Kategorie: Amazon AWS

Amazon GameLift Servers launches DDoS Protection

We’re excited to announce Amazon GameLift Servers DDoS Protection, a new feature that helps game developers protect session-based multiplayer games that utilize Amazon GameLift Servers to help improve overall game session resiliency. DDoS Protection is designed to defend against denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks, providing proactive, User Datagram Protocol (UDP)-based traffic protection–without the need for manual byte matching, and with negligible latency added.
Amazon GameLift Servers DDoS Protection co-locates a relay network directly alongside your game servers. The relay authenticates client traffic using access tokens so that only authorized traffic reaches the server. The feature also enforces per-player traffic limits to help prevent disruptions, even from seemingly legitimate sources. Game developers can use DDoS Protection to protect against targeted disruptions to specific players or entire game sessions. Check out the Amazon GameLift Servers release notes to get started through the console or API, with sample code provided for popular game engines including Unreal Engine and native C++.
Amazon GameLift Servers DDoS Protection is available at no additional cost to Amazon GameLift Servers customers and is initially available in the following regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Asia Pacific (Sydney), Asia Pacific (Tokyo), Pacific (Seoul).
Quelle: aws.amazon.com

Amazon OpenSearch Ingestion now supports Amazon Managed Service for Prometheus as a sink

Amazon OpenSearch Ingestion now supports Amazon Managed Service for Prometheus  as a sink, making it possible to build fully managed, end-to-end metrics ingestion pipelines without any custom forwarding infrastructure. With this launch, customers can now manage their entire metrics ingestion workflow using the same pipeline infrastructure they already use for logs and traces.
Customers can now choose the right destination for each observability signal — sending logs and traces to Amazon OpenSearch Service for powerful full-text search, log analytics, and trace correlation, while routing metrics to Amazon Managed Service for Prometheus for time-series storage and analysis. This flexibility allows teams to build purpose-fit observability pipelines that leverage the strengths of each service without compromising on data fidelity or analytical capability. Amazon OpenSearch Ingestion’s built-in data transformation and enrichment capabilities allow customers to prepare and refine metrics before they land in Amazon Managed Service for Prometheus, improving data quality and consistency. Once metrics are in Amazon Managed Service for Prometheus, customers can query them using Prometheus Query Language to analyze trends, configure alerting rules to get notified when metrics cross defined thresholds, and visualize their data using Amazon Managed Grafana for rich, customizable views of infrastructure and application health.
The feature is supported in all regions that Amazon OpenSearch Ingestion and  is currently available. Customers can get started by using the new sink for Amazon Managed Service for Prometheus in their pipeline configuration via the AWS Management console or using the AWS CLI and start ingesting metrics into their Amazon Managed Service for Prometheus workspace.
To learn more and get started, visit the Amazon OpenSearch Ingestion documentation.
Quelle: aws.amazon.com

Amazon Lightsail now offers OpenClaw, a private self-hosted AI assistant

Amazon Lightsail now lets you deploy OpenClaw, a private self-hosted AI assistant, on your own cloud infrastructure in a simple and secure manner. Every Lightsail OpenClaw instance ships with built-in security controls, pre-configured and ready to use. Sandboxing isolates each agent session for improved security posture. One-click HTTPS access puts the OpenClaw dashboard in your browser securely, without requiring manual TLS configuration. Device pairing authentication ensures only your authorized devices can connect to your assistant. Automatic snapshots back up your configuration continuously, so you never lose your setup. Amazon Bedrock serves as the default model provider for Lightsail OpenClaw, and you can swap models or connect to Slack, Telegram, WhatsApp, and Discord as per your requirements. Amazon Lightsail is available in 15 AWS Regions including US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (London), Asia Pacific (Tokyo), and Asia Pacific (Jakarta). To get started, visit the Lightsail console. For pricing and other details, visit the Amazon Lightsail pricing and quick start documentation pages.
Quelle: aws.amazon.com

 Policy in Amazon Bedrock AgentCore is now generally available

Policy in Amazon Bedrock AgentCore is now generally available, providing organizations with centralized, fine-grained controls for agent-tool interactions. Policy operates outside your agent code, enabling security, compliance, and operations teams to define tool access and input validation rules without modifying agent code. Teams can author policies using natural language that automatically converts to Cedar, the AWS open-source policy language. Policies are stored in a policy engine and attached to an AgentCore Gateway, which intercepts agent-tool traffic and evaluates each request against the policies before allowing or denying tool access. Policy helps ensure agents operate within defined parameters while maintaining organizational visibility and governance.
Policy in AgentCore is available in thirteen AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Paris), and Europe (Stockholm).
Learn more about Policy in AgentCore through the documentation, and get started with the AgentCore Starter Toolkit.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio launches support for remote connection from Kiro IDE

Today, AWS announces the ability to remotely connect from Kiro IDE to Amazon SageMaker Unified Studio. This new capability allows data scientists, ML engineers, and developers to leverage their Kiro setup – including its spec-driven development, conversational coding, and automated feature generation capabilities – while accessing the scalable compute resources of Amazon SageMaker. By connecting Kiro to SageMaker Unified Studio using the AWS toolkit extension, you can eliminate context switching between your local IDE and cloud infrastructure, maintaining your existing agentic development workflows within a single environment for all your AWS analytics and AI/ML services.
SageMaker Unified Studio, part of the next generation of Amazon SageMaker, offers a broad set of fully managed cloud interactive development environments (IDE), including JupyterLab and Code Editor based on Code-OSS (Open-Source Software). Starting today, you can also use your customized local Kiro setup – complete with specs, steering files, and hooks – while accessing your compute resources and data on Amazon SageMaker. Since Kiro is built on Code-OSS, authentication is secure via IAM through the AWS Toolkit extension, giving you access to all your SageMaker Unified Studio domains and projects. This integration provides a convenient path from your local AI-powered development environment to scalable infrastructure for running workloads across data processing, SQL analytics services like Amazon EMR, AWS Glue, and Amazon Athena, and ML workflows – all with enterprise-grade security including customer-managed encryption keys and AWS IAM integration.
This feature is available in all Regions where Amazon SageMaker Unified Studio is available. To learn more, refer to the SageMaker user guide.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio adds metadata sync with third-party catalogs

Amazon SageMaker Unified Studio now supports metadata and context sync across Atlan, Collibra, and Alation. These integrations synchronize catalog metadata between Amazon SageMaker Catalog and each partner platform, giving teams a consistent view of their data and AI assets regardless of which tool they use day to day. Organizations can maintain aligned glossary terms, asset descriptions, and ownership information across platforms without manual reconciliation.
All three integrations synchronize key metadata elements including projects, assets, descriptions, glossary terms, and their hierarchies. With the Collibra integration, you can synchronize metadata in both directions between SageMaker Catalog and the partner platform, so updates you make in one are reflected in the other. Also, you can manage SageMaker Unified Studio data access requests from Collibra. With the Atlan and Alation integration, you can ingest metadata from SageMaker Catalog into Alation with additional enhancements coming soon. You set up these integrations by setting up a connection to SageMaker Unified Studio from within Atlan and Alation, while the Collibra integration is available as an open-source solution on GitHub.
To learn more, visit the Amazon SageMaker Unified Studio documentation. For implementation details, see the Atlan blog post, Collibra blog post , and Alation blog post.
Quelle: aws.amazon.com

Amazon SageMaker Unified Studio now supports AWS Glue 5.1 for data processing jobs

Amazon SageMaker Unified Studio now supports AWS Glue 5.1 for Visual ETL, notebook, and code-based data processing jobs. With AWS Glue 5.1 in Amazon SageMaker Unified Studio, data engineers and data scientists can run jobs on Apache Spark 3.5.6 with Python 3.11 and Scala 2.12.18, and use updated open table format libraries including Apache Iceberg 1.10.0, Apache Hudi 1.0.2, and Delta Lake 3.3.2.
You can use AWS Glue 5.1 in Amazon SageMaker Unified Studio when creating data processing jobs by selecting Glue 5.1 from the version dropdown in job settings. This applies to Visual ETL jobs, notebook jobs, and code-based jobs, so you can take advantage of the latest Spark runtime and open table format libraries across all your data processing workflows.
AWS Glue 5.1 in Amazon SageMaker Unified Studio is available in all the regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Amazon SageMaker Unified Studio documentation. For details on what’s included in AWS Glue 5.1, including updated open table format support and access control capabilities, see the AWS Glue documentation.
Quelle: aws.amazon.com

AWS Elemental MediaLive Now Supports SRT Listener Mode

AWS Elemental MediaLive now supports Secure Reliable Transport (SRT) Listener mode for both inputs and outputs. With SRT Listener mode, MediaLive waits for connections rather than initiating them. Upstream sources push live video directly to MediaLive, and downstream systems pull encoded streams on demand. This simplifies network setup by removing the need for complex firewall configurations or static, publicly accessible IP addresses on the source or destination side. SRT Listener mode complements MediaLive’s existing SRT Caller mode, giving you full control over which side of the connection initiates the SRT handshake.
SRT Listener mode enables flexible contribution and distribution workflows. On the input side, you can push streams from on-premises encoders or remote production sites, including MediaLive Anywhere deployments, directly to MediaLive in the cloud without coordinating firewall changes with your network team. On the output side, downstream distribution partners can connect to MediaLive and pull encoded streams when ready, without requiring MediaLive to initiate outbound connections. Both SRT Listener inputs and outputs support configurable latency settings and mandatory AES encryption to help ensure content security.
SRT Listener mode is available in all AWS Regions where AWS Elemental MediaLive is offered. To get started, see Setting up an SRT Listener input and Creating SRT outputs in listener mode in the AWS Elemental MediaLive User Guide.
Quelle: aws.amazon.com

AWS Batch now supports configurable scale down delay

AWS Batch now allows you to configure a scale down delay for managed compute environments, helping reduce job processing delays for intermittent and periodic workloads. With the new minScaleDownDelayMinutes parameter, you can specify how long AWS Batch keeps instances running after their jobs complete (from 20 minutes to 1 week), preventing unnecessary instance terminations and relaunches that can delay subsequent job processing. You can configure the scale down delay when creating or updating a compute environment via the AWS Batch API (CreateComputeEnvironment or UpdateComputeEnvironment) or the AWS Batch Management Console. The delay is applied at the instance level, based on when each instance last completed a job. Scale down delay is supported today in all AWS Regions where AWS Batch is available. For more information, see the AWS Batch API Guide.
Quelle: aws.amazon.com

AWS Config now supports 30 new resource types

AWS Config now supports 30 additional AWS resource types across key services including Amazon Bedrock AgentCore and Amazon Cognito. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources. With this launch, if you have enabled recording for all resource types, then AWS Config will automatically track these new additions. The newly supported resource types are also available in Config rules and Config aggregators. You can now use AWS Config to monitor the following newly supported resource types in all AWS Regions where the supported resources are available: Resource Types:

AWS::AppSync::DataSource
AWS::Deadline::LicenseEndpoint

AWS::Batch::ConsumableResource
AWS::Deadline::QueueEnvironment

AWS::Bedrock::DataSource
AWS::Detective::OrganizationAdmin

AWS::BedrockAgentCore::Gateway
AWS::GameLift::ContainerFleet

AWS::BedrockAgentCore::Memory
AWS::GameLift::ContainerGroupDefinition

AWS::Cognito::IdentityPoolRoleAttachment
AWS::GameLift::GameServerGroup

AWS::Cognito::LogDeliveryConfiguration
AWS::GameLift::Location

AWS::Cognito::UserPoolUICustomizationAttachment
AWS::IoT::TopicRule

AWS::Connect::RoutingProfile
AWS::Omics::ReferenceStore

AWS::DataBrew::Dataset
AWS::PCAConnectorAD::Template

AWS::DataBrew::Job
AWS::PCAConnectorSCEP::Challenge

AWS::DataBrew::Project
AWS::ResourceExplorer2::View

AWS::DataBrew::Recipe
AWS::ResourceGroups::Group

AWS::DataBrew::Ruleset
AWS::Scheduler::ScheduleGroup

AWS::DataBrew::Schedule
AWS::VerifiedPermissions::IdentitySource

Quelle: aws.amazon.com