• Categoría: Noticias de Microsoft

mayo 23, 2025

Exploren las perspectivas del Informe sobre la IA en la educación

Por: Equipo de Microsoft Educación.

El rápido auge de la IA generativa ha comenzado a remodelar la forma en que las escuelas abordan la creación, la resolución de problemas, el aprendizaje y la comunicación. Sus escuelas se encuentran en un momento crucial en el que el pensamiento crítico y las habilidades metacognitivas son más importantes que nunca a medida que se desarrollan las nuevas tecnologías.

A medida que continuamos con los aprendizajes, Microsoft cree que es importante compartir los primeros hallazgos de nuestro Informe sobre IA en la educación. En este informe, destacamos las ideas de nuestra investigación, así como las investigaciones de las organizaciones asociadas.

Entre las principales conclusiones del Informe sobre la IA en la Educación se incluyen las siguientes:

• Inicien conversaciones de IA hoy mismo. Existe una necesidad urgente de comunicarse de forma clara y abierta sobre la IA, aumentar la alfabetización en IA y crear directrices de uso en las organizaciones educativas.
• Descubran cómo puede ayudar la IA. Existe una clara oportunidad para que la IA ayude a los educadores y administradores a aligerar las cargas de trabajo, aumentar la productividad y mejorar la eficiencia.
• Exploren nuevas formas de aprender con IA. Los primeros estudios demuestran el potencial de la IA para mejorar las experiencias educativas y los resultados del aprendizaje.
• Prepárense para el lugar de trabajo del futuro. Los estudiantes necesitan desarrollar habilidades interpersonales y capacidad técnica para prepararse para un mundo transformado por la IA.

Exploren el Informe sobre la IA en la educación para obtener recursos y recomendaciones que ayuden a representar las oportunidades que vienen con este momento único.

Inicien conversaciones sobre IA hoy mismo

Cuando se empieza a utilizar herramientas de IA, es habitual empezar por averiguar formas de facilitar las tareas cotidianas. En el ámbito de la educación, la IA también ofrece oportunidades para proporcionar información procesable, mejorar los resultados del aprendizaje y dedicar más tiempo a la conexión y la colaboración humanas. Pero también hay desafíos que navegar y superar para hacer realidad ese potencial. Para comprender mejor las necesidades y oportunidades en torno a la IA en la educación, Microsoft encuestó a educadores, líderes académicos y de TI, y estudiantes de escuelas K-12 e instituciones de educación superior sobre sus percepciones, familiaridad, usos y preocupaciones en torno a las herramientas de IA.

Algunos ejemplos de los resultados de la encuesta incluyen:

• El 47% de los líderes educativos utilizan la IA todos los días
• El 68% de todos los educadores han utilizado la IA al menos una o dos veces
• El 62% de todos los estudiantes han utilizado la IA al menos una o dos veces.

A pesar de la baja familiaridad general con la IA, en especial entre los estudiantes, cabe destacar que los encuestados de cada grupo utilizan la IA. Esta adopción generalizada subraya la necesidad de contar con una orientación clara y marcos prácticos para ayudar a navegar por las complejidades de la IA en la educación. Las preocupaciones sobre las trampas prevalecen en todos los grupos, incluidos los estudiantes, lo que destaca aún más la importancia de establecer una orientación transparente y coherente.

Sigan estos pasos a continuación para iniciar conversaciones sobre IA en su escuela o institución:

1. Soliciten que los líderes de su escuela o distrito creen pautas y políticas claras y brinden oportunidades de aprendizaje profesional. Consideren la posibilidad de compartir el archivo Kit de herramientas TeachAI como un recurso.
2. Ayuden a los estudiantes a aprender a usar la IA de manera responsable sin comprometer su integridad académica a través del establecimiento de expectativas claras.

Formas comunes en que se utilizan las herramientas de IA generativa en las escuelas

La IA puede permitir el aprendizaje personalizado, liberar tiempo para que los educadores se centren en lo que más importa y ayudar a abordar los problemas de equidad y accesibilidad. También puede mejorar la eficiencia operativa, al brindar el soporte que tanto necesitan los administradores y equipos de TI sobrecargados. Existe una clara oportunidad para que la IA ayude a los educadores y administradores a aligerar las cargas de trabajo, aumentar la productividad y mejorar la eficiencia.

Entre los encuestados que afirman utilizar la IA, algunas de las tareas más comunes para las que la utilizan son:

• Los líderes utilizan las herramientas de IA de manera primordial para mejorar la eficiencia de los procesos operativos y administrativos, mejorar el acceso a los recursos, apoyar la comunicación con los estudiantes e identificar oportunidades de mejora para los estudiantes.
• Los educadores utilizan las herramientas de IA, en su mayoría, para crear o actualizar planes de clase, generar nuevas ideas, simplificar temas complejos, liberar su tiempo y diferenciar la enseñanza para satisfacer las necesidades de los estudiantes.
• Los estudiantes utilizan las herramientas de IA de manera principal para resumir información, ayudarlos a hacer lluvias de ideas, obtener respuestas o información de manera rápida, obtener retroalimentación inicial y mejorar sus habilidades de escritura.

Descubran cómo la IA puede ayudar a su escuela

Cada mes, los usuarios más habituales de Microsoft 365 Education reciben cientos de correos electrónicos y mensajes de chat para hacer las cosas. La IA puede permitir una mayor productividad en tareas como la planificación de lecciones y el desarrollo del plan de estudios, que representan el 45% de las responsabilidades de los docentes. Eso libera tiempo para que los educadores hagan las cosas que solo los humanos pueden hacer, como conectarse con los estudiantes.

Las instituciones educativas se mueven con rapidez en lo que respecta a la IA y han comenzado a ver retornos significativos de su inversión. Sin embargo, un estudio de IDC sobre la oportunidad de la IA en la educación descubrió que los líderes educativos se sienten menos preparados para el cambio impulsado por la IA que sus pares en otras industrias.

Las organizaciones educativas pueden tomar estas medidas para aumentar la preparación y desarrollar una estrategia:

• Establecer un comité rector que defina y dirija la estrategia de IA, las políticas de uso responsable, los modelos de gobernanza y las prioridades.
• Prepararse para el cambio mediante la creación de un equipo de IA centralizado y multifuncional que pueda conectar las iniciativas de IA con las prioridades existentes de la organización y crear oportunidades de formación.
• Priorizar los casos de uso de IA de alto valor y baja complejidad. Comiencen con algo pequeño, recopilen y respondan a los comentarios, y planifiquen soluciones escalables e impactantes.

Para obtener más información sobre IDC de un estudio patrocinado por Microsoft, exploren los siguientes recursos:

• Lean el recorrido de la IA en la educación detrás de los titulares
• El impacto de la IA en la educación se extiende mucho más allá de las aulas

Exploren nuevas formas de aprender con IA

Tanto los estudiantes como los educadores ya han descubierto los beneficios de usar la IA generativa en el aula, en particular cuando se usa como un entrenador académico personalizado que fomenta el aprendizaje y la participación en lugar de simplemente dar respuestas.

Exploren estas conclusiones clave de los primeros estudios sobre el impacto potencial de la IA generativa en el aprendizaje:

• En diciembre de 2023, Microsoft Research y Harsh Kumar,  de la Universidad de Toronto, descubrieron que las explicaciones generadas por la IA mejoraban el aprendizaje en comparación con la visualización tan solo de las respuestas correctas. Las ventajas fueron más significativas para los estudiantes que primero intentaron problemas de forma independiente antes de recibir ayuda.
• Un estudio de 2023 realizado por profesores de la Universidad de Harvard y la Universidad de Yale descubrió que los chatbots de IA pueden brindar a los estudiantes de clases grandes una experiencia que se aproxima a una relación ideal uno a uno entre el educador y el estudiante.

Un estudiante compartió que «se sintió como tener un tutor personal… Me encanta cómo los bots de IA responderán preguntas sin ego y sin juicio, por lo general entreteniendo incluso las preguntas más estúpidas sin tratarlas como si fueran estúpidas».

Sigan estos pasos a continuación para explorar cómo la IA puede apoyar el aprendizaje de los estudiantes:

• Modelen y fomenten una mentalidad de crecimiento que incluya aprendizaje, iteración y curiosidad.
• Aprendan de los demás y exploren los recursos educativos de IA.
• Sean intencionales en el diseño de nuevas experiencias de IA. ¿Cuál es su objetivo y cómo podría ayudarlos la IA a alcanzarlo?

Prepárense para el lugar de trabajo del futuro

Los lugares de trabajo, al igual que las aulas, se han visto alterados por el auge de las herramientas de IA generativa. Como resultado, las habilidades que los estudiantes necesitan aprender también han cambiado.

Entre los hallazgos importantes sobre la evolución de las habilidades en el lugar de trabajo se encuentra que el 82% de los líderes encuestados para el Índice de Tendencias Laborales 2023 de Microsoft afirman que los empleados necesitarán nuevas habilidades para estar preparados para el crecimiento de la IA. Y aprender a trabajar junto con la IA no se tratará solo de desarrollar la capacidad técnica. Será necesario priorizar las habilidades interpersonales y las nuevas habilidades analíticas, emocionales y de pensamiento crítico. Según el Informe sobre el futuro del trabajo de LinkedIn de 2023, el 92% de los ejecutivos estadounidenses están de acuerdo en que las habilidades interpersonales son más importantes que nunca.

Sigan estos pasos para ayudar a preparar a sus estudiantes para habilidades que los preparen para el futuro:

• Enseñar a los estudiantes habilidades metacognitivas y centradas en el ser humano, incluida la capacidad de analizar, comprender y controlar sus propios procesos de pensamiento. Pueden empezar con preguntar a los alumnos por qué están de acuerdo o en desacuerdo con el contenido generado por la IA.
• Modelen el uso de herramientas de IA para iniciar el debate y explorar puntos de vista alternativos en lugar de limitarse a proporcionar respuestas.

El rápido ascenso de la IA generativa ha comenzado a revolucionar la forma en que las escuelas fomentan la creatividad, abordan los desafíos y mejoran el aprendizaje. Descubran perspectivas, recursos y recomendaciones en nuestro Informe sobre IA en la educación para aprovechar el potencial de esta era transformadora.

Etiquetas:

• Educación
• IA
• Tecnología

The post Exploren las perspectivas del Informe sobre la IA en la educación appeared first on Source LATAM.

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Amazon Managed Service for Prometheus, a fully managed Prometheus-compatible monitoring service, now provides the capability to identify expensive PromQL queries, and limit their execution. This enables customers to monitor and control the types of queries being issued against their Amazon Managed Service for Prometheus workspaces.

Customers have highlighted the need for tighter governance controls for queries, specifically around high cost queries. You can now monitor queries above a certain Query Samples Processed (QSP) threshold, and log those queries to Amazon CloudWatch. The information in the vended logs allows you to identify expensive queries. The vended logs contain the PromQL query and metadata about where it originated from, such as from Grafana dashboard IDs or alerting rules.
In addition, you can now set warning or error thresholds for query execution. To control query cost, you can pre-empt the execution of expensive queries by providing an error threshold in the HTTP headers to the QueryMetrics API. Alternatively, by setting a warning threshold, we return the query results, charge you for the QSP, and return a warning to the end-user that the query is more expensive than the limit set by your workspace administrator.

This feature is now available in all regions where Amazon Managed Service for Prometheus is generally available.

To learn more about Amazon Managed Service for Prometheus collector, visit the user guide or product page.
 

​Amazon Managed Service for Prometheus, a fully managed Prometheus-compatible monitoring service, now provides the capability to identify expensive PromQL queries, and limit their execution. This enables customers to monitor and control the types of queries being issued against their Amazon Managed Service for Prometheus workspaces. Customers have highlighted the need for tighter governance controls for queries, specifically around high cost queries. You can now monitor queries above a certain Query Samples Processed (QSP) threshold, and log those queries to Amazon CloudWatch. The information in the vended logs allows you to identify expensive queries. The vended logs contain the PromQL query and metadata about where it originated from, such as from Grafana dashboard IDs or alerting rules. In addition, you can now set warning or error thresholds for query execution. To control query cost, you can pre-empt the execution of expensive queries by providing an error threshold in the HTTP headers to the QueryMetrics API. Alternatively, by setting a warning threshold, we return the query results, charge you for the QSP, and return a warning to the end-user that the query is more expensive than the limit set by your workspace administrator. This feature is now available in all regions where Amazon Managed Service for Prometheus is generally available. To learn more about Amazon Managed Service for Prometheus collector, visit the user guide or product page.    

The next generation of Anthropic’s Claude models, Claude Opus 4 and Claude Sonnet 4, are now available in Amazon Bedrock, representing significant advancements in AI capabilities. These models excel at coding, enable AI agents to analyze thousands of data sources, execute long-running tasks, write high-quality content, and perform complex actions. Both Opus 4 and Sonnet 4 are hybrid reasoning models offering two modes: near-instant responses and extended thinking for deeper reasoning.

Claude Opus 4: Opus 4 is Anthropic’s most powerful Claude model to date and Anthropic’s benchmarks show it is the best coding model available, excelling at autonomously managing complex, multi-step tasks with accuracy. It can independently break down abstract projects, plan architectures, and maintain high code quality throughout extended tasks. Opus 4 is ideal for powering agentic AI applications that require uncompromising intelligence for orchestrating cross-functional enterprise workflows or handling a major code migration for a large codebase.

Claude Sonnet 4: Sonnet 4 is a midsize model designed for high-volume use cases and can function effectively as a task-specific sub-agent within broader AI systems. It efficiently handles specific tasks like code generation, search, data analysis, and content synthesis, making it well suited for production AI applications requiring a balance of quality, costeffectiveness, and responsiveness.

You can now use both Claude 4 models in Amazon Bedrock. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information including region availability, see the AWS News Blog, Anthropic’s Claude in Amazon Bedrock product page, and the Amazon Bedrock pricing page.
 

​The next generation of Anthropic’s Claude models, Claude Opus 4 and Claude Sonnet 4, are now available in Amazon Bedrock, representing significant advancements in AI capabilities. These models excel at coding, enable AI agents to analyze thousands of data sources, execute long-running tasks, write high-quality content, and perform complex actions. Both Opus 4 and Sonnet 4 are hybrid reasoning models offering two modes: near-instant responses and extended thinking for deeper reasoning. Claude Opus 4: Opus 4 is Anthropic’s most powerful Claude model to date and Anthropic’s benchmarks show it is the best coding model available, excelling at autonomously managing complex, multi-step tasks with accuracy. It can independently break down abstract projects, plan architectures, and maintain high code quality throughout extended tasks. Opus 4 is ideal for powering agentic AI applications that require uncompromising intelligence for orchestrating cross-functional enterprise workflows or handling a major code migration for a large codebase. Claude Sonnet 4: Sonnet 4 is a midsize model designed for high-volume use cases and can function effectively as a task-specific sub-agent within broader AI systems. It efficiently handles specific tasks like code generation, search, data analysis, and content synthesis, making it well suited for production AI applications requiring a balance of quality, costeffectiveness, and responsiveness. You can now use both Claude 4 models in Amazon Bedrock. To get started, visit the Amazon Bedrock console. Integrate it into your applications using the Amazon Bedrock API or SDK. For more information including region availability, see the AWS News Blog, Anthropic’s Claude in Amazon Bedrock product page, and the Amazon Bedrock pricing page.    

Amazon Relational Database Service (Amazon RDS) Custom for Oracle now supports R7i and M7i instances. These instances are powered by custom 4th Generation Intel Xeon Scalable custom processors, available only on AWS. R7i and M7i instances are available in sizes up to 48xlarge, or 50% larger than the previous generation R6i and M6i instances.

M7i and R7i instances are available for Amazon RDS Custom for Oracle in Bring Your Own License model for Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2) . You can modify your existing RDS instance or create a new instance with just a few clicks on the Amazon RDS Management Console or using the AWS SDK or CLI. Visit Amazon RDS Custom Pricing Page for pricing details and region availability.

Amazon RDS Custom for Oracle is a managed database service for legacy, custom, and packaged applications that require access to the underlying operating system and database environment. To get started with Amazon RDS Custom for Oracle, refer the User Guide.

​Amazon Relational Database Service (Amazon RDS) Custom for Oracle now supports R7i and M7i instances. These instances are powered by custom 4th Generation Intel Xeon Scalable custom processors, available only on AWS. R7i and M7i instances are available in sizes up to 48xlarge, or 50% larger than the previous generation R6i and M6i instances. M7i and R7i instances are available for Amazon RDS Custom for Oracle in Bring Your Own License model for Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2) . You can modify your existing RDS instance or create a new instance with just a few clicks on the Amazon RDS Management Console or using the AWS SDK or CLI. Visit Amazon RDS Custom Pricing Page for pricing details and region availability. Amazon RDS Custom for Oracle is a managed database service for legacy, custom, and packaged applications that require access to the underlying operating system and database environment. To get started with Amazon RDS Custom for Oracle, refer the User Guide.  

Today, AWS Control Tower introduces a new ‘Enabled controls’ page, helping customers track, filter, and manage their enabled controls across their AWS Control Tower organization. This enhancement significantly improves visibility and streamlines the management of your AWS Control Tower controls, saving valuable time and reducing the complexity of managing enabled controls. For organizations managing hundreds or thousands of AWS accounts, this feature provides a centralized view of control coverage, making it easier to maintain consistent governance at scale.

Previously, to assess the enabled controls coverage, you had to navigate to the organizational unit (OU) or account details page in the console to track the controls deployed per target. With this release, the Enabled controls view centralizes all the enabled controls across your AWS Control Tower environment, giving you a single, unified location to track, filter, and manage enabled controls. With this new feature, you can now more easily identify gaps in your control coverage. For instance, you can quickly search and filter for all enabled preventive controls and verify if they’re applied consistently across critical OUs.

You can drill down by organizational units, behavior, severity and implementation to see exactly which controls are enabled, giving you a targeted visibility into your governance posture across your environment. Lastly, you can also get a pre-filtered list of enabled controls by behavior from the AWS Control Tower dashboard’s Controls summary page.

To benefit from the new Enabled controls view page, navigate to the Controls section in your AWS Control Tower console. To learn more, visit the AWS Control Tower homepage or see the AWS Control Tower User Guide. For a full list of AWS Regions where AWS Control Tower is available, see the AWS Region Table.

​Today, AWS Control Tower introduces a new ‘Enabled controls’ page, helping customers track, filter, and manage their enabled controls across their AWS Control Tower organization. This enhancement significantly improves visibility and streamlines the management of your AWS Control Tower controls, saving valuable time and reducing the complexity of managing enabled controls. For organizations managing hundreds or thousands of AWS accounts, this feature provides a centralized view of control coverage, making it easier to maintain consistent governance at scale. Previously, to assess the enabled controls coverage, you had to navigate to the organizational unit (OU) or account details page in the console to track the controls deployed per target. With this release, the Enabled controls view centralizes all the enabled controls across your AWS Control Tower environment, giving you a single, unified location to track, filter, and manage enabled controls. With this new feature, you can now more easily identify gaps in your control coverage. For instance, you can quickly search and filter for all enabled preventive controls and verify if they’re applied consistently across critical OUs. You can drill down by organizational units, behavior, severity and implementation to see exactly which controls are enabled, giving you a targeted visibility into your governance posture across your environment. Lastly, you can also get a pre-filtered list of enabled controls by behavior from the AWS Control Tower dashboard’s Controls summary page. To benefit from the new Enabled controls view page, navigate to the Controls section in your AWS Control Tower console. To learn more, visit the AWS Control Tower homepage or see the AWS Control Tower User Guide. For a full list of AWS Regions where AWS Control Tower is available, see the AWS Region Table.  

AWS HealthImaging announces rich hierarchical search per the DICOMweb QIDO-RS standard as well as an improved data management experience. With this launch, HealthImaging automatically organizes image sets into DICOM Study and Series resources. Incoming DICOM SOP instances are automatically merged to the same DICOM Series.

Rich DICOMweb QIDO-RS search capabilities make it easier to find and retrieve data, enabling customers to focus more on empowering end users and less on infrastructure management. HealthImaging’s automatic organization of data by DICOM Studies and Series makes it easier for healthcare and life sciences customers to manage their data at scale by eliminating the need for post-import workflows, saving time and reducing complexity. This helps customers more efficiently organize data and better resolve any inconsistencies. This launch also delivers significant reductions in the last byte latency of DICOMweb WADO-RS APIs, and faster import of large instances (such as digital pathology whole slide imaging).

AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland).

AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.

​AWS HealthImaging announces rich hierarchical search per the DICOMweb QIDO-RS standard as well as an improved data management experience. With this launch, HealthImaging automatically organizes image sets into DICOM Study and Series resources. Incoming DICOM SOP instances are automatically merged to the same DICOM Series. Rich DICOMweb QIDO-RS search capabilities make it easier to find and retrieve data, enabling customers to focus more on empowering end users and less on infrastructure management. HealthImaging’s automatic organization of data by DICOM Studies and Series makes it easier for healthcare and life sciences customers to manage their data at scale by eliminating the need for post-import workflows, saving time and reducing complexity. This helps customers more efficiently organize data and better resolve any inconsistencies. This launch also delivers significant reductions in the last byte latency of DICOMweb WADO-RS APIs, and faster import of large instances (such as digital pathology whole slide imaging). AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland). AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.  

Today, AWS HealthImaging announces support for retrieving the metadata for all DICOM instances in a series via a single API action. This new feature extends HealthImaging’s support for the DICOMweb standard, simplifying integrations and improving interoperability with existing applications.

This launch significantly reduces the cost and complexity of retrieving series level metadata, especially when DICOM series contain hundreds or even thousands of instances. With this enhancement, it is easier than ever to retrieve instance metadata with consistent low latency, enabling clinical, AI, and research use cases.

AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland).

AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.

​Today, AWS HealthImaging announces support for retrieving the metadata for all DICOM instances in a series via a single API action. This new feature extends HealthImaging’s support for the DICOMweb standard, simplifying integrations and improving interoperability with existing applications. This launch significantly reduces the cost and complexity of retrieving series level metadata, especially when DICOM series contain hundreds or even thousands of instances. With this enhancement, it is easier than ever to retrieve instance metadata with consistent low latency, enabling clinical, AI, and research use cases. AWS HealthImaging is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), and Europe (Ireland). AWS HealthImaging is a HIPAA-eligible service that empowers healthcare providers, life sciences researchers, and their software partners to store, analyze, and share medical images at petabyte scale. To learn more, see the AWS HealthImaging Developer Guide.  

Amazon Aurora for MySQL and Amazon Aurora for PostgreSQL now offer faster Global Database cross-Region switchover, reducing recovery time for read/write operations to typically under 30 seconds and enhancing availability for applications operating at a global scale.

With Global Database, a single Aurora cluster can span multiple AWS Regions, providing disaster recovery from Region-wide outages and enabling fast local reads for globally distributed applications. Global Database cross-Region switchover is a fully managed process designed for planned events such as regional rotations. This launch optimizes the duration during which a writer in your global cluster is unavailable, improving recovery time and business continuity for your applications following cross-Region switchover operations. See documentation to learn more about Global Database Switchover.

To access these improvements for Aurora MySQL, upgrade your cluster to version 3.09 (compatible with MySQL 8.0.40) or higher. For Aurora PostgreSQL, upgrade your cluster to versions 16.8, 15.12, 14.17, 13.20, or higher. Once upgraded, the faster switchover capabilities are automatically available for your cluster without any additional configuration. See upgrading an Amazon Aurora global database guide to learn about upgrading your cluster.

Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started with Amazon Aurora, take a look at our getting started page.

​Amazon Aurora for MySQL and Amazon Aurora for PostgreSQL now offer faster Global Database cross-Region switchover, reducing recovery time for read/write operations to typically under 30 seconds and enhancing availability for applications operating at a global scale. With Global Database, a single Aurora cluster can span multiple AWS Regions, providing disaster recovery from Region-wide outages and enabling fast local reads for globally distributed applications. Global Database cross-Region switchover is a fully managed process designed for planned events such as regional rotations. This launch optimizes the duration during which a writer in your global cluster is unavailable, improving recovery time and business continuity for your applications following cross-Region switchover operations. See documentation to learn more about Global Database Switchover. To access these improvements for Aurora MySQL, upgrade your cluster to version 3.09 (compatible with MySQL 8.0.40) or higher. For Aurora PostgreSQL, upgrade your cluster to versions 16.8, 15.12, 14.17, 13.20, or higher. Once upgraded, the faster switchover capabilities are automatically available for your cluster without any additional configuration. See upgrading an Amazon Aurora global database guide to learn about upgrading your cluster. Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started with Amazon Aurora, take a look at our getting started page.  

EC2 Public DNS names can now resolve to IPv6 Global Unicast Address (AAAA record) associated with your EC2 instances and Elastic Network Interfaces (ENI). This allows customers to publicly access their IPv6-enabled Amazon EC2 instances over IPv6, using EC2 Public DNS names.

Prior to this, EC2 Public DNS name resolved to the Public IPv4 address (A record) associated with the primary ENI of the instance. So, customers adopting IPv6, used the specific IPv6 address instead of a DNS name to access an IPv6-only Amazon EC2 instance, or used a custom domain by creating a hosted zone using Amazon Route 53. IPv6 support for EC2 Public DNS names allows customers to easily access their IPv6-only Amazon EC2 instances, or formulate a migration plan that allows them to access a dual stack instance via IPv6, with a simple DNS cut over.

This feature is available in all AWS commercial and AWS GovCloud (US) Regions, and customers can set IPv6 support for EC2 Public DNS using the same VPC settings that customers use to enable IPv4-only EC2 Public DNS name today. To learn more about using IPv6 support for EC2 Public DNS name, please refer to our documentation.
 

​EC2 Public DNS names can now resolve to IPv6 Global Unicast Address (AAAA record) associated with your EC2 instances and Elastic Network Interfaces (ENI). This allows customers to publicly access their IPv6-enabled Amazon EC2 instances over IPv6, using EC2 Public DNS names. Prior to this, EC2 Public DNS name resolved to the Public IPv4 address (A record) associated with the primary ENI of the instance. So, customers adopting IPv6, used the specific IPv6 address instead of a DNS name to access an IPv6-only Amazon EC2 instance, or used a custom domain by creating a hosted zone using Amazon Route 53. IPv6 support for EC2 Public DNS names allows customers to easily access their IPv6-only Amazon EC2 instances, or formulate a migration plan that allows them to access a dual stack instance via IPv6, with a simple DNS cut over. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, and customers can set IPv6 support for EC2 Public DNS using the same VPC settings that customers use to enable IPv4-only EC2 Public DNS name today. To learn more about using IPv6 support for EC2 Public DNS name, please refer to our documentation.    

Amazon Aurora Global Database now supports adding up to 10 secondary Regions to your global cluster, further enhancing scalability and availability for globally distributed applications.

With Global Database, a single Aurora cluster can span multiple AWS Regions, providing disaster recovery from Region-wide outages and enabling fast local reads for globally distributed applications. This launch increases the number of secondary Regions that can be added to a global cluster from the previously supported limit of up to 5 secondary Regions to up to 10 secondary Regions, providing a larger global footprint for operating your applications See documentation to learn more about Global Database.

Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started with Amazon Aurora, take a look at our getting started page.
 

​Amazon Aurora Global Database now supports adding up to 10 secondary Regions to your global cluster, further enhancing scalability and availability for globally distributed applications. With Global Database, a single Aurora cluster can span multiple AWS Regions, providing disaster recovery from Region-wide outages and enabling fast local reads for globally distributed applications. This launch increases the number of secondary Regions that can be added to a global cluster from the previously supported limit of up to 5 secondary Regions to up to 10 secondary Regions, providing a larger global footprint for operating your applications See documentation to learn more about Global Database. Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started with Amazon Aurora, take a look at our getting started page.