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new 880b7cd9 [DOCS] update: documentation typos (#839)
880b7cd9 is described below
commit 880b7cd94855e0d9ebab5fcb15780a4161410db0
Author: Tejesh Reddy <[email protected]>
AuthorDate: Tue May 23 16:29:14 2023 -0700
[DOCS] update: documentation typos (#839)
---
README.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 36804deb..dfadfd50 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@
## What is Apache Sedona?
-Apache Sedona™ is a spatial computing engine that enables developers to easily
process spatial data at any scale within modern cluster computing systems such
as Apache Spark and Apache Flink. Sedona developers can express their spatial
data processing tasks in Spatial SQL, Spatial Python or Spatial R. Internally,
Sedona provides spaital data loading, indexing, partitioning, and query
processing/optimization functionality that enable users to efficiently analyze
spatial data at any scale.
+Apache Sedona™ is a spatial computing engine that enables developers to easily
process spatial data at any scale within modern cluster computing systems such
as Apache Spark and Apache Flink. Sedona developers can express their spatial
data processing tasks in Spatial SQL, Spatial Python or Spatial R. Internally,
Sedona provides spatial data loading, indexing, partitioning, and query
processing/optimization functionality that enable users to efficiently analyze
spatial data at any scale.
<img src="docs/image/sedona-ecosystem.png" width="800" class="center">
@@ -25,8 +25,8 @@ Some of the key features of Apache Sedona include:
* Scalable distributed processing of large vector and raster datasets.
* Tools for spatial indexing, spatial querying, and spatial join operations.
* Integration with popular geospatial python tools such as GeoPandas.
-* Integration with popular big data tools, such as Spark, Hadopp, Hive, and
Flink for data storage and querying.
-* A user-friendly API for working with geospatial data in the SQL, Python
Scala and Java languages.
+* Integration with popular big data tools, such as Spark, Hadoop, Hive, and
Flink for data storage and querying.
+* A user-friendly API for working with geospatial data in the SQL, Python,
Scala and Java languages.
* Flexible deployment options, including standalone, local, and cluster modes.
These are some of the key features of Apache Sedona, but it may offer
additional capabilities depending on the specific version and configuration.
