AWS Storage Blog

Category: Edge

Autonomous vehicle data collection with AWS Snowcone and AWS IoT Greengrass

Self-driving and self-flying vehicles — autonomous cars, airplanes, and drones — require vast amounts of data to fulfill their promise of a safe mode of transportation for goods and people. Connected vehicles and the Internet of Things (IoT) have a strong influence on the way we collect and process low-bandwidth telemetry data, in addition to […]

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Making it even simpler to create and manage your AWS Snow Family jobs

Customers use AWS Snow Family devices to run storage, compute, and data-processing operations in austere environments with inconsistent (or no) network connectivity. The AWS Snow Family, comprised of AWS Snowcone and AWS Snowball, offers a number of physical devices and capacity points, most with built-in computing capabilities. These devices help physically transport up to exabytes […]

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Implementing sensor workflows using AWS Snowcone and AWS IoT Greengrass

In our first blog of this series, we covered using an IoT device to store the sensor data on an Amazon EC2 instance running on AWS Snowcone. That use case covered collecting data from sensors in locations such as a factory floor or a mine with austere network connectivity. There are other instances where you […]

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Video transcoding at the edge with AWS Snowcone

A customer doing video analysis in remote locations has the following problem: they must capture high-resolution video in the field and then transfer that data to a durable, highly available data store in the cloud for long-term storage. They also want to keep copies of video files in the remote location so that they can […]

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re:Invent

AWS re:Invent recap: Edge computing innovation with the AWS Snow Family

Ramesh Kumar, Senior Product Manager of the AWS Snow Family, just broadcasted his AWS re:Invent 2020-2021 session yesterday: “Edge computing innovation with AWS Snowcone and AWS Snowball Edge.” You can now watch Ramesh’s 30-minute edge session on-demand. In this blog, I discuss highlights from Ramesh’s session, including an overview of AWS services from the cloud […]

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re:Invent

Running Kubernetes cluster with Amazon EKS Distro across AWS Snowball Edge

AWS Snowball Edge customers are running applications for edge local data processing, analysis, and machine learning using Amazon EC2 compute instances on Snowball Edge devices in remote or disconnected locations. Customers use Snowball Edge devices in locations including, but not limited to, cruise ships, oil rigs, and factory floors with no or limited network connectivity. […]

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Best practices for accelerating data migrations using AWS Snowball Edge

Customers frequently perform bulk migrations of their application data when moving to the cloud. There are different online and offline methods for moving your data to the cloud. When proceeding with a data migration, data owners must consider the amount of data, transfer time, frequency, bandwidth, network costs, and security concerns. No matter how data […]

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Building an IoT solution at the edge with AWS Snowcone

UPDATE: The second blog post in this two-post series was published on January 5, 2020. Internet of Things (IoT) applications, like other applications, require edge solutions to operate in austere conditions with limited network connectivity or limited infrastructure. IoT applications at the edge can span numerous uses, like automation, optimization, and intelligent manufacturing to name […]

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Deluxe enables same day global theatrical delivery using AWS Snowcone

Deluxe Entertainment Services Inc. (Deluxe) is a world-leading video creation and distribution company offering global, end-to-end services and technology for the media and entertainment market. The largest content creators, broadcasters, OTTs, and distributors around the globe rely on Deluxe’s scale, technology, and capabilities to enable the worldwide market for premium content. Deluxe’s latest innovation for […]

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Deploying a LAMP-based multi-tier web application on AWS Snowball Edge

Imagine you are building an application at a remote location where you must process and make decisions based on locally generated data, say from surveillance cameras or from detection systems. If there is high network latency, you cannot run these applications in the cloud and process data in real time. Other times, you may be […]

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