Kategorie: Amazon AWS

Amazon Connect Customer launches the ability to interrupt an agent with an urgent contact

Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well. You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts. This feature is available in all AWS regions where Amazon Connect Customer is offered. To learn more about this feature, see the Amazon Connect Customer Administrator Guide. To learn more about Amazon Connect Customer, the AWS cloud-based contact center, please visit the Amazon Connect Customer website.
Quelle: aws.amazon.com

Nested virtualization is now available on additional Intel platforms and US Gov Cloud regions

Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C7id,R7id, M7id, C7i-flex,R7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Quelle: aws.amazon.com

Amazon RDS for PostgreSQL, MySQL, and MariaDB now supports M9g database instances

AWS Graviton5-based M9g database (DB) instances are now generally available for Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB. Graviton5-based instances provide up to a 30% performance improvement and up to a 23% price/performance improvement for on-demand pricing over Graviton4-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload. AWS Graviton5 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. M9g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, M9g DB instances offer up to 192 vCPU, up to 100Gbps enhanced networking bandwidth, and up to 72Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). These instances are now available in the US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.
Quelle: aws.amazon.com

AWS Outposts racks now support bmn-cx3a instances, the first AMD-based instances with accelerated networking on Outposts

AWS announces the availability of bmn-cx3a instances on second-generation AWS Outposts racks. Bmn-cx3a instances feature 5th Gen AMD EPYC processors with a maximum frequency of 4.1 GHz and NVIDIA ConnectX-7 (CX7) network interface cards, delivering up to 800 Gbps of bare-metal accelerated network bandwidth operating at near line rate. Bmn-cx3a instances offer up to 256 cores and 1.5 TB of memory across two sizes, bmn-cx3a.metal-32xl and bmn-cx3a.metal-64xl, with 2x 8 TB NVMe SSD storage. With native Layer 2 (L2) multicast and hardware Precision Time Protocol (PTP) support, bmn-cx3a instances are designed for high-throughput workloads such as real-time market data ingestion and distribution, market and risk analytics, telecom 5G core network applications, and media distribution. Bmn-cx3a instances on AWS Outposts racks are available in all countries and regions where second-generation Outposts racks are supported. For a current list of AWS Regions and countries/territories where Outposts racks are supported, check out the Outposts rack FAQs page.
Quelle: aws.amazon.com

AWS DevOps Agent adds release management capability (preview)

AWS DevOps Agent now offers a release management capability in preview, reviewing code changes for release readiness and running autonomous release testing to help you ship code to production safely and with confidence. With this addition, AWS DevOps Agent now works across both delivery and operations. It accelerates and validates the deployment of code changes, then keeps your applications running optimally across AWS, multicloud, and on-prem environments, so your team ships faster, reduces MTTR, and achieves operational excellence. With release readiness review, AWS DevOps Agent evaluates code changes for production safety during code generation by checking for drift from your internal standards, dependency impacts, and access controls. It maps cross-repository dependencies to surface breaking changes before commit and uses deterministic proofs to review that infrastructure changes do not drift from AWS Well-Architected best practices. With release testing, AWS DevOps Agent generates and runs test plans for web and API-based applications in customer-provisioned environments, catching regressions, UX issues, and integration failures a human reviewer may miss. To get started with the preview, connect your code repositories and pipelines in your AWS DevOps Agent space. AWS DevOps Agent release management is available in the US East (N. Virginia) Region and at no additional cost during the preview period. For the list of AWS Regions where AWS DevOps Agent production operations is available, see the supported Regions table. For pricing of production operations features, which are generally available, see AWS DevOps Agent pricing.
Quelle: aws.amazon.com

Amazon Aurora and RDS for MySQL expand Extended Support for MySQL 5.7 through June 2029

Amazon Aurora MySQL-Compatible Edition and Amazon Relational Database Service (RDS) for MySQL now offer Amazon RDS Extended Support for MySQL 5.7 through June 30, 2029, from the previous end date of February 28, 2027. This applies to Aurora MySQL version 2 (with MySQL 5.7 compatibility) and RDS for MySQL version 5.7, giving customers additional time to plan and complete their upgrades to a supported major version while continuing to receive critical security patches and bug fixes. RDS Extended Support delivers security patches for critical and high CVEs, bug fixes for critical operational issues, and access to AWS Support within the standard Aurora and RDS SLAs. There is no price increase with this extension, and customers using RDS Extended Support for MySQL 5.7 will continue to pay Year 3 pricing through June 30, 2029. For pricing details, see Aurora pricing and RDS for MySQL pricing. We recommend upgrading to MySQL 8.0 or MySQL 8.4 compatible versions to benefit from the latest database features, performance improvements, and security enhancements. You can upgrade using Amazon RDS Blue/Green Deployments, in-place upgrade, or snapshot restore. To learn more, see the Aurora MySQL and RDS for MySQL user guides. This extension is available in all AWS Regions where Aurora MySQL and RDS for MySQL are available. Amazon Aurora is designed for high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Amazon RDS for MySQL, PostgreSQL, and MariaDB make it simple to set up, operate, and scale open source deployments in the cloud. Visit the getting started pages for Aurora and RDS to begin.
Quelle: aws.amazon.com

AWS HealthOmics now streams workflow engine logs to Amazon CloudWatch in real time

AWS HealthOmics now streams workflow engine logs to Amazon CloudWatch in real time, enabling customers to monitor workflow execution progress as it happens. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Real-time engine log streaming accelerates iterative workflow development and debugging by giving researchers, bioinformaticians, and workflow developers immediate access to execution details during a run. The streamed engine logs provide visibility into workflow orchestration events, task scheduling details, import/export activity, and full stack traces on errors — all routed into the engine log stream in real time. Customers can set up CloudWatch alarms on log patterns to detect anomalies early, build dashboards for ongoing monitoring, and integrate with existing observability tooling.
Real-time engine log streaming is now available for Nextflow, WDL, and CWL workflow runs in all AWS HealthOmics regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul). To learn more, visit the Monitoring HealthOmics with CloudWatch Logs documentation.
Quelle: aws.amazon.com

AWS Glue Interactive Sessions now support Spark Connect for interactive workloads

AWS Glue Interactive Sessions now support Apache Spark Connect, using which you can now develop and run Apache Spark applications from your preferred environment, including managed notebooks in Amazon SageMaker Unified Studio, or your preferred notebook environments and IDEs like Jupyter, Visual Studio Code, while running them on AWS Glue’s serverless infrastructure without managing clusters. With Spark Connect, you submit Spark jobs to AWS Glue Interactive Sessions using a thin client architecture that decouples your client application from the Spark execution environment. This unlocks workflows like ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production, all from the tools you already use. Spark Connect also simplifies upgrades and improves stability by isolating client dependencies from the server-side Spark runtime. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management using the AWS Glue API, CLI, or SDK. AWS Glue Interactive Sessions with Spark Connect is available in Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), South America (São Paulo), US East (Ohio, N. Virginia), and US West (Oregon). To get started, connect to Glue Interactive Sessions using Spark Connect from notebooks in Amazon SageMaker Unified Studio, your favorite IDE with a Python interpreter, or the AWS API, SDK, and CLI. To learn more, visit the AWS Glue Interactive Sessions documentation.
Quelle: aws.amazon.com

AWS Transform for mainframe now delivers a traceable reimagine workflow

AWS Transform for mainframe now delivers a connected, traceable reimagine experience from assessment through code generation. Previously, modernizing mainframe applications required months of analysis across multiple tools for discovery, reverse engineering, and code generation with manual handoffs between phases. With this launch, enterprises running z/OS COBOL and PL/I workloads can assess their portfolio to identify the discrete business functions, extract business rules, generate development-ready requirements, and produce traceable cloud-native code in a single connected workflow.
The experience starts with a portfolio assessment, where AWS Transform systematically identifies and catalogs discrete business functions. Selected business functions flow directly into the reimagine workflow, creating a connected path from portfolio analysis through code generation. For each business function, AWS Transform generates development-ready requirements with full traceability, flowing directly into Kiro and other IDEs through MCP-based integrations. Teams can generate interactive documentation for any requirement or code directly in the IDE. Every requirement traces back to the source code, so teams can audit any transformation decision back to its origin. This end-to-end approach compresses what previously took years of manual effort into months of automated, evidence-based modernization.
These capabilities are available in all AWS Regions where AWS Transform for mainframe is available. For more information, see the AWS Region table.
To learn more, visit AWS Transform for mainframe or see the AWS Transform for mainframe documentation.
Quelle: aws.amazon.com

Amazon S3 Vectors now supports up to 10,000 similarity search results per query

Amazon S3 Vectors can now return up to 10,000 similarity search results per query, a 100x increase from the previous limit. The higher result limit helps you retrieve a larger, more comprehensive set of candidates during similarity queries. This is especially valuable for applications with multi-stage retrieval pipelines that need to apply additional processing such as reranking, aggregations, or deduplication to produce a more relevant final result set.
To get started with the higher limit, use the latest AWS SDK and update your application code to specify up to 10,000 relevant results (topK nearest neighbors) when making a QueryVectors API request. Query results are now returned across multiple pages, and you can start processing the first page immediately while retrieving additional pages as needed. For queries that return larger result sets, you pay a small data-returned fee based on the total size of results returned. The first 512 KB of data returned per query is free. For full pricing details, visit the S3 pricing page.
S3 Vectors supports retrieving up to 10,000 results per query in all AWS Regions where it is available. To learn more about S3 Vectors, visit the product page and S3 User Guide.
Quelle: aws.amazon.com