9/3/2024, 12:00:00 AM ~ 9/4/2024, 12:00:00 AM (UTC)

Recent Announcements

AWS Fault Injection Service introduces additional safety control

AWS Fault Injection Service (FIS) now provides additional safety control with a safety lever that, when engaged, stops all running experiments and prevents new experiments from starting. Customers can now prevent fault injection during certain time periods, such as sales events or product launches, or in response to application health alarms.\n FIS has built-in safety guardrails, including “stop conditions” that automatically stop experiments and remove faults when alarms are triggered. Today we add another important guardrail, the “safety lever”. When engaged, a safety lever stops all experiments running in the account, including multi-account experiments. The safety lever will remain engaged until manually disengaged by the customer, such as when the application has returned to a healthy state or a planned peak event has concluded. Safety levers are generally available in all AWS Regions where FIS is available, including the AWS GovCloud (US) Regions, at no additional costs. To get started, visit the safety levers user guide.

AWS Glue now provides job queuing

Today, AWS adds job queuing for AWS Glue jobs. This new capability enables you to submit AWS Glue job runs without needing to manage account level quotas and limits.\n AWS Glue job queuing monitors your account level quotas and limits. If quotas or limits are insufficient to start a Glue job run, AWS Glue will automatically queue the job and wait for limits to free up. Once limits become available, AWS Glue will retry the job run. Glue jobs will queue for limits like max concurrent job runs per account, max concurrent Data Processing Units (DPU), and resource unavailable due to IP address exhaustion in Amazon Virtual Private Cloud (Amazon VPC). AWS Glue job queuing can be enabled on your jobs via the AWS Management console or API/CLI. AWS Glue job queuing is available in all AWS commercial regions where AWS Glue is generally available. To learn more, visit the AWS Glue product page, our documentation, or blog post.

Amazon EBS direct APIs now supports IPv6 in AWS PrivateLink

Amazon EBS direct APIs now support the Internet Protocol version 6 (IPv6) protocol when you connect your Virtual Private Cloud (VPC) to EBS Direct APIs using AWS PrivateLink. EBS direct APIs can help customers to simplify their backup and recovery workflows by directly creating and reading EBS snapshots via APIs. Through AWS PrivateLink, customers can access EBS direct APIs as if it were in your VPC. This change can support customers with their IPv6 compliance needs, integrate with existing IPv6-based on-premises applications, and remove the need for expensive networking equipment to handle the address translation between IPv4 and IPv6.\n To use this new capability, you can configure your applications to use the Amazon EBS direct API IPv6 endpoints, or dual-stack Amazon EBS direct APIs endpoints which support both IPv4 and IPv6. When you make a request to a dual-stack Amazon EBS direct APIs endpoint, the endpoint resolves to an IPv6 or an IPv4 address, depending on the protocol used by your network and client. You can access Amazon EBS direct APIs with the IPv6 protocol through AWS PrivateLink in all AWS Regions where EBS direct APIs are available. To learn more about EBS Direct APIs please visit our product page.

Introducing sagemaker-core: A New Object-Oriented SDK for Amazon SageMaker

Amazon SageMaker is excited to announce sagemaker-core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint resource classes. The resource chaining feature in sagemaker-core lets developers pass resource objects as parameters, eliminating the need to manually specify complex parameters. The SDK also abstracts low-level details like resource state transitions and polling logic. It achieves full parity with SageMaker APIs, allowing developers to leverage all SageMaker capabilities directly through the SDK. Additional key usability improvements include auto code completion in popular IDEs, comprehensive documentation, and type hints.\n The dedicated resource classes in sagemaker-core provide an intuitive object-oriented view of available functionalities, reducing cognitive load for developers and minimizing the need to manage complex parameter structures. Comprehensive documentation, and type hints help developers write code faster and with fewer errors without needing to navigate complex API documentation. By handling resource state management automatically, developers can focus on building and deploying machine learning models without getting bogged down by lower level resource monitoring tasks. When used with intelligent defaults, sagemaker-core alleviates the burden of repeatedly specifying common parameters. The combined effects of these features result in more readable and maintainable code along with increased developer productivity. To get started, check out our example notebooks and technical documentation. We’re excited to bring sagemaker-core to the SageMaker community and look forward to your contributions in making it even better.

Amazon DynamoDB announces support for Attribute-Based Access Control

Amazon DynamoDB now supports Attribute-Based Access Control (ABAC) for tables and indexes. ABAC is an authorization strategy that defines access permissions based on tags attached to users, roles, and AWS resources.\n With ABAC, you can now use your tags to configure access permissions and policies. Tag-based access conditions can be used to allow or deny specific actions, when AWS Identity and Access Management (IAM) principals’ tags match the tags on an Amazon DynamoDB table. With the flexibility of using tag-based conditions, you can now set more granular access permissions based on your organizational structures. ABAC allows you to scale your tag-based permissions to new employees and changing resource structures, without rewriting policies as organizations grow. ABAC is supported through the AWS Management Console, AWS API, AWS CLI, AWS SDK, and AWS CloudFormation. Attribute-Based Access Control for Amazon DynamoDB is now available in limited preview in the US East (Ohio), US East (Virginia), and US West (N. California) Regions. To request access to the limited preview, visit the preview page.

AWS announces session reuse with Amazon Redshift Data API

Today, Amazon Redshift launches session reuse feature in Data API that enables you to access data efficiently from Amazon Redshift data warehouses by eliminating the need to manage database drivers, connections, network configurations, data buffering, and more. Data API session reuse allows you to retain the context of a session from one query execution to another, which reduces connection setup latency on repeated queries to the same data warehouse.\n With session reuse, you can utilize session context on objects like variables or temporary tables, which you create once and use multiple times for various queries. This reduces the overall execution time for your queries. To reuse sessions, you must specify in seconds how long a session should be kept for after a query finishes and any subsequent queries can reference this session until the time expires, or it’s extended.

Amazon Connect now provides a weekly view of agent schedules

Amazon Connect now provides a weekly view of agent schedules, making it easier for contact center managers to get an at-a-glance view of staffing for an entire week. With this launch, you can now ensure there is required coverage each day via daily aggregated metrics including service level, occupancy, and forecasted versus scheduled hours. For example, from the weekly view you can easily identify if there is overstaffing on Wednesday and understaffing on Friday. You can then move agent shifts from Wednesday to Friday within the weekly view. Weekly view also makes it easy to verify that agents receive the appropriate shifts each day (e.g. each agent has an 8-hour shift) and that they are not working too many days in a row (e.g. each agent gets at least 2 days off every week). Weekly view improves manager productivity by reducing time spent on day to day management of agent shifts and makes it easier to review staffing for multiple days in a single view.\n This feature is available in all AWS Regions where Amazon Connect agent scheduling is available. To get started with Amazon Connect agent scheduling, click here.

Amazon Connect Contact Lens can now generate transcriptions in 10 new languages

Amazon Connect Contact Lens can now generate transcriptions in 10 new languages that include Catalan (Spain), Danish (Denmark), Dutch (Netherlands), Finnish (Finland), Indonesian (Indonesia), Malay (Malaysia), Norwegian Bokmål (Norway), Polish (Poland), Swedish (Sweden), and Tagalog/Filipino (Philippines). With this launch, Contact Lens conversational analytics now provides transcription support for 33 languages.\n Amazon Connect Contact Lens helps you to monitor, measure, and continuously improve contact quality and agent performance for a better overall customer experience. With Contact Lens conversational analytics, you can transcribe customer calls, analyze customer sentiment, discover top contact drivers, help redact sensitive data, and more, all natively within Amazon Connect. Transcription support for these 10 new languages is available for post-call scenarios and across all regions where Contact Lens conversational analytics is available. To learn more, please visit our documentation and our webpage. This feature is included with Contact Lens conversational analytics at no additional charge. For information about Contact Lens pricing, please visit our pricing page.

AI recommendations for descriptions in Amazon DataZone expanded to more regions

AWS has expanded the AI recommendations for descriptions capability in Amazon DataZone to four new regions: South America (Sao Paulo), Europe (London), Asia Pacific (Sydney), and Canada (Central). This expansion helps improve data discovery, understanding, and usage by enriching the business data catalog. With a single click, data producers can generate comprehensive business data descriptions and context, highlight impactful columns, and include recommendations on analytical use cases.\n With AI recommendations for descriptions in Amazon DataZone, data consumers can identify data tables and columns enhancing, which enhances data discoverability and cuts down on back-and-forth communications with data producers. Data consumers (such as data analysts, data engineers, and data scientists) have more contextualized data at their fingertips to inform their analysis. The auto-generated descriptions enable a richer search experience, as search results are now also based on detailed descriptions, possible use cases, and key columns. Data producers can also use APIs to programmatically generate descriptions for assets. Amazon DataZone AI recommendations for descriptions is generally available in Amazon DataZone domains provisioned in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Asia Pacific (Tokyo), South America (Sao Paulo), Europe (London), Asia Pacific (Sydney), and Canada (Central). To learn more, see the Amazon DataZone Automate Data Discovery webpage, and User Guide. For pricing information, see the pricing page.

AWS Network Load Balancer now supports configurable TCP idle timeout

Today AWS Network Load Balancer (NLB) is launching a new capability that allows you to align the TCP idle timeout value of NLB with clients and target applications. Using this capability you can now reduce TCP connection retries and latency in applications that use long-lived flows, such as telemetry reporting devices, databases, streaming services and ERP systems, when using NLB.\n Prior to this launch, TCP idle timeout was a fixed value of 350 seconds, which could cause TCP connection handshake retries for the long-lived traffic flows of some applications and add latency. With this launch, you now have the flexibility to configure NLB’s TCP idle timeout to be a value between 60 seconds and 6000 seconds, with the default remaining at 350 seconds for backward compatibility. This configuration can help reduce latency for long-lived traffic flows by maintaining target stickiness for the optimal duration based on the needs of your application. You can configure the TCP idle timeout value using the ’tcp.idle_timeout.seconds’ listener attribute on your existing and newly created NLBs. This capability is available in all AWS commercial and AWS GovCloud (US) regions. For more information on how to use this feature, see this AWS blog post and product documentation.

Amazon Connect now offers intraday forecasts

Amazon Connect forecasting, capacity planning, and agent scheduling now includes machine learning (ML) powered intraday forecast capabilities, available within the Amazon Connect Contact Lens dashboards. With intraday forecasts, you receive updates every 15 minutes with predictions for rest-of-day contact volumes, average queue answer time, and average handle time. These forecasts allow you to take proactive actions to improve customer wait time and service level. For example, if contact volume drops below expected levels, contact center managers can use the intraday forecast to predict how long that drop will continue, determine the required staffing levels, and shift the remaining agents into back office work or other higher volume queues.\n This feature is available in all AWS Regions where Amazon Connect forecasting, capacity planning, and agent scheduling is available. To learn more see the Amazon Connect Administrator Guide.

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