* Realistic spatial datasets
+* Configurable scale factors
+* Reproducible query workloads
+* Comparable results across engines
+
+The first SpatialBench release evaluated SedonaDB, DuckDB with spatial
extensions, and GeoPandas, offering transparent and reproducible performance
comparisons.
+
+Blog post: [Introducing
SpatialBench](https://sedona.apache.org/latest/blog/2025/12/11/introducing-spatialbench-performance-benchmarks-for-spatial-database-queries/)
+
+## Advancing Open Geospatial Data Formats
+
+2025 was also a turning point for geospatial interoperability. Apache Iceberg
and Apache Parquet gained native geometry and geography type support, making it
easier to store spatial data directly in open lakehouse tables.
+
+This advancement enables:
+
+* Open and vendor neutral spatial storage
+* Reliable transactions for geospatial tables
+* Filtering data early so engines can scan less
+* Seamless interoperability across engines
+
+Apache Sedona and the broader geospatial community played an active role in
driving this effort forward.
+
+Blog post: [Apache Iceberg and Parquet now support
Geo](https://wherobots.com/blog/apache-iceberg-and-parquet-now-support-geo/)
+
+## Community and Ecosystem Growth
+
+Beyond technical milestones, 2025 saw continued growth in the Apache Sedona
community:
+
+* New committers and contributors joined the project
+ - New committers: Pranav Toggi, Peter Nguyen, Dewey Dunnington
+ - New PMC member: Feng Zhang
+* More contributors participated across the year
+ - In 2025, 27 new contributors made their first contribution to the Apache
Sedona repository for SedonaSpark, SedonaFlink, and SedonaSnow, bringing the
project to 155 total contributors. In total, 46 people contributed to Apache
Sedona in 2025.
+ - New contributors across the ecosystem:
+ - SedonaDB: 26 new contributors in 2025
+ - SpatialBench: 8 new contributors in 2025
+* Adoption continued to grow
+ - Total downloads of Apache Sedona have exceeded 65 million overall.
+ - Monthly downloads are now more than 2 million.
+ - Commit activity increased from 1,509 commits in 2024 to 2,137 commits in
2025.
+
+Sedona’s evolution into a multi-engine, multi-deployment ecosystem reflects
both community demand and sustained contributor effort.
+
+## Looking Ahead to 2026
+
+With strong momentum across distributed analytics, single-node engines,
benchmarking, and open formats, Apache Sedona enters 2026 well positioned for
further growth.
+
+Areas of continued focus include:
+
+* Deeper raster analytics support
+* Expanded SpatialBench coverage
+* Tighter integration with Iceberg native spatial features
+* Improved developer experience across Python, SQL, and Rust
+
+Spatial analytics is becoming a core capability in modern data platforms, and
Apache Sedona is increasingly positioned as a foundational project in that
landscape.
+
+Thank you to everyone in the community who contributed to making 2025 such a
productive year.
+
+## References
+
+* Sedona 1.7.1, 1.7.2, 1.8.0, and 1.8.1 release notes:
[https://sedona.apache.org/latest/setup/release-notes/](https://sedona.apache.org/latest/setup/release-notes/)
+* SedonaDB 0.1.0 release notes:
[https://sedona.apache.org/latest/blog/2025/09/24/introducing-sedonadb-a-single-node-analytical-database-engine-with-geospatial-as-a-first-class-citizen/](https://sedona.apache.org/latest/blog/2025/09/24/introducing-sedonadb-a-single-node-analytical-database-engine-with-geospatial-as-a-first-class-citizen/)
+* SedonaDB 0.2.0 release notes:
[https://sedona.apache.org/latest/blog/2025/12/01/sedonadb-020-release/](https://sedona.apache.org/latest/blog/2025/12/01/sedonadb-020-release/)
+* SpatialBench release notes:
[https://sedona.apache.org/latest/blog/2025/12/11/introducing-spatialbench-performance-benchmarks-for-spatial-database-queries/](https://sedona.apache.org/latest/blog/2025/12/11/introducing-spatialbench-performance-benchmarks-for-spatial-database-queries/)
+* Apache Parquet and Iceberg native geo type:
[https://wherobots.com/blog/apache-iceberg-and-parquet-now-support-geo/](https://wherobots.com/blog/apache-iceberg-and-parquet-now-support-geo/)
