Skip to content

A cluster computing framework for processing large-scale geospatial data

License

Notifications You must be signed in to change notification settings

vinooganesh/incubator-sedona

 
 

Repository files navigation

Scala and Java build Python build R build Example project build Docs build

Click Binder and play the interactive Sedona Python Jupyter Notebook immediately!

Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona equips cluster computing systems such as Apache Spark and Apache Flink with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines.

Download statistics Maven PyPI CRAN
Apache Sedona 180k/month Downloads Downloads
Archived GeoSpark releases 10k/month DownloadsDownloads

System architecture

Our users and code contributors are from ...

Modules in the source code

Name API Introduction
Core Scala/Java Distributed Spatial Datasets and Query Operators
SQL Spark RDD/DataFrame in Scala/Java/SQL Geospatial data processing on Apache Spark
Flink Flink DataStream/Table in Scala/Java/SQL Geospatial data processing on Apache Flink
Viz Spark RDD/DataFrame in Scala/Java/SQL Geospatial data visualization on Apache Spark
Python Spark RDD/DataFrame in Python Python wrapper for Sedona
R Spark RDD/DataFrame in R R wrapper for Sedona
Zeppelin Apache Zeppelin Plugin for Apache Zeppelin 0.8.1+

Sedona supports several programming languages: Scala, Java, SQL, Python and R.

Compile the source code

Please refer to Sedona website

Contact

Twitter: Sedona@Twitter

Sedona JIRA: Bugs, Pull Requests, and other similar issues

Sedona Mailing Lists: dev@sedona.apache.org: project development, general questions or tutorials.

Please visit Apache Sedona website for detailed information

Powered by

About

A cluster computing framework for processing large-scale geospatial data

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Java 38.1%
  • Scala 28.1%
  • Python 17.7%
  • Jupyter Notebook 9.5%
  • R 4.3%
  • C 1.9%
  • Other 0.4%