Amazon Web Services, Inc. has announced Spatial Data Management on AWS, a cloud-native service that helps organizations manage, enrich, and interconnect spatial data at scale. With the launch of SDMA, AWS is making it easier for customers to store their multimodal spatial datasets comprising 3D models, geospatial records, behavioral logs, and temporal assets in a secure, scalable cloud environment.
SDMA acts as a single point of control, enabling enterprises to easily integrate their spatial data into ISV SaaS applications and AWS services. Users are able to organize and enhance spatial datasets in a coherent manner thanks to collection rules built into the service. This leads to better data governance and greater transparency throughout operational workflows. It supports REST APIs, a desktop application, and a web portal-all of which enable users of every level of technical knowledge to rationalize activities around spatial data and drive faster data-informed decision-making.
The main features of SDMA are designed to meet the challenges of managing spatial data by:
- Centralize data in a secure, highly available cloud repository, ensuring easier access and collaboration across teams and applications.
- Automating metadata extraction for formats such as .LAZ, .E57, .GLB, and .GLTF will make spatial data easier to find and relate to.
- Simplify integrations with external systems by providing configurable connectors and APIs that eliminate manual file handling and improve interoperability across cloud and on-premise environments.
- Intuitive interfaces that create auto-preview files, hence making the workflows faster and allowing users to validate data without needing to download huge files.
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It is now generally available across key AWS Global Regions, including Asia Pacific-Tokyo, Singapore, and Sydney; Europe-Frankfurt, Ireland, and London; and US East and West-N. Virginia, Ohio, and Oregon.
The solution for AWS Spatial Data Management helps enable emerging use cases across industries in logistics, smart infrastructure, digital twins, and physical operations optimization by streamlining how enterprises store, govern, and derive insights from spatial data. It is positioned to become the backbone for organizations seeking to implement spatial data workflows with better security, visibility, and collaborative power.
