AWS Storage Blog

Category: AWS Snowball

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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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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re:Invent

AWS re:Invent recap: Optimize your data migration with AWS Snow Family

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 […]

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Data migration, edge, and hybrid sessions social image reinvent 2020

AWS re:Invent 2020-2021: data transfer, hybrid cloud, and edge computing sessions

The biggest cloud computing event of the year has begun! As you’re most likely aware of by now, re:Invent 2020-2021 is a FREE, 3-week virtual conference taking place from Nov 30 – Dec 18 and Jan 12 – 14. We wanted to make sure we ended 2020 offering you a ton of educational AWS content […]

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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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Amazon S3

Migrating and managing large datasets on Amazon S3 (Part 1)

UPDATE: The second post in this two-part series was published on October 29th, 2020. This is the first of a two-post series intended for customers migrating and managing large datasets on Amazon S3. This post addresses moving your data to S3 and choosing the optimal storage class for your data. The second follow-on post (coming […]

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Comparing your on-premises storage patterns with AWS Storage services

Many companies want to move to the cloud, and for most of those companies, moving to the cloud starts with an assessment of existing storage infrastructure. It is useful to know how to map existing storage with AWS Storage options to assess the existing storage infrastructure effectively. Companies that take the time to identify where […]

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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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AWS Identity and Access Management on AWS Snowball Edge

Many of our customers use AWS Snowball Edge devices for secure data transfer and edge computing applications. Recently, AWS announced support for AWS Identity and Access Management (IAM) on Snowball Edge. Before the introduction of IAM on Snowball Edge, IT administrators shared a single access key/secret key combination with all the users who wanted to […]

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Building a Linux edge-computing solution with AWS Snowball Edge and Amazon EC2

There are many situations where you must run data inference close to the data sources. Often times these are located at remote, disconnected locations. Let’s consider the following examples: A remote oil drilling platform has numerous sensors that generate data. Critical components have to be monitored for wear and tear, or failure, and replacements must […]

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