Cloud Computing Köln

Amazon CloudWatch now supports warm-up periods for alarms

Amazon CloudWatch now lets you configure a warm-up period for metric alarms and log alarms, delaying alarm evaluation for a set time after the alarm is created. This reduces noise from missing data while a new resource or service starts up and begins publishing metrics. For example, a team that provisions a new microservice and its alarms together through a CI/CD pipeline can attach a warm-up period so alarms do not page the on-call engineer while the service is still starting up and has not yet published metrics.

Previously, when you created an alarm before the underlying metric was reporting data, CloudWatch evaluated the alarm right away using its “treat missing data” setting. For resources that take time to begin emitting metrics, such as a newly deployed application or service, this could cause the alarm to transition state and run actions on missing data during startup, triggering unnecessary notifications. A warm-up period fixes this by giving you two ways to hold off evaluation during startup: wait a fixed duration you set before evaluation begins, or let CloudWatch start evaluating automatically as soon as the metric actually has enough data to fill the alarm’s evaluation window.

You set the warm-up period with the WarmUpConfiguration parameter when you create or update an alarm. Specify a warm-up duration from 1 to 2,880 minutes (2 days). By default, the alarm ends warm-up early and begins evaluating as soon as enough data fills its evaluation window. Additionally, you can optionally require the alarm to wait the full duration before evaluating.

Warm-up periods are available in all AWS Regions where Amazon CloudWatch is offered at no additional charge beyond standard CloudWatch alarm pricing.

To get started, see  Alarm warm-up periods  and  Create an alarm that uses a warm-up period  in the Amazon CloudWatch User Guide.
Quelle: aws.amazon.com

Claude Fable 5.1, Anthropic's new frontier model is now available on AWS

Claude Fable 5.1 is generally available on AWS and brings Anthropic’s most capable frontier model to all customers. Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. A clear improvement over Claude Fable 5, Fable 5.1 is a step up in intelligence on the hardest reasoning tasks, providing better judgement on ambiguous work and fewer confident wrong answers. Claude Mythos 5.1, the same underlying model as Claude Fable 5.1 with its full cyber and bio capabilities retained for cybersecurity and biology research, is available with limited access.
Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its own, handling features across an entire codebase, code review, and performance work over sessions that run for hours. If it gets stuck, it says so instead of reporting success, and it is less likely to take shortcuts like disabling a failing test. It takes analysis from the first question to the finished document, and it is Anthropic’s strongest model yet for knowledge work.
Anthropic has designated Fable 5.1 a Covered Model, a category of Claude models that carry additional data retention, safety review, and access policies wherever they’re offered. Enterprise Frontier Safeguards (EFS) built in partnership between AWS and Anthropic, will let eligible customers use Covered Models while keeping their data in a cloud environment they control. Read the launch blog to learn more.
Customers have two ways to access Claude Fable 5.1: Amazon Bedrock and Claude Platform on AWS. To learn more, see the Amazon Bedrock documentation, regional availability, and Claude Platform on AWS documentation..
Quelle: aws.amazon.com

Amazon MWAA supports Apache Airflow version 3.3.1

Amazon Managed Workflows for Apache Airflow (MWAA) now supports Apache Airflow version 3.3.1, the latest release of the popular open-source workflow orchestration framework. Amazon MWAA is a managed orchestration service for Apache Airflow that makes it easier to set up and operate end-to-end data pipelines in the cloud.
Apache Airflow 3.3 introduces stateful tasks and multi-language support. With the new Task and Asset State Store, tasks can now persist durable state across retries and reruns, enabling cursor tracking and crash-safe reconnection to long-running jobs. The Language Task SDK (experimental) lets teams write task logic in Java or Go while keeping orchestration in Python. Additional improvements include expanded asset partitioning, pluggable retry policies, and bulk actions for DAG runs and task instances. Apache Airflow 3.3.1 also delivers stability, security, and UI improvements on top of these capabilities.
You can launch a new Apache Airflow 3.3.1 environment on Amazon MWAA, or upgrade from 3.2 or later, with a few clicks in the AWS Management Console in all currently supported Amazon MWAA regions. To learn more about Apache Airflow 3.3.1, visit the Amazon MWAA documentation and the Apache Airflow 3.3.1 change log in the Apache Airflow documentation.
Apache, Apache Airflow, and Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
Quelle: aws.amazon.com

Amazon Connect Customer dashboards now support compact mode

Amazon Connect Customer now offers compact mode on the analytics dashboards, increasing data density so supervisors can see more of their operational data without scrolling. Compact mode reduces widget size, font, and minimizes filters to maximize screen space. For example, a supervisor on a 13-inch laptop toggles compact mode, which reduces row height, enabling them to view all team agents on the widget without scrolling and spot non-adherent agents faster.
Compact mode on dashboards is available in all AWS regions where Amazon Connect Customer is offered. To learn more about Amazon Connect Customer analytics dashboards, see the Amazon Connect Customer Administrator Guide.  To learn more about Amazon Connect Customer, visit the Amazon Connect Customer website.
Quelle: aws.amazon.com

Amazon Timestream for InfluxDB is now available in 8 additional AWS Regions

You can now use Amazon Timestream for InfluxDB in the Africa (Cape Town), Asia Pacific (Bangkok), Asia Pacific (Hong Kong), Asia Pacific (Hyderabad), Asia Pacific (Melbourne), Asia Pacific (Seoul), Europe (Zurich), and Israel (Tel Aviv) AWS Regions. Timestream for InfluxDB makes it easy for application developers and DevOps teams to run fully managed InfluxDB databases on AWS for real-time time-series applications using open-source APIs.
Timestream for InfluxDB offers Multi-AZ high availability, read replicas, enhanced durability, and multi-node scaling — giving you flexible deployment options to match your workload as it evolves. Whether you’re starting with a single-node setup or scaling to a 15-node Enterprise cluster, you can right-size your infrastructure without re-architecting.
You can create your InfluxDB databases using the Amazon Timestream for InfluxDB console. AWS CLI, or AWS SDKs . Amazon Timestream for InfluxDB is available in the following AWS Regions. For more information, see the Amazon Timestream for InfluxDB documentation and pricing page.
Quelle: aws.amazon.com