AWS Snowball Edge Compute Optimised offering comes to Singapore
- 06 April, 2021 08:23
Amazon Web Services’ (AWS) Snowball Edge Compute Optimised offering is now available in the cloud vendor’s Asia Pacific (Singapore) region.
AWS Snowball, a part of the AWS Snow Family – suite of services designed for users that need to run operations in non-data centre environments and locations where there is a lack of consistent network connectivity – is an edge computing, data migration and edge storage offering.
Snowball Edge Compute Optimised is described by AWS as a secure, rugged device that brings AWS computing and storage capabilities, such as Amazon EC2, Amazon EBS, Amazon S3, AWS IoT Greengrass, AWS Lambda functions, and AWS IAM to edge environments for machine learning, data analytics, processing, and local storage.
Users can employ Snowball Edge devices in environments with intermittent connectivity, such as manufacturing, industrial and transportation, or in extremely remote locations, like military or maritime operations.
These devices may also be rack mounted and clustered together to build larger installations, the company said.
“The Snowball Edge Compute Optimised device provides 52 vCPUs, 208 gibibyte (GiB) of memory, and an optional NVIDIA Tesla V100 GPU,” AWS said in a blog post. “For storage, the device provides 42 terabytes (TB) usable hard disk drive (HDD) capacity for S3 compatible object storage or EBS compatible block volumes, as well as 7.68 TB of usable NVMe solid state drive (SSD) capacity for EBS compatible block volumes.
“Snowball Edge Compute Optimised devices run Amazon EC2 sbe-c and sbe-g instances, which are equivalent to C5, M5a, G3, and P3 instances,” it added.
AWS Control Tower is used to set up and govern a secure, multi-account AWS environment based on best practices established through AWS’ experience working with thousands of enterprises as they move to the cloud.
Glue DataBrew is designed to make it easy for data analysts and data scientists to clean and normalise data for analytics and machine learning.