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commit 8a458d97e890718b52a9524a7209aeae3f529483
Author: Matthew Powers <matthewkevinpow...@gmail.com>
AuthorDate: Tue Sep 23 20:09:04 2025 -0400
[DOCS] blog: announce sedonadb post (#2361)
* blog: announce sedonadb post
* add more authors
* blacken docs
* run pre commit
* address nits
* add sf10
* pre commit
---
docs/blog/.authors.yml | 24 +++
docs/blog/posts/intro-sedonadb.md | 408 +++++++++++++++++++++++++++++++++++
docs/image/blog/sedonadb1/image1.png | Bin 0 -> 76709 bytes
docs/image/blog/sedonadb1/image2.png | Bin 0 -> 150429 bytes
4 files changed, 432 insertions(+)
diff --git a/docs/blog/.authors.yml b/docs/blog/.authors.yml
index 425cddcec4..368b8341ca 100644
--- a/docs/blog/.authors.yml
+++ b/docs/blog/.authors.yml
@@ -28,3 +28,27 @@ authors:
name: Matthew Forrest
description: Director of Customer Engineering & Product Led Growth,
Wherobots
avatar:
https://media.licdn.com/dms/image/v2/D4E03AQHxYTrEgc53_g/profile-displayphoto-shrink_200_200/profile-displayphoto-shrink_200_200/0/1722352936567?e=2147483647&v=beta&t=X10Z02O2UX8IRmbypcw-m-jbIDeNsPWWL-YOPX_v1XQ
+ jia:
+ name: Jia
+ description: Sedona PMC
+ avatar:
https://media.licdn.com/dms/image/v2/D5603AQEez8EVYH82LQ/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1719619666312?e=1761782400&v=beta&t=4xYyTp1peAtupNdaPfbWEYbdMjMMbcdHWEbqO7Y-2dw
+ dewey:
+ name: Dewey
+ description: Software Engineer
+ avatar:
https://media.licdn.com/dms/image/v2/C5603AQEQpLYQ6u-a8Q/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1630000445999?e=1761782400&v=beta&t=dR8biUFIxB_KL1nYXTibNrjaRzTAkfc3LO6FSfRPREE
+ kristin:
+ name: Kristin
+ description: Software Engineer
+ avatar: https://avatars.githubusercontent.com/u/5501374?v=4
+ feng:
+ name: Feng
+ description: Software Engineer
+ avatar:
https://media.licdn.com/dms/image/v2/C5603AQEuI_xKhOjapA/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1645716542340?e=1761782400&v=beta&t=6vDu04f9Mun3LXoQaTs-C9DVSSZcH1LaasulqWqpliw
+ james:
+ name: James
+ description: Software Engineer
+ avatar:
https://media.licdn.com/dms/image/v2/C4E03AQETRcdZDZNlRg/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1516810351294?e=1761782400&v=beta&t=AlPGe5RqTMd6gfh4qEILx9XsivRWnIM5KIdVSx7AzFE
+ pranav:
+ name: Pranav
+ description: Software Engineer
+ avatar:
https://media.licdn.com/dms/image/v2/D4E03AQE1XVwinur7-w/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1729284611733?e=1761782400&v=beta&t=B1SOzUOz9CW0J0LEVICMLztKFLAwDg3okem5p-VE--4
diff --git a/docs/blog/posts/intro-sedonadb.md
b/docs/blog/posts/intro-sedonadb.md
new file mode 100644
index 0000000000..015e1aae81
--- /dev/null
+++ b/docs/blog/posts/intro-sedonadb.md
@@ -0,0 +1,408 @@
+---
+date:
+ created: 2025-09-24
+links:
+ - Apache Sedona Discord: https://discord.com/invite/9A3k5dEBsY
+authors:
+ - dewey
+ - feng
+ - jia
+ - kristin
+ - matt_powers
+title: "Introducing SedonaDB: A spatial-first query engine"
+---
+
+<!--
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+-->
+
+The Apache Sedona community is excited to announce the initial release of
SedonaDB.
+
+SedonaDB is the first open-source, single-node analytical database engine that
treats spatial data as a first-class citizen.
+
+<!-- more -->
+
+Written in Rust, it’s lightweight, blazing fast, and spatial-native. Out of
the box, it provides:
+
+* Full support for spatial types, joins, CRS (coordinate reference systems),
and functions on top of industry-standard query operations.
+* Query optimizations, indexing, and data pruning features under the hood that
make spatial operations just work with high performance.
+* Pythonic and SQL interfaces familiar to developers, plus APIs for R and Rust.
+* Flexibility to run in single-machine environments on local files or data
lakes.
+
+SedonaDB utilizes Apache Arrow and Apache DataFusion, providing everything you
need from a modern, vectorized query engine. However, it also delivers the
unique ability to run high-performance spatial workloads easily, without
requiring extensions. It's for builders who need a spatial-first query engine.
+
+SedonaDB is easy to download and run on your local machine or in the cloud.
You can install it easily in any runtime.
+
+Apache Sedona already adds geospatial support to Apache Spark (SedonaSpark),
Apache Flink (SedonaFlink), and Snowflake (SedonaSnow). SedonaDB is an
excellent small data complement to the existing big data/streaming Sedona
libraries.
+
+The initial release of SedonaDB provides a comprehensive suite of geometric
vector operations and seamlessly integrates with GeoArrow, GeoParquet, and
GeoPandas. Subsequent releases will support all popular spatial functions,
including functions for raster data.
+
+## SedonaDB quickstart example
+
+Start by installing SedonaDB:
+
+```
+pip install "apache-sedona[db]"
+```
+
+Now instantiate the connection:
+
+```python
+import sedona.db
+
+sd = sedona.db.connect()
+```
+
+Let's perform a spatial join using SedonaDB.
+
+Suppose you have a `cities` table with latitude and longitude points
representing the center of each city, and a `countries` table with a column
containing a polygon of the country's geographic boundaries.
+
+Here are a few rows from the `cities` table:
+
+```
+┌──────────────┬───────────────────────────────┐
+│ name ┆ geometry │
+│ utf8view ┆ geometry <epsg:4326> │
+╞══════════════╪═══════════════════════════════╡
+│ Vatican City ┆ POINT(12.4533865 41.9032822) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ San Marino ┆ POINT(12.4417702 43.9360958) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ Vaduz ┆ POINT(9.5166695 47.1337238) │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+```
+
+And here are a few rows from the countries table:
+
+```
+┌─────────────────────────────┬───────────────┬────────────────────────────────────────────────────┐
+│ name ┆ continent ┆ geometry
│
+│ utf8view ┆ utf8view ┆ geometry
<epsg:4326> │
+╞═════════════════════════════╪═══════════════╪════════════════════════════════════════════════════╡
+│ Fiji ┆ Oceania ┆ MULTIPOLYGON(((180
-16.067132663642447,180 -16.55… │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ United Republic of Tanzania ┆ Africa ┆ POLYGON((33.90371119710453
-0.9500000000000001,34… │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ Western Sahara ┆ Africa ┆ POLYGON((-8.665589565454809
27.656425889592356,-8… │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+```
+
+Here’s how to perform a spatial join to compute the country of each city:
+
+```python
+sd.sql(
+ """
+select
+ cities.name as city_name,
+ countries.name as country_name,
+ continent
+from cities
+join countries
+where ST_Intersects(cities.geometry, countries.geometry)
+"""
+).show(3)
+```
+
+The code utilizes `ST_Intersects` to determine if a city is contained within a
given country.
+
+Here’s the result of the query:
+
+```
+┌───────────────┬─────────────────────────────┬───────────┐
+│ city_name ┆ country_name ┆ continent │
+│ utf8view ┆ utf8view ┆ utf8view │
+╞═══════════════╪═════════════════════════════╪═══════════╡
+│ Suva ┆ Fiji ┆ Oceania │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
+│ Dodoma ┆ United Republic of Tanzania ┆ Africa │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┤
+│ Dar es Salaam ┆ United Republic of Tanzania ┆ Africa │
+└───────────────┴─────────────────────────────┴───────────┘
+```
+
+The code above is an example of a point-in-polygon join. The point represents
the city, and the polygon represents the area that represents the country.
SedonaDB can easily perform spatial joins with optimizations, such as
effectively utilizing spatial indices where required and adapting join
strategies at runtime based on samples of the input data.
+
+These types of spatial operations can be relatively slow for engines that are
not optimized for spatial data. SedonaDB is optimized for these spatial
computations.
+
+## Apache Sedona SpatialBench
+
+To test our work on SedonaDB, we also needed to develop a mechanism to
evaluate its performance and speed. As a result of this, and other motivations,
we also created Apache Sedona SpatialBench, which is a new standard for
measuring the performance of spatial data processing engines and databases. We
will discuss the benefits and capabilities of SpatialBench in more detail in
our next blog.
+
+## Single-node spatial benchmarks
+
+Let’s compare the performance of SedonaDB vs. GeoPandas and DuckDB Spatial for
some representative spatial queries as defined in
[SpatialBench](https://sedona.apache.org/spatialbench/). Here are the results
for Scale Factor 1 (SF 1):
+
+{ align=center
width="80%" }
+
+And here are the results for SF 10:
+
+{ align=center
width="80%" }
+
+Here’s an example of the SpatialBench query #8 SQL code that works for
SedonaDB and DuckDB:
+
+```sql
+SELECT b.b_buildingkey, b.b_name, COUNT(*) AS nearby_pickup_count
+FROM trip t JOIN building b ON ST_DWithin(ST_GeomFromWKB(t.t_pickuploc),
ST_GeomFromWKB(b.b_boundary), 0.0045) -- ~500m
+GROUP BY b.b_buildingkey, b.b_name
+ORDER BY nearby_pickup_count DESC
+```
+
+Here’s what the query returns:
+
+```
+┌───────────────┬──────────┬─────────────────────┐
+│ b_buildingkey ┆ b_name ┆ nearby_pickup_count │
+│ int64 ┆ utf8view ┆ int64 │
+╞═══════════════╪══════════╪═════════════════════╡
+│ 3779 ┆ linen ┆ 42 │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ 19135 ┆ misty ┆ 36 │
+├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ 4416 ┆ sienna ┆ 26 │
+└───────────────┴──────────┴─────────────────────┘
+```
+
+Here’s the equivalent GeoPandas code:
+
+```python
+trips_df = pd.read_parquet(data_paths["trip"])
+trips_df["pickup_geom"] = gpd.GeoSeries.from_wkb(
+ trips_df["t_pickuploc"], crs="EPSG:4326"
+)
+pickups_gdf = gpd.GeoDataFrame(trips_df, geometry="pickup_geom",
crs="EPSG:4326")
+
+buildings_df = pd.read_parquet(data_paths["building"])
+buildings_df["boundary_geom"] = gpd.GeoSeries.from_wkb(
+ buildings_df["b_boundary"], crs="EPSG:4326"
+)
+buildings_gdf = gpd.GeoDataFrame(
+ buildings_df, geometry="boundary_geom", crs="EPSG:4326"
+)
+
+threshold = 0.0045 # degrees (~500m)
+result = (
+ buildings_gdf.sjoin(pickups_gdf, predicate="dwithin", distance=threshold)
+ .groupby(["b_buildingkey", "b_name"], as_index=False)
+ .size()
+ .rename(columns={"size": "nearby_pickup_count"})
+ .sort_values(["nearby_pickup_count", "b_buildingkey"], ascending=[False,
True])
+ .reset_index(drop=True)
+)
+```
+
+SedonaDB optimizes query execution behind the scenes. GeoPandas requires more
manual optimizations for better performance. If you have experience tuning
GeoPandas code and would like to help optimize it, please comment on [this
issue](https://github.com/apache/sedona-spatialbench).
+
+DuckDB Spatial offers optimized spatial join capabilities, enabling the quick
execution of some queries, but it errors out for other queries. We encourage
the DuckDB community to investigate these issues.
+
+## SedonaDB CRS management
+
+SedonaDB manages the CRS when reading/writing files, as well as in DataFrames,
making your pipelines safer and saving you from manual work.
+
+Let’s compute the number of buildings in the state of Vermont to highlight the
CRS management features embedded in SedonaDB.
+
+Start by reading in a FlatGeobuf file that uses the EPSG 32618 CRS with
GeoPandas and then convert it to a SedonaDB DataFrame:
+
+```python
+import geopandas as gpd
+
+path =
"https://raw.githubusercontent.com/geoarrow/geoarrow-data/v0.2.0/example-crs/files/example-crs_vermont-utm.fgb";
+gdf = gpd.read_file(path)
+vermont = sd.create_data_frame(gdf)
+```
+
+Let’s check the schema of the `vermont` DataFrame:
+
+```
+vermont.schema
+
+SedonaSchema with 1 field:
+ geometry: wkb <epsg:32618>
+```
+
+We can see that the `vermont` DataFrame maintains the CRS that’s specified in
the FlatGeobuf file. SedonaDB doesn’t have a native FlatGeobuf reader yet, but
it’s easy to use the GeoPandas FlatGeobuf reader and then convert it to a
SedonaDB DataFrame with a single line of code.
+
+Now read a GeoParquet file into a SedonaDB DataFrame.
+
+```python
+buildings = sd.read_parquet(
+
"https://github.com/geoarrow/geoarrow-data/releases/download/v0.2.0/microsoft-buildings_point_geo.parquet";
+)
+```
+
+Check the schema of the DataFrame:
+
+```
+buildings.schema
+
+SedonaSchema with 1 field:
+ geometry: geometry <ogc:crs84>
+```
+
+Let’s expose these two tables as views and run a spatial join to see how many
buildings are in Vermont:
+
+```python
+buildings.to_view("buildings", overwrite=True)
+vermont.to_view("vermont", overwrite=True)
+
+sd.sql(
+ """
+select count(*) from buildings
+join vermont
+where ST_Intersects(buildings.geometry, vermont.geometry)
+"""
+).show()
+```
+
+This command correctly errors out because the tables have different CRSs. For
safety, SedonaDB errors out rather than give you the wrong answer! Here’s the
error message that’s easy to debug:
+
+```
+SedonaError: type_coercion
+caused by
+Error during planning: Mismatched CRS arguments: ogc:crs84 vs epsg:32618
+Use ST_Transform() or ST_SetSRID() to ensure arguments are compatible.
+```
+
+Let’s rewrite the spatial join to convert the `vermont` CRS to EPSG:4326, so
it’s compatible with the `buildings` CRS.
+
+```python
+sd.sql(
+ """
+select count(*) from buildings
+join vermont
+where ST_Intersects(buildings.geometry, ST_Transform(vermont.geometry,
'EPSG:4326'))
+"""
+).show()
+```
+
+We now get the correct result:
+
+```
+┌──────────┐
+│ count(*) │
+│ int64 │
+╞══════════╡
+│ 361856 │
+└──────────┘
+```
+
+SedonaDB tracks the CRS when reading/writing files, converting to/from
GeoPandas DataFrames, or when performing DataFrame operations, so your spatial
computations run safely and correctly!
+
+## Realistic example with SedonaDB
+
+Let’s now turn our attention to a KNN join, which is a more complex spatial
operation.
+
+Suppose you’re analyzing ride-sharing data and want to identify which
buildings are most commonly near pickup points, helping understand the
relationship between trip origins and nearby landmarks, businesses, or
residential structures that might influence ride demand patterns.
+
+This query finds the five closest buildings to each trip pickup location using
spatial nearest neighbor analysis. For every trip, it identifies the five
buildings that are geographically closest to where the passenger was picked up
and calculates the exact distance to each of those buildings.
+
+Here’s the query:
+
+```sql
+WITH trip_with_geom AS (
+ SELECT t_tripkey, t_pickuploc, ST_GeomFromWKB(t_pickuploc) as pickup_geom
+ FROM trip
+),
+building_with_geom AS (
+ SELECT b_buildingkey, b_name, b_boundary, ST_GeomFromWKB(b_boundary) as
boundary_geom
+ FROM building
+)
+SELECT
+ t.t_tripkey,
+ t.t_pickuploc,
+ b.b_buildingkey,
+ b.b_name AS building_name,
+ ST_Distance(t.pickup_geom, b.boundary_geom) AS distance_to_building
+FROM trip_with_geom t JOIN building_with_geom b
+ON ST_KNN(t.pickup_geom, b.boundary_geom, 5, FALSE)
+ORDER BY distance_to_building ASC, b.b_buildingkey ASC
+```
+
+Here are the results of the query:
+
+```
+┌───────────┬───────────────────────────────┬───────────────┬───────────────┬──────────────────────┐
+│ t_tripkey ┆ t_pickuploc ┆ b_buildingkey ┆ building_name ┆
distance_to_building │
+│ int64 ┆ binary ┆ int64 ┆ utf8 ┆
float64 │
+╞═══════════╪═══════════════════════════════╪═══════════════╪═══════════════╪══════════════════════╡
+│ 5854027 ┆ 01010000001afa27b85825504001… ┆ 79 ┆ gainsboro ┆
0.0 │
+├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ 3326828 ┆ 01010000001bfcc5b8b7a95d4083… ┆ 466 ┆ deep ┆
0.0 │
+├╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
+│ 1239844 ┆ 0101000000ce471770d6ce2a40f9… ┆ 618 ┆ ivory ┆
0.0 │
+└───────────┴───────────────────────────────┴───────────────┴───────────────┴──────────────────────┘
+```
+
+This is one of the queries from
[SpatialBench](https://github.com/apache/sedona-spatialbench/).
+
+## Why SedonaDB was built in Rust
+
+SedonaDB is built in Rust to leverage performance, fine-grained memory
management capabilities, and an expansive ecosystem of data libraries.
+
+Rust is a high-performance, memory-safe programming language.
+
+The Rust data ecosystem is mature, and SedonaDB leverages Rust libraries like
[Apache DataFusion](https://github.com/apache/datafusion),
[GeoArrow](https://github.com/geoarrow/geoarrow), and
[georust/geo](https://github.com/georust/geo).
+
+Whereas Spark, as a database engine, exposes several extension points that
allow Sedona Spark to optimize spatial queries in a distributed setting, on a
single node, DataFusion exposes stable and well-documented APIs for pruning,
specialized physical operators like spatial joins, and optimizer rules at the
logical and execution levels. This allowed us to add deep spatial awareness
throughout the engine while maintaining non-spatial type and function coverage
that users need and expect. N [...]
+
+## Why SedonaDB and SedonaSpark are Both Needed
+
+SedonaSpark is an excellent option for users who have large geospatial
datasets or an existing production Spark runtime. For example, if you have a
large 4-terabyte vector dataset and want to join it with a large raster
dataset, then SedonaSpark is clearly the best option.
+
+But Spark has limitations for smaller datasets. It is slow to run locally,
cumbersome to install, and challenging to tune.
+
+SedonaDB is better for smaller datasets and when running computations locally.
The SedonaDB spatial functions are compatible with the SparkSedona functions,
so SQL chunks that work for one engine will usually work for the other. Over
time, we will ensure that both project APIs are fully interoperable. Here’s an
example of a chunk to analyze the Overture buildings table that works for both
engines.
+
+```
+nyc_bbox_wkt = (
+ "POLYGON((-74.2591 40.4774, -74.2591 40.9176, -73.7004 40.9176, -73.7004
40.4774, -74.2591 40.4774))"
+)
+
+sd.sql(f"""
+SELECT
+ id,
+ height,
+ num_floors,
+ roof_shape,
+ ST_Centroid(geometry) as centroid
+FROM
+ buildings
+WHERE
+ is_underground = FALSE
+ AND height IS NOT NULL
+ AND height > 20
+ AND ST_Intersects(geometry, ST_SetSRID(ST_GeomFromText('{nyc_bbox_wkt}'),
4326))
+LIMIT 5;
+```
+
+## Next steps
+
+While SedonaDB is well-tested and provides a core set of features that can
perform numerous spatial analyses, it remains an early-stage project with
multiple opportunities for new features.
+
+Many more ST functions are required. Some are relatively straightforward, but
others are complex.
+
+The community will add built-in support for other spatial file formats, such
as GeoPackage and GeoJSON, to SedonaDB. You can read data in these formats
into GeoPandas DataFrames and convert them to SedonaDB DataFrames in the
meantime.
+
+Raster support is also on the roadmap, which is a complex undertaking, so it’s
an excellent opportunity to contribute if you’re interested in solving
challenging problems with Rust.
+
+Refer to the [SedonaDB v0.2
milestone](https://github.com/apache/sedona-db/milestone/1) for more details on
the specific tasks outlined for the next release. Additionally, feel free to
create issues, comment on the Discord, or start GitHub discussions to
brainstorm new features.
+
+## Join the community
+
+The Apache Sedona community has an active Discord community, monthly user
meetings, and regular contributor meetings.
+
+SedonaDB has a [well-defined
roadmap](https://github.com/apache/sedona-db/milestones) and welcomes
contributions from the community. Feel free to request to take ownership of an
issue, and we will be happy to assign it to you. You’re also welcome to join
the contributor meetings, and the other active contributors will be glad to
help you get your pull request over the finish line!
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