PluralSight – Querying Geospatial Data from MongoDB

PluralSight – Querying Geospatial Data from MongoDB-BOOKWARE-KNiSO
English | Size: 114.82 MB
Category: Tutorial


Release Notes: Analyzing spatial data doesn’t have to be a difficult task. In this course Querying Geospatial Data from MongoDB, you ll learn to execute geospatial queries in MongoDB. First, you ll explore the concepts needed to understand the geospatial features of MongoDB. Next, you ll discover how to store geospatial data in GeoJSON format. Finally, you ll learn how to find places within a certain area and close to a point. When you re finished with this course, you ll have the skills and knowledge of MongoDB and geospatial data needed to execute geospatial queries efficiently

Packt – Learning the FOSS4G Stack Python for Geospatial

Packt – Learning the FOSS4G Stack Python for Geospatial-XQZT
English | Size: 822.92 MB
Category: Tutorial

If you work in the field of GIS, you’ve probably heard everyone talking about Python, whether it’s Arcpy in ArcGIS or special Python packages for geocoding. In this course, you’ll learn how to write Python code to perform spatial analysis. The course focuses primarily on integrating different spatial libraries within your Python code. With the help of videos, you’ll see how you can solve spatial problems by blending Python code with various packages.

How NASA Is Building a Petabyte-Scale Geospatial Archive in the Cloud

How NASA Is Building a Petabyte-Scale Geospatial Archive in the Cloud
English | Size: 1.41 GB
Category: Tutorial

EOSDIS is working toward a vision of a cloud-based, highly flexible system to meet its ever-growing and evolving data demands. Cumulus, a free and open source framework, supports this vision via configurable workflows to ingest, process, archive, manage, and distribute NASA’s Earth imagery. The Cumulus infrastructure is designed for scalability and reliability, using much of the AWS serverless platform, which enables Cumulus to scale in real time to be performant under the largest expected workloads.