AWS Greengrass

Name: AWS Greengrass
Brand: AWS
Availability: InStock
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AWS Greengrass is software designed to let users run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way.

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SoftwareIot Development ToolsIoT Development Tools IoT Edge Platforms

AWS Greengrass Introduction

What is AWS Greengrass?

AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with intermittent connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud. ML Inference is a feature of AWS Greengrass that makes it easy to perform machine learning inference locally on Greengrass Core devices using models that are built and trained in the cloud. AWS Greengrass seamlessly extends AWS to devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With Greengrass, you can use familiar languages and programming models to create and test your device software in the cloud, and then deploy it to your devices. AWS Greengrass can be programmed to filter device data and only transmit necessary information back to the cloud. AWS Greengrass authenticates and encrypts device data at all points of connection using the security and access management capabilities of AWS IoT Core. This way, data is never exchanged between devices when they communicate with each other and the cloud, without proven identity.

How to use AWS Greengrass?

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Why Choose AWS Greengrass?

Choosing this is smart if you want your IoT devices to run locally with secure compute, messaging, and ML inference, even offline. It helps reduce cloud costs and latency while keeping your device data safe and synced.