Category: AWS Snowcone

Video feeds and still images from judiciary locations are considered critical forms of evidence in the court of law. These locations can be police stations and government offices or even civil locations of importance like banks and hospitals. As governments, particularly in smart cities rely upon video surveillance, it is critical to design a cost […]

This is by far my favorite post that I write every year, it’s the re:Invent preview for AWS Storage! On behalf of the entire AWS Storage team, I am excited to tell you some of the highlights of the storage category that you shouldn’t miss. AWS re:Invent is a learning conference hosted for the global […]

Customers often choose to move large amounts of on-premises data to Amazon S3 Glacier. These customers choose to use S3 Glacier for its ability to reliably and securely store any amount of data at a cost-effective price, while being designed for 11 9’s of data durability. This makes S3 Glacier a popular choice for long-term […]

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 […]

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 […]

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 […]

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 […]

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 […]

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. […]

At AWS re:Invent 2020-2021, Ramesh Kumar, Senior Manager of Product Management for the AWS Snow Family, presented a session on “Optimizing data migrations with AWS Snowcone and AWS Snowball Edge“. That session, now on-demand, shed some light on solutions, services, and considerations for optimizing your data migration to AWS with those devices. Ramesh also discussed […]