Kategorie: Amazon AWS

Aurora DSQL launches new support for Tortoise, Flyway, and Prisma

Today we are announcing the release of Aurora DSQL integrations for popular ORM and database migration tools: an adapter for Tortoise (Python ORM), a dialect for Flyway (schema management tool), and CLI tools for Prisma (Node.js ORM). These integrations help developers use their preferred frameworks with Aurora DSQL while automatically handling IAM authentication and Aurora DSQL-specific compatibility requirements. The Aurora DSQL Adapter for Tortoise enables Python developers to build applications using Tortoise without writing custom authentication code. The adapter supports both asyncpg and psycopg drivers, integrates with the Aurora DSQL Connector for Python for automatic IAM token generation, and includes compatibility patches for rich migrations. The Flyway dialect adapts Flyway for Aurora DSQL’s distributed architecture by automatically handling Aurora DSQL-specific behaviors such as IAM-based authentication. The Prisma CLI tools help Node.js developers validate their Prisma schemas for Aurora DSQL compatibility and generate Aurora DSQL-compatible migrations, streamlining the path from development to production. To get started, visit the GitHub repositories for Tortoise ORM, Flyway, and Prisma. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.
Quelle: aws.amazon.com

Aurora DSQL launches new integrations for Visual Studio Code SQLTools and DBeaver

Today we are announcing the release of the Aurora DSQL Driver for SQLTools and the Aurora DSQL Plugin for DBeaver Community Edition. These integrations allow customers to leverage popular database tools to run queries against Aurora DSQL clusters, explore database schemas, and manage their data. Both integrations simplify database connectivity by automatically handling IAM authentication and transparently managing access tokens, eliminating the need to write token generation code or manually supply IAM tokens. The SQLTools driver integrates Aurora DSQL with Visual Studio Code and is also available on Open VSX Registry for use with VS Code-compatible editors such as Cursor and Kiro. The DBeaver plugin is built on top of the Aurora DSQL Connector for JDBC. Both integrations eliminate security risks associated with traditional user-generated passwords by using AWS IAM credentials for secure, password-free authentication. To get started, visit the Aurora DSQL documentation page for VSCode and DBeaver. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.
Quelle: aws.amazon.com

AWS Security Agent adds support for penetration tests on shared VPCs across AWS accounts

AWS Security Agent now enables customers to run penetration tests against Virtual Private Cloud (VPC) resources shared from other AWS accounts within the same organization. This new capability allows security teams to perform comprehensive security assessments across their multi-account environments using AWS Security Agent. By leveraging AWS Resource Access Manager (RAM), customers can securely share VPC resources from sub-accounts to a central AWS account where penetration testing is conducted. This feature addresses the challenge of testing distributed architectures spanning multiple AWS accounts. Security professionals can now create an Agent Space in a central account and use RAM to access VPC resources from connected sub-accounts for testing. This streamlines security assessments for organizations with complex multi-account setups. The ability to comprehensively test shared VPC resources enhances an organization’s overall security posture. To get started, ensure your accounts are part of the same AWS Organization and configure resource sharing using RAM. Then launch AWS Security Agent in your central account to begin penetration testing across the shared VPC resources. For more information on AWS Security Agent and its penetration testing capabilities, visit the AWS Security Agent documentation. 
Quelle: aws.amazon.com

AWS Outposts racks now support additional Amazon CloudWatch metrics in AWS GovCloud (US) Regions

AWS Outposts racks now support VifConnectionStatus and VifBgpSessionState Amazon CloudWatch metrics in AWS GovCloud (US) Regions. These metrics provide visibility into the connectivity status of your Outposts racks’ Local Gateway (LGW) and Service Link Virtual Interfaces (VIFs) with your on-premises devices. These metrics provide you with the ability to monitor Outposts VIF connectivity status directly within the CloudWatch console, without having to rely on external networking tools or coordination with other teams. You can use these metrics to set alarms, troubleshoot connectivity issues, and ensure your Outposts racks are properly integrated with your on-premises infrastructure. The VifConnectionStatus metric indicates whether an Outposts VIF is successfully connected, configured, and ready to forward traffic. A value of “1” means that the VIF is operational, while “0” means that it is not ready. The VifBgpSessionState metric shows the current state of the BGP session between the Outposts VIF and the on-premises device, with values ranging from 1 (IDLE) to 6 (ESTABLISHED). The VifConnectionStatus and VifBgpSessionState metrics are now available for all Outposts VIFs in AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions where Outposts racks are available. To get started, read this blog post and access the metrics in the CloudWatch console. To learn more, check out the CloudWatch metrics for Outposts documentation for first-generation Outposts racks.
Quelle: aws.amazon.com

Announcing new metal sizes for Amazon EC2 M8gn and M8gb instances

Today, AWS announces the general availability of metal-24xl and metal-48xl sizes for Amazon Elastic Compute Cloud (Amazon EC2) M8gn and M8gb instances. These instances are powered by AWS Graviton4 processors to deliver up to 30% better compute performance than AWS Graviton3 processors. M8gn instances feature the latest 6th generation AWS Nitro Cards, and offer up to 600 Gbps network bandwidth, the highest network bandwidth among network optimized EC2 instances. M8gb offers up to 300 Gbps of EBS bandwidth to provide higher EBS performance compared to same-sized equivalent Graviton4-based instances.
M8gn and M8gb instances offer instance sizes up to 48xlarge and metal-48xl, with up to 768 GiB of memory. M8gn instances offer up to 600 Gbps of networking bandwidth, up to 60 Gbps of bandwidth to Amazon Elastic Block Store (EBS), and are ideal for network-intensive workloads such as high-performance file systems, distributed web scale in-memory caches, caching fleets, real-time big data analytics, Telco applications such as 5G User Plane Function (UPF). M8gb instances offer up to 300 Gbps of EBS bandwidth, up to 400 Gbps of networking bandwidth, and are ideal for workloads requiring high block storage performance such as high-performance databases and NoSQL databases.
M8gn and M8gb instances support Elastic Fabric Adapter (EFA) networking on 16xlarge, 24xlarge, 48xlarge, metal-24xl, and metal-48xl sizes. EFA networking enables lower latency and improved cluster performance for workloads deployed on tightly coupled clusters.
The new metal-24xl and metal-48xl sizes are available in the AWS US East (N. Virginia) region. 
To begin your Graviton journey, visit the Level up your compute with AWS Graviton page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs.
Quelle: aws.amazon.com

Amazon RDS Snapshot Export to S3 now available in AWS GovCloud (US) Regions

Amazon RDS Snapshot Export to S3 is now available in AWS GovCloud (US) regions, enabling you to export snapshot data in Apache Parquet format for analytics, data retention, and machine learning use cases. Snapshot export to S3 supports all DB snapshot types (manual, automated system, and AWS Backup snapshots) and runs directly on the snapshot without impacting database performance. The exported data in Apache Parquet format can be analyzed using other AWS services such as Amazon Athena, Amazon SageMaker, or Amazon Redshift Spectrum, or with big data processing frameworks such as Apache Spark. You can create a snapshot export with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. Snapshot Export to S3 is supported for Amazon Aurora PostgreSQL – Compatible Edition and Amazon Aurora MySQL, Amazon RDS for PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for MariaDB snapshots. For more information, including instructions on getting started, read Aurora documentation or Amazon RDS documentation.
Quelle: aws.amazon.com

AWS Observability now available as a Kiro power

Today, AWS announces AWS Observability as a Kiro power, enabling developers and operators to investigate infrastructure and application health issues faster with AI agent-assisted workflows in Kiro. Kiro Powers is a repository of curated and pre-packaged Model Context Protocol (MCP) servers, steering files, and hooks validated by Kiro partners to accelerate specialized software development and deployment use cases. The AWS Observability power packages four specialized MCP servers with targeted observability guidance: the CloudWatch MCP server for observability data; the Application Signals MCP server for application performance monitoring; the CloudTrail MCP server for security analysis and compliance; and the AWS Documentation MCP server for contextual reference access. This unified platform gives Kiro agents instant context for comprehensive workflows including alarm response, anomaly detection, distributed tracing, SLO compliance monitoring, and security investigation. Additionally, the power includes automated gap analysis that helps you identify and fix missing instrumentation. With the AWS Observability power, developers can now accelerate troubleshooting their distributed applications and infrastructure in minutes, directly in their IDE. The power addresses two critical needs: reducing mean time to resolution (MTTR) for active incidents and proactively improving your observability stack. For faster incident response, when investigating an active alarm, the power dynamically loads relevant guidance and operational signals so AI agents receive only the context needed for the specific troubleshooting task at hand. For stack improvement, the automated gap analysis examines your code to identify missing instrumentation patterns—such as unlogged errors, missing correlation IDs, or absent distributed tracing—and provides actionable recommendations. The power includes eight comprehensive steering guides covering incident response, alerting, performance monitoring, security auditing, and gap analysis. The AWS Observability power is available for one-click installation within Kiro IDE and Kiro powers webpage in all AWS Regions, with each underlying MCP server functional based on regional support of the corresponding AWS service. To learn more about AWS observability MCP servers, visit our documentation. 
Quelle: aws.amazon.com

AWS Compute Optimizer now applies AWS-generated tags to EBS snapshots created during automation

AWS Compute Optimizer makes it easier to identify snapshots that are created when snapshotting and deleting unattached Amazon Elastic Block Store (EBS) volumes by automatically applying an AWS-generated tag during creation. This enhancement improves visibility and tracking of EBS snapshots created through Compute Optimizer Automation.
When Compute Optimizer creates a snapshot before deleting an unattached EBS volume—whether initiated through manual actions or automation rules—the snapshot now receives the tag aws:compute-optimizer:automation-event-id with a tag value that links the snapshot to the unique identifier of the automation event that created it. This allows you to easily identify, track, and manage snapshots created through the automated optimization process, helping you maintain better governance over your backup resources and understand the source of snapshots in your environment.
This is available in all AWS Regions where AWS Compute Optimizer Automation is available. To get started with automated optimization, go to the AWS Compute Optimizer console or visit the user guide documentation.
Quelle: aws.amazon.com

Amazon Bedrock now supports server-side tool execution with AgentCore Gateway

Amazon Bedrock now enables server-side tool execution through Amazon Bedrock AgentCore Gateway integration with the Responses API. Customers can connect their AgentCore Gateway tools to Amazon Bedrock models, enabling server-side tool execution without client-side orchestration.
With this launch, customers can specify an AgentCore Gateway ARN as a tool connector in Responses API requests. Amazon Bedrock automatically discovers available tools from the gateway, presents them to the model during inference, and executes tool calls server-side when the model selects them, all within a single API call. This eliminates the need for customers to build and maintain client-side tool orchestration loops, reducing application complexity and latency for agentic workflows. Customers retain full control over tool access through their existing AgentCore Gateway configurations and AWS IAM permissions.
Server-side tool execution with AgentCore Gateway supports all models available through the Amazon Bedrock Responses API. Customers define tools using the MCP server connector type with their gateway ARN, and Amazon Bedrock handles tool discovery, model-driven tool selection, execution, and result injection automatically. Multiple tool calls within a single conversation turn are supported, and tool results are streamed back to the client in real time.
This capability is generally available in all AWS Regions where both Amazon Bedrock’s Responses API and Amazon Bedrock AgentCore Gateway are available. To get started, visit the Amazon Bedrock documentation or the Amazon Bedrock console. For more information about Amazon Bedrock AgentCore Gateway, see the AgentCore documentation.
Quelle: aws.amazon.com

Amazon EKS Node Monitoring Agent is now open source

Amazon Elastic Kubernetes Service (Amazon EKS) Node Monitoring Agent is now open source. You can access the Amazon EKS Node Monitoring Agent source code and contribute to its development on GitHub. Running workloads reliably in Kubernetes clusters can be challenging. Cluster administrators often have to resort to manual methods of monitoring and repairing degraded nodes in their clusters. The Amazon EKS Node Monitoring Agent simplifies this process by automatically monitoring and publishing node-level system, storage, networking, and accelerator issues as node conditions, which are used by Amazon EKS for automatic node repair. With the Amazon EKS Node Monitoring Agent’s source code available on GitHub, you now have visibility into the agent’s implementation, can customize it to fit your requirements, and can contribute directly to its ongoing development. The Amazon EKS Node Monitoring Agent is included in Amazon EKS Auto Mode and is available as an Amazon EKS add-on in all AWS Regions where Amazon EKS is available. To learn more about the Amazon EKS Node Monitoring Agent and node repair, visit the Amazon EKS documentation.
Quelle: aws.amazon.com