Phones are constantly tracking the location of a user in space. Devices like cars, smart watches, and drones are also picking up high volumes of location data. This location data is also called “geospatial data.”
The amount of geospatial data is rapidly increasing, and there is a growing demand for software to perform operations over that data. Geospatial data sets are often massive–so it is non-trivial to perform operations over this data.
Geospatial data can consist of something as simple as a set of latitude/longitude data points. A single lat/long coordinate pair can be enriched with information about what ZIP code it is in, how far that data point is from the other data points in the set, and where the nearest coffee shop is in relation to that data point.
Ram Sriharsha created Magellan, a geospatial analytics library for Spark. In today’s show, Ram describes the set of problems within the domain of geospatial analytics engineering. Ram also works as a product manager for Apache Spark at Databricks.
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Azure Container Service simplifies the deployment, management and operations of Kubernetes. You can continue to work with the tools you already know, such as Helm, and move applications to any Kubernetes deployment. Integrate with your choice of container registry, including Azure Container Registry. Also, quickly and efficiently scale to maximize your resource utilization without having to take your applications offline. Isolate your application from infrastructure failures and transparently scale the underlying infrastructure to meet growing demands—all while increasing the security, reliability, and availability of critical business workloads with Azure. Check out the Azure Container Service at aka.ms/sedaily.
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