December 2025
Intermediate to advanced
338 pages
8h 38m
English
Content preview from Cloud Native Geospatial Analytics with Apache Sedona
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Foreword
Geospatial data has become central to our understanding and response to the world around us. From monitoring ecosystems to precise map matching, location is often the key to unlocking insight. However, as the volume and velocity of geospatial data have surged, our analytical tools have struggled to keep pace. Traditional GIS tools excel at analysis but are often limited to single machine environments. Meanwhile, cloud data warehouses offer impressive scalability but often treat geospatial data as an afterthought.
Apache Sedona bridges this divide. Sedona is an open source framework that embeds geospatial analysis directly into distributed computing platforms such as Apache Spark, Flink, and Snowflake. It treats spatial as a first-class concern, enabling complex spatial joins, queries, and raster processing across billions of records. With Sedona, we gain both the depth of geospatial science coupled with the elasticity of the cloud.
I introduced Sedona briefly in my previous book, Introduction to GIS Programming: A Practical Python Guide to Open Source Geospatial Tools, where I included a chapter on distributed computing with Apache Sedona. That chapter sparked strong interest among readers, but it could only scratch the surface. Sedona is far too powerful and comprehensive to be condensed into a single section. It deserves a full-length treatment, and that is precisely what Cloud Native Geospatial Analytics with Apache Sedona provides.
This book is authored by Sedona’s ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.