Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Frankfurt) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances.
M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements.
M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets.
To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
Starting today, the general-purpose Amazon EC2 M8a instances are available in AWS Europe (Frankfurt) region. M8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to M7a instances. M8a instances deliver 45% more memory bandwidth compared to M7a instances, making these instances ideal for even latency sensitive workloads. M8a instances deliver even higher performance gains for specific workloads. M8a instances are up to 60% faster for GroovyJVM benchmark, and up to 39% faster for Cassandra benchmark compared to Amazon EC2 M7a instances. M8a instances are SAP-certified and offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. M8a instances are built using the latest sixth generation AWS Nitro Cards and ideal for applications that benefit from high performance and high throughput such as financial applications, gaming, rendering, application servers, simulation modeling, mid-size data stores, application development environments, and caching fleets. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 M8a instance page.
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.
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.
AWS AppConfig today launched a new integration that enables automated, intelligent rollbacks during feature flag and dynamic configuration deployments using New Relic Workflow Automation. Building on AWS AppConfig’s third-party alert capability, this integration provides teams using New Relic with a solution to automatically detect degraded application health and trigger rollbacks in seconds, eliminating manual intervention.
When you deploy feature flags using AWS AppConfig’s gradual deployment strategy, the AWS AppConfig New Relic Extension continuously monitors your application health against configured alert conditions. If issues are detected during a feature flag update and deployment, such as increased error rates or elevated latency, the New Relic Workflow automatically sends a notification to trigger an immediate rollback, reverting the feature flag to its previous state. This closed-loop automation reduces the time between detection and remediation from minutes to seconds, minimizing customer impact during failed deployments.
AWS AppConfig today launched a new integration that enables automated, intelligent rollbacks during feature flag and dynamic configuration deployments using New Relic Workflow Automation. Building on AWS AppConfig’s third-party alert capability, this integration provides teams using New Relic with a solution to automatically detect degraded application health and trigger rollbacks in seconds, eliminating manual intervention. When you deploy feature flags using AWS AppConfig’s gradual deployment strategy, the AWS AppConfig New Relic Extension continuously monitors your application health against configured alert conditions. If issues are detected during a feature flag update and deployment, such as increased error rates or elevated latency, the New Relic Workflow automatically sends a notification to trigger an immediate rollback, reverting the feature flag to its previous state. This closed-loop automation reduces the time between detection and remediation from minutes to seconds, minimizing customer impact during failed deployments.
Microsoft Sovereign Cloud añade gobernanza, productividad y soporte para grandes modelos de IA que se ejecutan de forma segura incluso cuando están desconectados por completo
Por: Douglas Phillips, presidente y director de tecnología, Microsoft Specialized Clouds.
A medida que la soberanía digital se convierte en un requisito estratégico, las organizaciones se replantean cómo despliegan infraestructuras críticas y capacidades de IA bajo expectativas regulatorias más estrictas y condiciones de mayor riesgo. El enfoque de Microsoft hacia la soberanía se basa en permitir que las empresas, los sectores públicos y las industrias reguladas participen en la economía digital de manera segura, independiente y en sus propios términos. Microsoft Sovereign Cloud (Nube Soberana de Microsoft) combina cargas de productividad, seguridad y nube para abarcar tanto entornos públicos como privados. Los clientes pueden elegir la postura de control adecuada para cada carga de trabajo, a través de un continuo de opciones soberanas que protegen contra la fragmentación de su arquitectura o el aumento del riesgo operativo. La confianza se construye con certeza: la certeza en que los datos permanecen protegidos, los controles son aplicables y las operaciones pueden continuar en condiciones reales.
Para soportar estos entornos confidenciales, Microsoft ofrece capacidades full stack que soportan a los clientes en modos conectados, conectados de manera intermitente y desconectados por completo. La expansión actual de las capacidades incluye tres actualizaciones principales:
Operaciones desconectadas de Azure Local (ya disponible) – Las organizaciones pueden ahora ejecutar infraestructuras críticas con gobernanza y control de políticas de Azure, sin conectividad en la nube, lo que optimiza la continuidad para entornos soberanos, clasificados o aislados.
Microsoft 365 Local desconectado (ya disponible) – Las cargas de trabajo principales de productividad, Exchange Server, SharePoint Server y Skype for Business Server pueden funcionar por completo dentro del límite operativo soberano del cliente en Azure Local, para mantener a los equipos productivos incluso cuando están desconectados de la nube.
Foundry Local añade capacidades modernas de infraestructura y soporte para grandes modelos de IA – Las organizaciones ahora pueden llevar grandes modelos de IA a entornos desconectados por completo y soberanos con Foundry Local. A través de infraestructuras modernas de socios como NVIDIA, los clientes con necesidades soberanas podrán ahora ejecutar modelos multimodales a nivel local en su propio hardware, dentro de límites soberanos estrictos, lo que permitirá una inferencia de IA local y potente en entornos desconectados por completo.
Funciona conectada o desconectada por completo. Sovereign Private Cloud unifica Azure Local, Microsoft 365 Local y Foundry Local, para aportar infraestructuras modernas, productividad y soporte para grandes modelos de IA a cualquier límite operativo.
Esto ofrece una experiencia full stack en verdad localizada basada en la infraestructura de Azure Local y cargas de trabajo Microsoft 365 Local, diseñada para mantenerse resiliente en cualquier condición de conectividad, con modelos grandes que forman parte de Foundry Local que amplía la pila para ejecutar modelos multimodales avanzados a nivel local, de forma segura, incluso cuando están desconectados por completo. Los clientes ahora pueden ayudar a mantener operaciones ininterrumpidas, proteger las cargas de trabajo críticas para la misión y aplicar una gobernanza y aplicación de políticas coherentes, para mantener los datos, identidades y operaciones dentro de sus límites soberanos.
Azure Local ejecuta infraestructuras críticas a nivel local, incluso cuando está desconectado
Para cargas de trabajo con requisitos especializados, Azure Local proporciona a la base local controles de gobernanza y políticas de Azure coherentes. Con Azure Local operaciones desconectadas, la gestión, la ejecución de políticas y de carga de trabajo permanecen dentro de los entornos operados por el cliente, por lo que los servicios continúan en funcionamiento de manera segura incluso cuando los entornos deben aislarse o no hay conectividad disponible. A través de experiencias familiares con Azure y políticas consistentes, las organizaciones pueden desplegar y gobernar cargas de trabajo a nivel local sin depender de una conexión continua a servicios públicos en la nube. Azure Local está diseñado para escalar con necesidades críticas para la misión, desde despliegues más pequeños hasta espacios de mayor tamaño que soportan cargas de trabajo intensivas en datos e impulsadas por IA. Los clientes pueden empezar rápido, expandirse con el tiempo y mantener un modelo operativo unificado, todo dentro de sus límites soberanos.
Operar en entornos desconectados aporta restricciones que van más allá de las suposiciones tradicionales en la nube: las dependencias externas pueden ser inaceptables, la conectividad puede estar restringida de manera intencionada y la continuidad operativa es un imperativo empresarial.
«La disponibilidad de operaciones desconectadas de Azure Local representa un avance para las organizaciones que necesitan control sobre sus datos sin sacrificar el poder de Microsoft Cloud. Para Luxemburgo, donde la soberanía digital no es solo un principio sino una necesidad estratégica, este modelo ofrece la resiliencia, autonomía y confianza que nuestro mercado espera. Al combinar el liderazgo tecnológico de Microsoft con la experiencia soberana en la nube de Proximus NXT, permitimos que nuestros clientes innoven con confianza — incluso en modo desconectado por completo», dijo Gerard Hoffmann, CEO de Proximus Luxemburgo.
Microsoft 365 Local mantiene la productividad y la colaboración disponibles en entornos desconectados por completo
A medida que los entornos soberanos se trasladan a entornos desconectados, mantener a las personas productivas se vuelve tan crítico como mantener la infraestructura en línea. Basándose en más de una década de entrega y soporte de estos servicios, Microsoft 365 Local desconectado aporta esa continuidad a la capa de productividad, para entregar las cargas de trabajo principales de Microsoft — Exchange Server, SharePoint Server y Skype for Business Server soportadas al menos hasta 2035 — directo en la nube privada soberana del cliente.
Con Microsoft 365 Local, los equipos pueden comunicarse, compartir información y colaborar de manera segura dentro del mismo límite controlado que su infraestructura y cargas de trabajo de IA. Todo se ejecuta a nivel local, bajo políticas propiedad del cliente, con control total de la resiliencia, acceso y cumplimiento de los datos. Al operar con una gestión y gobernanza consistentes con Azure, los clientes obtienen la experiencia de productividad en la que confían, diseñada para mantenerse resilientes y seguros incluso cuando están desconectados.
Llevar grandes modelos e infraestructura moderna a Foundry Local
Con la disponibilidad de modelos más grandes e infraestructuras modernas como parte del portafolio de Foundry Local, Microsoft permite a los clientes con entornos de una alta seguridad, ejecutar modelos multimodales y grandes directo dentro de sus entornos de nube privada soberana. Esto aporta la riqueza de las capacidades de IA empresarial de Microsoft a los sistemas locales, con inferencias locales y APIs que operan por completo dentro de los límites de datos controlados por el cliente.
Al ampliar más allá de los modelos pequeños, la integración de Foundry Local con Azure Local está diseñada en específico para soportar modelos a gran escala que utilizan las últimas GPUs de socios como NVIDIA. Microsoft proporcionará soporte integral para despliegues, actualizaciones y salud operativa. Aunque las demandas de inferencia aumentan con el tiempo, los clientes mantienen el control total sobre sus datos y hardware.
Elección y control sin complicaciones añadidas
Los clientes que enfrentan estrictas soberanías y requisitos regulatorios dejan claro que una nube privada soberana desconectada por completo es una necesidad empresarial clave. Microsoft Sovereign Private Cloud está diseñada para satisfacer estas necesidades de forma directa, para permitir operaciones seguras y que cumplan con las normas, incluso en entornos sin conectividad externa. Al mismo tiempo, reconocemos que los entornos desconectados no son talla única; algunos clientes operan en modos conectados, híbridos y desconectados según la misión, el riesgo y la regulación. Nuestro enfoque ayuda a los clientes a cumplir con los estrictos requisitos soberanos en escenarios desconectados por completo sin comprometer la simplicidad, al mantener la flexibilidad cuando la conectividad es posible. En conjunto, Azure Local desconecta las operaciones, Microsoft 365 Local y Foundry Local ayudan a las organizaciones a elegir dónde se ejecutan las cargas de trabajo y cómo se gestionan los entornos, al tiempo que estandarizan las prácticas de gobernanza y operaciones entre despliegues conectados y desconectados.
Comiencen ahora
Azure Local desconectado y Microsoft 365 Local desconectado están ahora disponibles en todo el mundo, y los modelos grandes en Foundry Local están disponibles para clientes cualificados.
Douglas Phillips lidera los esfuerzos globales de ingeniería para las nubes especializadas, soberanas y privadas de Microsoft. Es responsable de la estrategia global, los productos y las operaciones de Microsoft que llevan las soluciones líderes en la industria de Microsoft, incluido Azure, nuestro portafolio de nube adaptativa y la suite de colaboración Microsoft 365, a clientes con requisitos adicionales de soberanía, seguridad, borde y cumplimiento.
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AWS announces the launch of source document references for Automated Reasoning policies, simplifying the task of reviewing and refining an Automated Reasoning policy. Automated Reasoning checks uses formal verification techniques to validate that content generated by foundation models is compliant with an Automated Reasoning policy. Automated Reasoning checks deliver up to 99% accuracy at detecting correct responses from LLMs, giving you provable assurance in detecting AI hallucinations while also assisting with ambiguity detection in model responses.
To create Automated Reasoning policies, users upload documents that describe the rules in a knowledge domain like HR policies or financial transaction approval guidelines. These documents are translated into a collection of formal logic rules and variables called an Automated Reasoning policy. With source document references, users can now review the generated policy rules and variables using references to content they are familiar with from the original document,
Test generation for Automated Reasoning checks is now available in the US (N. Virginia), US (Ohio), US (Oregon), Europe (Frankfurt), Europe (Ireland), and Europe (Paris) Regions. Customers can access the service through the Amazon Bedrock console, as well as the Amazon Bedrock Python SDK.
AWS announces the launch of source document references for Automated Reasoning policies, simplifying the task of reviewing and refining an Automated Reasoning policy. Automated Reasoning checks uses formal verification techniques to validate that content generated by foundation models is compliant with an Automated Reasoning policy. Automated Reasoning checks deliver up to 99% accuracy at detecting correct responses from LLMs, giving you provable assurance in detecting AI hallucinations while also assisting with ambiguity detection in model responses. To create Automated Reasoning policies, users upload documents that describe the rules in a knowledge domain like HR policies or financial transaction approval guidelines. These documents are translated into a collection of formal logic rules and variables called an Automated Reasoning policy. With source document references, users can now review the generated policy rules and variables using references to content they are familiar with from the original document, Test generation for Automated Reasoning checks is now available in the US (N. Virginia), US (Ohio), US (Oregon), Europe (Frankfurt), Europe (Ireland), and Europe (Paris) Regions. Customers can access the service through the Amazon Bedrock console, as well as the Amazon Bedrock Python SDK. To learn more about Automated Reasoning checks and how you can integrate it into your generative AI workflows, please read the Amazon Bedrock documentation, review the tutorials on the AWS AI blog, and visit the Bedrock Guardrails webpage.
Introducing Probe API, a powerful and free metadata analysis tool for AWS Elemental MediaConvert. Optimized for efficiency, Probe API reads header metadata to quickly return essential information about your media files, including codec specifications, pixel formats, color space details, and container information – all without waiting to process the actual video content. This analysis capability makes it an invaluable tool for content creators, developers, and media professionals who need to quickly validate files, automate workflows, or utilize Elementals’ Step Functions to make encoding decisions based on source material characteristics.
For complete implementation details and usage examples, please visit the MediaConvert API Reference documentation. The Probe API can be utilized in any region where AWS Elemental MediaConvert is available, making it a versatile tool for streamlining your media workflow analysis.
To get started with Probe API and explore its capabilities, visit the AWS Elemental MediaConvert product page or consult the User Guide for comprehensive documentation.
Introducing Probe API, a powerful and free metadata analysis tool for AWS Elemental MediaConvert. Optimized for efficiency, Probe API reads header metadata to quickly return essential information about your media files, including codec specifications, pixel formats, color space details, and container information – all without waiting to process the actual video content. This analysis capability makes it an invaluable tool for content creators, developers, and media professionals who need to quickly validate files, automate workflows, or utilize Elementals’ Step Functions to make encoding decisions based on source material characteristics. For complete implementation details and usage examples, please visit the MediaConvert API Reference documentation. The Probe API can be utilized in any region where AWS Elemental MediaConvert is available, making it a versatile tool for streamlining your media workflow analysis. To get started with Probe API and explore its capabilities, visit the AWS Elemental MediaConvert product page or consult the User Guide for comprehensive documentation.
AWS Trusted Advisor has enhanced its detection of unused NAT Gateway checks powered by AWS Compute Optimizer detection capabilities. The enhanced detection analyzes additional CloudWatch metrics over a 32-day lookback period and verifies whether NAT Gateways are associated with route tables, reducing false positives by avoiding flagging critical backup resources. This helps cost optimization teams and DevOps engineers confidently identify and remove unused NAT Gateways that incur unnecessary charges.
Each recommendation includes estimated monthly cost savings, enabling you to prioritize cleanup based on monetary impact. With these recommendations, you can run regular cost audits to catch idle NAT Gateways before charges accumulate. This simplifies cleaning up resources left behind after workload migrations or decommissions. You can view and act on these recommendations in the Trusted Advisor console alongside your other cost optimization checks, or through Trusted Advisor APIs.
This feature is available in all AWS Regions where AWS Trusted Advisor is supported. Organizations must be opted-in to Cost Optimization Hub and Compute Optimizer to access these enhanced recommendations. To learn more, visit the AWS Trusted Advisor documentation.
AWS Trusted Advisor has enhanced its detection of unused NAT Gateway checks powered by AWS Compute Optimizer detection capabilities. The enhanced detection analyzes additional CloudWatch metrics over a 32-day lookback period and verifies whether NAT Gateways are associated with route tables, reducing false positives by avoiding flagging critical backup resources. This helps cost optimization teams and DevOps engineers confidently identify and remove unused NAT Gateways that incur unnecessary charges. Each recommendation includes estimated monthly cost savings, enabling you to prioritize cleanup based on monetary impact. With these recommendations, you can run regular cost audits to catch idle NAT Gateways before charges accumulate. This simplifies cleaning up resources left behind after workload migrations or decommissions. You can view and act on these recommendations in the Trusted Advisor console alongside your other cost optimization checks, or through Trusted Advisor APIs. This feature is available in all AWS Regions where AWS Trusted Advisor is supported. Organizations must be opted-in to Cost Optimization Hub and Compute Optimizer to access these enhanced recommendations. To learn more, visit the AWS Trusted Advisor documentation.
Today, AWS announces the general availability of Amazon Q Developer artifacts in the AWS Management Console. Amazon Q artifacts is a generative AI-based user experience that enables customers to visualize resource data in tables and cost data in charts. The launch also moves the Q icon to the navigation bar and the chat panel to the left, making Amazon Q easier to use and find.
Customers can access Amazon Q artifacts by selecting the Amazon Q icon from the navigation bar in the AWS Management Console. They can ask questions about their AWS resources to understand the state of their resources and costs using Amazon Q artifacts. For example, on asking “List S3 buckets with tag value production», Amazon Q displays the S3 buckets that has a tag value of production in a tabular format. Customers can then select the hyperlinks on the bucket name to view the bucket details in the S3 console. Customers can also visualize cost and billing information with charts. For example, on entering «Show me RDS costs by instance type over the last 6 months», Q will render the response in a Q artifacts using a chart (e.g., bar graph, line chart, pie chart, or area chart). Customers can also use sample prompts in the Prompt Library in the Amazon Q chat panel to get started quickly. The artifacts are displayed in an artifact panel next to the Amazon Q chat panel, which is now on the left. Users can expand Amazon Q to full-screen for a dedicated focus mode experience.
The Amazon Q Developer artifacts are available in all AWS Regions where Amazon Q Developer is available. To get started visit Amazon Q Developer documentation.
Today, AWS announces the general availability of Amazon Q Developer artifacts in the AWS Management Console. Amazon Q artifacts is a generative AI-based user experience that enables customers to visualize resource data in tables and cost data in charts. The launch also moves the Q icon to the navigation bar and the chat panel to the left, making Amazon Q easier to use and find. Customers can access Amazon Q artifacts by selecting the Amazon Q icon from the navigation bar in the AWS Management Console. They can ask questions about their AWS resources to understand the state of their resources and costs using Amazon Q artifacts. For example, on asking “List S3 buckets with tag value production», Amazon Q displays the S3 buckets that has a tag value of production in a tabular format. Customers can then select the hyperlinks on the bucket name to view the bucket details in the S3 console. Customers can also visualize cost and billing information with charts. For example, on entering «Show me RDS costs by instance type over the last 6 months», Q will render the response in a Q artifacts using a chart (e.g., bar graph, line chart, pie chart, or area chart). Customers can also use sample prompts in the Prompt Library in the Amazon Q chat panel to get started quickly. The artifacts are displayed in an artifact panel next to the Amazon Q chat panel, which is now on the left. Users can expand Amazon Q to full-screen for a dedicated focus mode experience. The Amazon Q Developer artifacts are available in all AWS Regions where Amazon Q Developer is available. To get started visit Amazon Q Developer documentation.
AWS Elemental Media Services are now available in the Asia Pacific (Malaysia) Region, expanding your ability to build cloud-based video workflows closer to your audiences in Southeast Asia. AWS Elemental MediaConnect, MediaLive, MediaPackage, MediaConvert, and MediaTailor form a comprehensive suite of services that enable you to ingest, transport, transcode, package, and deliver high-quality video content with lower latency and improved performance for viewers across Malaysia and the broader Southeast Asia region.
With this regional expansion, broadcasters, content providers, and streaming platforms in the region can now process video workloads closer to the edge reducing latency and improving video quality for regional audiences. For example, you can use MediaLive or MediaConvert to encode live sports events or VOD assets, respectively, in Kuala Lumpur, package the streams with MediaPackage for multi-device delivery, and monetize content using MediaTailor’s server-side ad insertion, all while keeping your video processing infrastructure within the Asia Pacific (Malaysia) Region.
AWS Elemental Media Services are a family of services that form the foundation of cloud-based workflows to transport, transcode, package, and deliver video.
Visit the AWS region table for a full list of AWS Regions where Elemental Media Services are available.
AWS Elemental Media Services are now available in the Asia Pacific (Malaysia) Region, expanding your ability to build cloud-based video workflows closer to your audiences in Southeast Asia. AWS Elemental MediaConnect, MediaLive, MediaPackage, MediaConvert, and MediaTailor form a comprehensive suite of services that enable you to ingest, transport, transcode, package, and deliver high-quality video content with lower latency and improved performance for viewers across Malaysia and the broader Southeast Asia region. With this regional expansion, broadcasters, content providers, and streaming platforms in the region can now process video workloads closer to the edge reducing latency and improving video quality for regional audiences. For example, you can use MediaLive or MediaConvert to encode live sports events or VOD assets, respectively, in Kuala Lumpur, package the streams with MediaPackage for multi-device delivery, and monetize content using MediaTailor’s server-side ad insertion, all while keeping your video processing infrastructure within the Asia Pacific (Malaysia) Region. AWS Elemental Media Services are a family of services that form the foundation of cloud-based workflows to transport, transcode, package, and deliver video. Visit the AWS region table for a full list of AWS Regions where Elemental Media Services are available.
Amazon S3 Tables are now available in AWS GovCloud (US-East) and AWS GovCloud (US-West).
Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, offering optimized tabular data storage at scale. S3 Tables are designed to perform continual table maintenance to automatically optimize query efficiency and storage cost over time, even as your data lake scales and evolves. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, with the Intelligent-Tiering storage class, S3 Tables automatically manage costs based on access patterns, without performance impact or operational overhead.
Amazon S3 Tables are now available in AWS GovCloud (US-East) and AWS GovCloud (US-West). Amazon S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, offering optimized tabular data storage at scale. S3 Tables are designed to perform continual table maintenance to automatically optimize query efficiency and storage cost over time, even as your data lake scales and evolves. With S3 Tables support for the Apache Iceberg standard, your tabular data can be easily queried by popular AWS and third-party query engines. Additionally, with the Intelligent-Tiering storage class, S3 Tables automatically manage costs based on access patterns, without performance impact or operational overhead.
For a full list of AWS Regions where S3 Tables are available, see S3 Tables AWS Regions and endpoints. To learn more, visit the product page, documentation, and the Amazon S3 pricing page.
