The Amazon SageMaker lakehouse architecture now supports tag based access control (TBAC) for managing fine-grained data access across federated catalogs. This capability, previously available only for default AWS Glue Data Catalog resources, is now available across Amazon S3 Tables, Amazon Redshift data warehouses, and federated data sources including Amazon DynamoDB, PostgreSQL, and SQL Server. TBAC enables simplified permission management by logically grouping catalog resources using tags, allows scaling permissions across datasets with a minimal set of permissions, and also facilitates data sharing across different accounts.
TBAC simplifies how administrators manage data access permissions by replacing direct resource-level permissions with tag-based grants. Instead of manually assigning permissions to individual tables or columns, administrators can now efficiently control access through tags that are automatically inherited by resources. This inheritance feature ensures that new tables automatically receive appropriate fine-grained access controls without additional policy modifications.
You can get started with TBAC through the AWS Lake Formation console. Create tags using key-value pairs, associate them with databases, tables, or columns, and grant permissions to principals based on specific tags. Users can then access tagged resources through Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon SageMaker Unified Studio.
This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in all commercial AWS Regions. To get started, read the blog and visit the Lake Formation Tags documentation.
The Amazon SageMaker lakehouse architecture now supports tag based access control (TBAC) for managing fine-grained data access across federated catalogs. This capability, previously available only for default AWS Glue Data Catalog resources, is now available across Amazon S3 Tables, Amazon Redshift data warehouses, and federated data sources including Amazon DynamoDB, PostgreSQL, and SQL Server. TBAC enables simplified permission management by logically grouping catalog resources using tags, allows scaling permissions across datasets with a minimal set of permissions, and also facilitates data sharing across different accounts. TBAC simplifies how administrators manage data access permissions by replacing direct resource-level permissions with tag-based grants. Instead of manually assigning permissions to individual tables or columns, administrators can now efficiently control access through tags that are automatically inherited by resources. This inheritance feature ensures that new tables automatically receive appropriate fine-grained access controls without additional policy modifications. You can get started with TBAC through the AWS Lake Formation console. Create tags using key-value pairs, associate them with databases, tables, or columns, and grant permissions to principals based on specific tags. Users can then access tagged resources through Amazon Athena, Amazon Redshift, Amazon EMR, or Amazon SageMaker Unified Studio. This feature is available through the AWS Management Console, AWS CLI, and AWS SDKs in all commercial AWS Regions. To get started, read the blog and visit the Lake Formation Tags documentation.
Today, we are excited to announce support for Stop/Start in Amazon Neptune Analytics, a new capability that enables organizations to pause and resume their graph workloads on demand,helping reduce costs during idle periods without losing data or configuration.
Many customers use Neptune Analytics for periodic graph workloads such as fraud detection, recommendation engines, or research simulations that run periodically. Until now, customers had to choose between keeping their Neptune Analytics graphs online even when not in use or deleting and recreating them each time they were needed. This approach was not only expensive, but also time-consuming, requiring manual infrastructure management, repeated data imports, and updates to downstream pipelines to accommodate each newly created graph. This adds significant operational overhead and complexity to their analytics workflows. With Stop/Start, customers can now pause a graph workload via the AWS Console, CLI, or API, and resume it later with a single action. While the graph is stopped, they pay only 10% of the normal compute cost, and all data and settings are preserved without needing to delete or rebuild graphs.
This feature is particularly valuable for cost-conscious startups, research teams, and enterprises with analytics workloads. It simplifies lifecycle management and unlocks experimentation at lower price points. Stop/Start for Neptune Analytics is available in all commercial regions where Neptune Analytics is offered. You can start using this feature today via the Neptune Analytics console, AWS CLI, or AWS SDKs. To learn more, visit the documentation and the pricing page.
Today, we are excited to announce support for Stop/Start in Amazon Neptune Analytics, a new capability that enables organizations to pause and resume their graph workloads on demand,helping reduce costs during idle periods without losing data or configuration. Many customers use Neptune Analytics for periodic graph workloads such as fraud detection, recommendation engines, or research simulations that run periodically. Until now, customers had to choose between keeping their Neptune Analytics graphs online even when not in use or deleting and recreating them each time they were needed. This approach was not only expensive, but also time-consuming, requiring manual infrastructure management, repeated data imports, and updates to downstream pipelines to accommodate each newly created graph. This adds significant operational overhead and complexity to their analytics workflows. With Stop/Start, customers can now pause a graph workload via the AWS Console, CLI, or API, and resume it later with a single action. While the graph is stopped, they pay only 10% of the normal compute cost, and all data and settings are preserved without needing to delete or rebuild graphs. This feature is particularly valuable for cost-conscious startups, research teams, and enterprises with analytics workloads. It simplifies lifecycle management and unlocks experimentation at lower price points. Stop/Start for Neptune Analytics is available in all commercial regions where Neptune Analytics is offered. You can start using this feature today via the Neptune Analytics console, AWS CLI, or AWS SDKs. To learn more, visit the documentation and the pricing page.
AWS is announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) storage optimized I8ge instances. I8ge instances are powered by AWS Graviton4 processors to deliver up to 60% better compute performance compared to previous generation Graviton2-based storage optimized Amazon EC2 instances. I8ge instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 55% better real-time storage performance per TB while offering up to 60% lower storage I/O latency and up to 75% lower storage I/O latency variability compared to previous generation Im4gn instances. At 120 TB, I8ge instances have the highest storage density among AWS Graviton-based storage optimized Amazon EC2 instances. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads.
I8ge instances offer instance sizes up to 48xlarge, 1,536 GiB of memory, and 120 TB instance storage. At 300 Gbps, these instances have the highest networking bandwidth among storage optimized Amazon EC2 instances. They are ideal for real-time applications that require much larger storage density such as relational databases, non-relational databases, streaming databases, search queries and data analytics.
I8ge instances are available in the following AWS Regions: US East (Ohio), US East (N. Virginia) and US West (Oregon).
AWS is announcing the general availability of Amazon Elastic Compute Cloud (Amazon EC2) storage optimized I8ge instances. I8ge instances are powered by AWS Graviton4 processors to deliver up to 60% better compute performance compared to previous generation Graviton2-based storage optimized Amazon EC2 instances. I8ge instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 55% better real-time storage performance per TB while offering up to 60% lower storage I/O latency and up to 75% lower storage I/O latency variability compared to previous generation Im4gn instances. At 120 TB, I8ge instances have the highest storage density among AWS Graviton-based storage optimized Amazon EC2 instances. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads.
I8ge instances offer instance sizes up to 48xlarge, 1,536 GiB of memory, and 120 TB instance storage. At 300 Gbps, these instances have the highest networking bandwidth among storage optimized Amazon EC2 instances. They are ideal for real-time applications that require much larger storage density such as relational databases, non-relational databases, streaming databases, search queries and data analytics.
I8ge instances are available in the following AWS Regions: US East (Ohio), US East (N. Virginia) and US West (Oregon).
To learn more, see Amazon EC2 I8ge instances. 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.
Today, Amazon QuickSight is announcing the general availability of a native Google Sheets connector.
Customers can now connect to Google Sheets by logging in with their Google account and importing sheets into a QuickSight SPICE dataset for analysis.
Google Sheets connector for Amazon QuickSight is now available in the following regions: US East (N.Virginia and Ohio), US West (Oregon), Canada (Central), South America (Sao Paulo), Europe (Frankfurt, Stockholm, Ireland, London), Asia Pacific (Singapore, Tokyo, Seoul, Sydney). For more details, read our blog post here.
Today, Amazon QuickSight is announcing the general availability of a native Google Sheets connector. Customers can now connect to Google Sheets by logging in with their Google account and importing sheets into a QuickSight SPICE dataset for analysis. Google Sheets connector for Amazon QuickSight is now available in the following regions: US East (N.Virginia and Ohio), US West (Oregon), Canada (Central), South America (Sao Paulo), Europe (Frankfurt, Stockholm, Ireland, London), Asia Pacific (Singapore, Tokyo, Seoul, Sydney). For more details, read our blog post here.
RDS Data API now supports Internet Protocol Version 6 (IPv6), enabling dual-stack configuration (IPv4 and IPv6) connectivity for your Aurora databases. IPv6 enables an expanded address space, enabling you to scale your application on AWS beyond the typical constraints of the number of IPv4 addresses in your VPC.
With IPv6, you can assign easy to manage contiguous IP ranges to micro-services and can get virtually unlimited scale for your applications. Moreover, with support for both IPv4 and IPv6, you can gradually transition applications from IPv4 to IPv6, enabling safer migration. IPv6 support is available in all commercial AWS regions where RDS Data API is offered, except Canada (Central). To learn more about Data API and instructions on configuring your network to use IPv6 endpoints, see the documentation.
RDS Data API eliminates the use of drivers and improves application scalability by automatically pooling and sharing database connections rather than requiring you to manage connections. Data API also enables access to Aurora databases via AWS AppSync GraphQL APIs. See the documentation to learn more about Data API.
RDS Data API now supports Internet Protocol Version 6 (IPv6), enabling dual-stack configuration (IPv4 and IPv6) connectivity for your Aurora databases. IPv6 enables an expanded address space, enabling you to scale your application on AWS beyond the typical constraints of the number of IPv4 addresses in your VPC. With IPv6, you can assign easy to manage contiguous IP ranges to micro-services and can get virtually unlimited scale for your applications. Moreover, with support for both IPv4 and IPv6, you can gradually transition applications from IPv4 to IPv6, enabling safer migration. IPv6 support is available in all commercial AWS regions where RDS Data API is offered, except Canada (Central). To learn more about Data API and instructions on configuring your network to use IPv6 endpoints, see the documentation. RDS Data API eliminates the use of drivers and improves application scalability by automatically pooling and sharing database connections rather than requiring you to manage connections. Data API also enables access to Aurora databases via AWS AppSync GraphQL APIs. See the documentation to learn more about Data API.
Lleven la IA a sus fórmulas con la función COPILOT en Excel
Por Catherine Pidgeon
¡Hola, Insiders! Soy Catherine Pidgeon, directora asociada del equipo de Excel. En esta ocasión, me complace presentar la nueva función COPILOT en Microsoft Excel para Windows y Excel para Mac, un gran paso adelante en la forma en que trabajan con datos que lleva el poder de los grandes modelos de lenguaje directo a la cuadrícula y hace que sea más fácil que nunca analizar texto, generar contenido y trabajar más rápido.
Puede ser doloroso y llevar mucho tiempo discutir datos, resumir comentarios, categorizar información y generar ideas. ¡La nueva función COPILOT en Excel para Windows y Excel para Mac está aquí para ahorrar tiempo y potenciar sus flujos de trabajo! Solo tienes que introducir un mensaje en lenguaje natural en tu hoja de cálculo, hacer referencia a los valores de las celdas según sea necesario y ver cómo Copilot genera al instante resultados impulsados por IA.
Y debido a que esta función está integrada en el motor de cálculo de Excel, cada vez que cambian sus datos, sus resultados también se actualizan de manera automática. No es necesario volver a ejecutar scripts ni actualizar complementos: su análisis siempre estará actualizado y será relevante.
La función COPILOT también funciona de forma natural junto con las funciones existentes de Excel. Se puede usar dentro de fórmulas como IF, SWITCH, LAMBDA o WRAPROWS, o pueden usar resultados de otras fórmulas como parte de su mensaje. Esto facilita la adición de funciones de IA a sus hojas de cálculo sin cambiar la forma en que están configuradas.
Cómo funciona
Para utilizar la nueva función COPILOT, deben introducir este código en cualquier celda: =COPILOT(prompt_part1, [context1], [prompt_part2], [context2], …)
La sintaxis de la función COPILOT tiene los siguientes argumentos:
Prompt_part: Texto que describe la tarea o pregunta para el modelo de IA.
Contexto (opcional): una referencia de la cuadrícula que proporciona contexto o datos para el modelo de IA. Puede ser una sola celda o un rango.
Digamos que han recopilado comentarios sobre una nueva máquina de café. Por lo general, se leían, etiquetaban y resumían de manera manual estos datos. Con la función COPILOT, tan solo pueden hacer referencia a la variedad de comentarios, pedirle a Copilot que clasifique cada comentario por sentimiento o categoría y recopilar información procesable.
Por ejemplo: =COPILOT(«Clasificar esta retroalimentación», D4:D18)
A partir de esto, Copilot podría dar los siguientes resultados:
NOTA: Los datos enviados a través de la función COPILOT nunca se utilizan para entrenar o mejorar los modelos de IA. La información que ingresa permanece confidencial y se utiliza sólo para generar la salida solicitada.
Escenarios para probar
A continuación, se muestran algunas formas de aprovechar la función COPILOT en Excel para trabajar de manera más eficiente y efectiva:
Estimular ideas: Ya sea que planeen una campaña de marketing o en el diseño de nuevas características del producto, la función COPILOT facilita la lluvia de ideas directo en la cuadrícula de Excel. ¿Necesitan un conjunto de palabras clave de SEO basadas en la descripción de un producto? ¿Quieres reescribir los mensajes para mayor claridad o cambiar el tono? COPILOT también puede manejar eso.
Generar resúmenes: La función COPILOT puede destilar grandes rangos de datos o pasajes largos en narrativas concisas, resaltar tendencias o producir explicaciones en lenguaje sencillo para cálculos complejos. Esto es en especial valioso para informar cuando necesitan convertir material de fondo extenso en contenido conciso y listo para la audiencia.
Clasificar datos: puede utilizar la función COPILOT para categorizar datos de texto, como comentarios de clientes, tickets de soporte o respuestas a encuestas, directo en su hoja de cálculo, en lugar de tener que exportar los datos a otra herramienta para el etiquetado o el análisis de opiniones.
Crear listas o tablas: La función COPILOT puede generar listas y tablas de datos que se adaptan a la perfección a sus modelos. Ya sea que estén en proceso de creación de un conjunto de datos rápido para probar fórmulas, armar una lista de ejemplos de la industria o redactar un esquema del plan del proyecto, la función puede devolver salidas de varias filas y varias columnas que se derraman directo en la cuadrícula.
Consejos y trucos
La forma en que escriban su prompt marca una gran diferencia en lo que devuelve COPILOT: cuanto más claras sean sus instrucciones, más útiles serán sus resultados. Especifiquen qué celdas, filas o columnas incluir en el análisis, el orden en el que desean que aparezcan los resultados y el formato que necesitan, como una lista o una tabla con encabezados.
Usen palabras de acción directa como «resumir», «categorizar» o «clasificar», y den ejemplos si desean que la salida esté en un estilo o formato determinado.
COPILOT utiliza los datos disponibles dentro del modelo de lenguaje grande, lo que significa que no pueden acceder directo a los datos web en vivo o a los documentos comerciales internos. Si necesitan que COPILOT analice datos actuales o internos, primero importen esos datos en su libro de trabajo y, a continuación, hagan referencia a ellos directo dentro de la función COPILOT. Su resultado debe revisarse y validarse para verificar su precisión, en especial para decisiones o informes comerciales críticos. (En el futuro se agregará soporte para datos web en vivo y documentos comerciales internos).
En la actualidad, la función admite 100 llamadas cada 10 minutos y hasta 300 llamadas por hora. Si necesitan llamadas adicionales, consideren la posibilidad de pasar matrices: una sola llamada que incluya un rango más amplio de datos cuenta solo como un uso, mientras que arrastrar o rellenar la función en varias celdas cuenta como varias llamadas individuales. Ampliaremos el número de llamadas con el tiempo, al brindar una experiencia aún más sólida y flexible para todos los usuarios de Excel.
La función COPILOT es por completo opcional y solo se añade a las hojas de cálculo cuando se decide utilizarla.
Para obtener más información sobre la nueva función COPILOT, visiten nuestra página de soporte.
Cosas en las que trabajamos
Por su naturaleza, la IA mejora de manera continua y la función COPILOT no es diferente. Estamos en el proceso de considerar muchas mejoras: algunas de estas mejoras estarán disponibles a través de la fase beta, y otras vendrán en el futuro en función de sus comentarios, o serán áreas de mejora continua.
Algunas áreas en las que trabajamos y/o investigamos incluyen:
Mejor compatibilidad con matrices grandes: las filas se pueden omitir al devolver matrices. Para solucionar este problema, reestructuren las consultas para devolver resultados de matriz más pequeños.
Los mejores modelos de su clase: Estamos en proceso de probar y comparar de manera activa los modelos para obtener la mejor combinación de rendimiento y capacidades. El modelo subyacente evolucionará y se volverá más capaz con el tiempo.
Mejor orientación: Estamos en proceso de investigar la posibilidad de proporcionar orientación al usuario cuando la función COPILOT se utiliza para tareas no adecuadas para LLM. Por ejemplo: =COPILOT(«Suma estos valores»,A1:A10).
Conocimiento mejorado: La función COPILOT se basa en modelos, sin acceso a datos web y empresariales. Estamos en proceso de investigar la posibilidad de agregar compatibilidad para ampliar estas capacidades.
Mejor soporte de fechas: En la actualidad, la función COPILOT devuelve fechas como texto en lugar del formato de serie de fechas de Excel.
Disponibilidad
La función COPILOT ahora se ha comenzado a implementar para los usuarios del canal beta con una licencia de Microsoft 365 Copilot (obtengan más detalles sobre las licencias para empresas) y que ejecuten:
Windows: versión 2509 (compilación 19212.20000) o posterior.
Mac: versión 16.101 (compilación 25081334) o posterior.
Pronto se implementará en Excel para los usuarios de la web a través del programa Frontier (más información).
Retroalimentación
¡Esperamos ver las formas innovadoras en que nuestra comunidad aprovecha la función COPILOT! Envíen sus comentarios al seleccionar los botones de pulgar hacia arriba o hacia abajo en la ventana emergente «Generado por IA» en su hoja de cálculo de Excel.
La función COPILOT es la sucesora de LABS. GENERATIVEAI que debutó como un experimento en nuestro complemento Excel Labs. ¡Gracias a la comunidad por todos sus comentarios hasta la fecha!
AWS Identity and Access Management (IAM) now offers three new global condition keys that will make it easier for you to establish a network perimeter. The new condition keys – aws:VpceAccount, aws:VpceOrgPaths, and aws:VpceOrgID – help you ensure that requests to your AWS resources or by your identities are made through your VPC endpoints.
The condition keys provide you with varied levels of granularity, enabling you to implement your network perimeter controls at an account, organization path, and entire organization level. The controls automatically scale with your VPC usage, eliminating the need to enumerate VPC endpoints or update policies as you add or remove them. You can use these condition keys with both new and existing service control policies (SCPs), resource control policies (RCPs), resource-based policies, and identity-based policies.
The condition keys are supported for a select set of AWS services and are available in all commercial AWS Regions where those services support AWS PrivateLink.
To learn more about these new condition keys and supported services, please visit the AWS IAM documentation and AWS blog.
AWS Identity and Access Management (IAM) now offers three new global condition keys that will make it easier for you to establish a network perimeter. The new condition keys – aws:VpceAccount, aws:VpceOrgPaths, and aws:VpceOrgID – help you ensure that requests to your AWS resources or by your identities are made through your VPC endpoints. The condition keys provide you with varied levels of granularity, enabling you to implement your network perimeter controls at an account, organization path, and entire organization level. The controls automatically scale with your VPC usage, eliminating the need to enumerate VPC endpoints or update policies as you add or remove them. You can use these condition keys with both new and existing service control policies (SCPs), resource control policies (RCPs), resource-based policies, and identity-based policies. The condition keys are supported for a select set of AWS services and are available in all commercial AWS Regions where those services support AWS PrivateLink. To learn more about these new condition keys and supported services, please visit the AWS IAM documentation and AWS blog.
AWS HealthOmics introduces support for third-party container registries, enabled through Amazon Elastic Container Registry (ECR) pull-through cache, along with URI remapping rules for automatic translation of third-party container URIs to ECR URIs. This enhancement enables AWS HealthOmics customers to more easily access containerized tools from popular third-party registries without needing to manually migrate them to private ECR repositories, or make changes to the workflow definition. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows.
The ECR pull-through cache capability allows bioinformatics teams to automatically retrieve and cache containers from popular registries including Amazon ECR Public, Docker Hub, Quay, GitHub Container Registry, GitLab Container Registry, Kubernetes container image registry, and Microsoft Azure Container Registry. This helps customers accelerate workflow development and execution by eliminating manual container synchronization tasks. Additionally, the new container URI remapping feature automatically translates third-party registry references in workflow definitions to corresponding private ECR URIs using customer-defined mapping rules, eliminating the need to manually update workflow definitions when migrating workflows.
ECR pull-through cache and container URI remapping features are now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv).
To learn more about these new features and how to implement them in your workflows, see the AWS HealthOmics documentation.
AWS HealthOmics introduces support for third-party container registries, enabled through Amazon Elastic Container Registry (ECR) pull-through cache, along with URI remapping rules for automatic translation of third-party container URIs to ECR URIs. This enhancement enables AWS HealthOmics customers to more easily access containerized tools from popular third-party registries without needing to manually migrate them to private ECR repositories, or make changes to the workflow definition. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows. The ECR pull-through cache capability allows bioinformatics teams to automatically retrieve and cache containers from popular registries including Amazon ECR Public, Docker Hub, Quay, GitHub Container Registry, GitLab Container Registry, Kubernetes container image registry, and Microsoft Azure Container Registry. This helps customers accelerate workflow development and execution by eliminating manual container synchronization tasks. Additionally, the new container URI remapping feature automatically translates third-party registry references in workflow definitions to corresponding private ECR URIs using customer-defined mapping rules, eliminating the need to manually update workflow definitions when migrating workflows. ECR pull-through cache and container URI remapping features are now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv). To learn more about these new features and how to implement them in your workflows, see the AWS HealthOmics documentation.
Starting today, Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the US Asia Pacific (Seoul) region. U7i-12tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment.
U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.
Starting today, Amazon EC2 High Memory U7i instances with 12TB of memory (u7i-12tb.224xlarge) are now available in the US Asia Pacific (Seoul) region. U7i-12tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). U7i-12tb instances offer 12TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment. U7i-12tb instances offer 896 vCPUs, support up to 100Gbps Elastic Block Storage (EBS) for faster data loading and backups, deliver up to 100Gbps of network bandwidth, and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server. To learn more about U7i instances, visit the High Memory instances page.
AWS HealthOmics introduces support for the Nextflow time directive, which enables customers to set task level timeout controls to limit run duration for specific tasks. With this launch, customers can now set fine-grained controls for their Nextflow workflow tasks to enable automated run cancellation if specific tasks take longer than expected. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows.
The Nextflow time directive is now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv).
To learn more about workflows and the Nextflow time directive support, see the AWS HealthOmics documentation.
AWS HealthOmics introduces support for the Nextflow time directive, which enables customers to set task level timeout controls to limit run duration for specific tasks. With this launch, customers can now set fine-grained controls for their Nextflow workflow tasks to enable automated run cancellation if specific tasks take longer than expected. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed biological data stores and workflows. The Nextflow time directive is now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv). To learn more about workflows and the Nextflow time directive support, see the AWS HealthOmics documentation.
