All Things Distributed

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All Things Distributed
Werner Vogels’ weblog on building scalable and robust distributed systems.

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Edge devices, like gateways or cameras, can act locally on the data they generate, while still using the cloud for management, analytics, durable storage, and more.
Edge devices are critical when it comes to accessing data located in remote locations.
To provide uninterrupted service, home monitoring devices need low latency and the ability to compute data locally to notify residents quickly of an intrusion or leak.
About two years ago, we launched AWS IoT Greengrass, software that lets you run local compute, messaging, and storage for connected devices in a secure way.
With AWS IoT Greengrass, connected devices can run AWS Lambda functions, execute predictions based on ML models, keep device data in sync, and communicate with other devices securely—even when not connected to the internet.
AWS IoT Greengrass runs on devices, such as edge gateways or cameras, that collect data, filter and analyze it before sending it to the cloud, and act in near real time.
AWS IoT Greengrass devices can connect to these smaller devices, collect data, and enable them to act locally as well.
Amazon FreeRTOS makes small, low-power edge devices easy to program, deploy, secure, connect, and manage.
One of the most exciting advancements in IoT and edge computing is the ability to run ML inference locally on devices.
Local inference allows the robot to make autonomous decisions in near-real time, even without a connection to the cloud.
Because AWS RoboMaker is built on AWS IoT Greengrass, your robot can act locally on generated data, while still using the cloud for management, analytics, and durable storage.
Three years ago, we announced AWS Snowball Edge, an edge-computing device to support independent local workloads in remote locations, as well as for data migration.
You can develop and test in AWS, then deploy applications on devices in remote locations to collect, pre-process, and return the data

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