AWS Snowcone
Amazon Web Services’ (AWS) Snowcone offering is now available for customer orders in the AWS Asia Pacific (Singapore) and AWS Asia Pacific (Tokyo) Regions.
With this launch, the cloud giant said in a blog post, Snowcone is now available for order in AWS Asia Pacific (Singapore), Asia Pacific (Tokyo), Canada (Central), EU (Frankfurt), EU (Ireland), US East (N. Virginia), US West (Oregon) Regions and Asia Pacific (Sydney), where it was launched in March.
AWS Snowcone is the smallest member of the AWS Snow family of edge computing, edge storage and data transfer devices.
The AWS Snow line of appliances represents just one flavour of the cloud giant’s edge infrastructure offering. AWS Snowcone is the smallest of the Snow product line's appliances, claiming two vCPUs and 4GB. It is primarily used for edge data storage and transfer.
The rationale behind AWS Snowcone is that edge locations often lack the space, power and cooling needed for data center IT equipment to run applications.
With its two CPUs and 8TB of usable storage as well as wired networking, Snowcone runs edge computing workloads with select Amazon EC2 instances or AWS IoT (internet of things) Greengrass.
The Snowcone product is designed to be portable, rugged and secure – light enough to fit in a backpack, and able to withstand harsh environments. It’s also small – approximately nine inches by six inches by three inches, weighing 4.5 lbs., and supporting operation via battery for mobility.
AWS customers largely use Snowcone to deploy applications at the edge and to collect data, process it locally and move it to AWS either offline – which can be done by shipping the device to AWS – or online, by using AWS DataSync on Snowcone to send the data to AWS over the network.
To setup and manage Snowcone, users can employ AWS OpsHub, a graphical user interface that enables the rapid deployment of edge computing workloads and the simplification of data migration to the cloud.
According to AWS, DataSync comes pre-installed on the device to move data online to and from Amazon S3, Amazon EFS or Amazon FSx for Windows File Server, as well as between AWS Storage services.
The local release comes several months after the cloud vendor made its data warehouse product Redshift ML generally available, including in Asia Pacific.
Amazon Redshift ML enables developers to create, train and deploy machine learning (ML) models using SQL commands. The move allows users to “leverage” Amazon SageMaker, a managed ML service, without moving data or learning a new skill, AWS claimed.
Writing in a blog post, the cloud giant said Amazon Redshift ML automatically discovers the best model and tunes it based on training data using Amazon SageMaker Autopilot. This chooses between regression, binary or multi-class classification models.
In May, AWS launched its machine learning (ML) service Amazon DevOps Guru, which can detect operational issues and recommend actions for remediation, into general availability.
