Getting Started With The Apache Sedona Docker Image

Описание

Apache Sedona is an open-source framework for working with large scale geospatial data. It adds spatial functionality to distributed data processing frameworks like Apache Spark and Apache Flink to enable developers and data scientists to work with spatial data at scale. Apache Sedona exposes native types for representing complex geometries like points, lines, polygons and implements geospatial indexing and partitioning for fast lookups and efficient distributed processing of spatial data at scale. Geospatial querying functionality is available with Spatial SQL by implementing the SQL-MM3 and OGC SQL standards. We can work with Apache Sedona via Python, R, Spatial SQL, and other tooling - such as a Jupyter Notebook environments and via seamless integration with the PyData ecosystem.

There are many ways to leverage Apache Sedona whether incorporating into an existing data pipeline or building a new greenfield analytics application. For example Apache Sedona can be deployed into a Databricks cluster, run in AWS EMR, it works with Snowflake, or can be run on your own infrastructure. In this video we'll be using the Apache Sedona Docker image to get a cluster running locally and perform some basic geoprocessing tasks.

Resources:
* Apache Sedona: https://sedona.apache.org
* Apache Sedona docker image: https://hub.docker.com/r/apache/sedona
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