Cloud Computing Köln

SageMaker Unified Studio automates Glue connector provisioning for cross-subnet job retries

Amazon SageMaker Unified Studio now supports automatic creation of connections for Glue job retries across subnets to improve data pipeline resilience. This helps organizations running business-critical data pipelines reduce unplanned downtime and meet their SLAs — without requiring engineers to manually configure backup connectors or intervene during subnet failures.
With this launch, SageMaker Unified Studio automates the provisioning of Glue connectors across subnets defined in the domain VPC configuration. Administrators can define their domain VPC with multiple private subnets across availability zones, and the system provisions the connectors needed for all new projects so that failed jobs can be retried on an alternate subnet automatically. If a Glue job fails because the primary subnet is unavailable due to IP address exhaustion or availability zone degradation, the job can be retried on a connector in a different subnet. No user action is needed beyond the initial VPC configuration on the domain.
This feature is available in all AWS Regions where Amazon SageMaker Unified Studio is available. To learn more, visit the Amazon SageMaker Unified Studio documentation. 
Quelle: aws.amazon.com

Amazon EC2 C7i-flex, M7i-flex & M7i instances now available in Asia Pacific (Hyderabad) region

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i-flex, M7i-flex and M7i instances powered by custom 4th Gen Intel Xeon Scalable processors (code-named Sapphire Rapids) are available in Asia Pacific (Hyderabad) region. These custom processors, available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. C7i-flex and M7i-flex instances are the easiest way for you to get price-performance benefits for a majority of general-purpose workloads. They deliver up to 19% better price-performance compared to C6i and M6i  instances respectively. These instances offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources such as web and application servers, virtual-desktops, batch-processing, and microservices. 
 
M7i deliver up to 15% better price-performance compared to M6i. M7i instances are a great choice for workloads that need the largest instance sizes or continuous high CPU usage, such as gaming servers, CPU-based machine learning (ML), and video-streaming. M7i offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl). These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology that are used to facilitate efficient offload and acceleration of data operations and optimize performance for workloads. To learn more, visit the EC2  C7i-flex and M7i/M7i-flex instances pages.
Quelle: aws.amazon.com

Amazon CloudWatch Logs Insights adds new query commands and functions

Amazon CloudWatch Logs Insights query language now supports 13 new commands and functions that give you more powerful ways to query, transform, and analyze your logs. Customers analyzing logs in CloudWatch Logs Insights often need to perform string manipulation, encode or decode field values, parse non-JSON log formats, or calculate geographic distances, so they can derive deeper insights from their logs. With this launch, CloudWatch Logs Insights provides new string and numeric functions (round, startswith, endswith, case, regex_replace, haversine), encoding and decoding functions (urlencode, urldecode, base64encode, base64decode), and new parse and analysis commands (parse logfmt, expand, relevantfields). You can now filter logs by string prefixes, decode Base64 payloads inline, parse logfmt structured logs into fields, expand nested JSON arrays into individual records, calculate distances between coordinates, and automatically surface relevant fields in high-cardinality log groups. These commands and functions are available today in all commercial AWS Regions. To learn more, see the Amazon CloudWatch Logs documentation.
Quelle: aws.amazon.com

Amazon Bedrock expands support for request-level usage attribution

Amazon Bedrock customers can now attribute model inference usage to specific teams, applications, environments, and experiments at the individual request level on the InvokeModel and InvokeModelWithResponseStream APIs. This gives customers fine-grained visibility into how their Amazon Bedrock usage is distributed across their organization, helping them understand consumption patterns, optimize spend, and report usage back to internal stakeholders without provisioning additional resources. This launch builds on Amazon Bedrock’s existing portfolio of usage attribution capabilities. Customers can already attribute model inference usage at the resource and identity level using application inference profiles, IAM principal-based attribution, project-level tracking on the OpenAI-compatible bedrock-mantle endpoint, and workspace-level tracking for Anthropic Claude models. For finer-grained, per-request attribution, the Converse and ConverseStream APIs have supported request-level metadata since launch. Today’s release brings the same capability to the InvokeModel and InvokeModelWithResponseStream APIs, giving customers a consistent way to tag inference calls across the entire bedrock-runtime endpoint. With this launch, customers can tag each Amazon Bedrock model inference call with attributes like team, project, or environment, and analyze usage by these tags in Amazon Bedrock model invocation logs. To get started, enable model invocation logging in the AWS Region where you call Amazon Bedrock, then add metadata to your inference requests. This feature is available in all AWS commercial Regions where Amazon Bedrock is available. To learn more, see Request metadata. 
Quelle: aws.amazon.com

Amazon RDS Custom now supports the latest GDR updates for Microsoft SQL Server

Amazon Relational Database Service (Amazon RDS) Custom for SQL Server now supports the latest General Distribution Release (GDR) updates for Microsoft SQL Server. This release includes support for SQL Server 2019 CU32+GDR KB5084816 (RDS version 15.00.4465.1.v1) and SQL Server 2022 CU24+GDR KB5083252 (RDS version 16.00.4250.1.v1). The GDR updates address vulnerabilities described in CVE-2026-32167 and CVE-2026-32176. For additional information on the improvements and fixes included in these updates, see Microsoft documentation for KB5084816, KB5083252. You can upgrade your Amazon RDS Custom for SQL Server instances to apply these recommended updates using Amazon RDS Management Console, or by using the AWS SDK or CLI. To learn more about upgrading your database instances, see Amazon RDS Custom User Guide.
Quelle: aws.amazon.com