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AWS DMS Schema Conversion introduces Virtual Mode

AWS Database Migration Service (DMS) Schema Conversion now supports Virtual Mode for Data providers, enabling you to perform schema assessment and conversion without connecting to target database instances. This feature helps you begin conversion planning immediately while reducing infrastructure costs.

Virtual Mode enables you to evaluate database compatibility, review and convert schema code, generate assessment reports, and plan resource requirements. All of this happens before provisioning actual database infrastructure. When you are ready for migration, you can switch from virtual to real Data providers seamlessly. Virtual Mode works with all AWS DMS Schema Conversion-supported target databases, including Amazon RDS and Aurora PostgreSQL, MySQL, Amazon RDS for Db2, and Amazon Redshift.

Virtual Mode is available in all AWS Regions where AWS DMS Schema Conversion is supported, at no additional charge. To learn more visit the Virtual Data provider page.

 

​AWS Database Migration Service (DMS) Schema Conversion now supports Virtual Mode for Data providers, enabling you to perform schema assessment and conversion without connecting to target database instances. This feature helps you begin conversion planning immediately while reducing infrastructure costs. Virtual Mode enables you to evaluate database compatibility, review and convert schema code, generate assessment reports, and plan resource requirements. All of this happens before provisioning actual database infrastructure. When you are ready for migration, you can switch from virtual to real Data providers seamlessly. Virtual Mode works with all AWS DMS Schema Conversion-supported target databases, including Amazon RDS and Aurora PostgreSQL, MySQL, Amazon RDS for Db2, and Amazon Redshift. Virtual Mode is available in all AWS Regions where AWS DMS Schema Conversion is supported, at no additional charge. To learn more visit the Virtual Data provider page.  

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Amazon RDS for Oracle now supports R6in and M6in instances

Amazon RDS for Oracle now supports R6in and M6in instances that deliver up to 170 Gbps of network bandwidth. Enhanced network bandwidth makes M6in and R6in DB instances ideal for write-intensive workloads.

R6in and M6in instances are available for Amazon RDS for Oracle in Bring Your Own License model for both Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2) editions. You can launch the new instance in the Amazon RDS Management Console or using the AWS CLI. Refer Amazon RDS for Oracle Pricing for available instance configurations and pricing details.

 

​Amazon RDS for Oracle now supports R6in and M6in instances that deliver up to 170 Gbps of network bandwidth. Enhanced network bandwidth makes M6in and R6in DB instances ideal for write-intensive workloads. R6in and M6in instances are available for Amazon RDS for Oracle in Bring Your Own License model for both Oracle Database Enterprise Edition (EE) and Oracle Database Standard Edition 2 (SE2) editions. You can launch the new instance in the Amazon RDS Management Console or using the AWS CLI. Refer Amazon RDS for Oracle Pricing for available instance configurations and pricing details.  

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Amazon DocumentDB Serverless is Generally Available

Today, AWS announces the general availability of Amazon DocumentDB Serverless, an on-demand, auto-scaling configuration for Amazon DocumentDB (with MongoDB compatibility). Amazon DocumentDB is a serverless, fully managed, MongoDB API–compatible document database service. Amazon DocumentDB Serverless automatically scales capacity up or down in fine-grained increments based on your application’s demand, offering up to 90% cost savings compared to provisioning for peak capacity.

For applications with variable workloads, Amazon DocumentDB Serverless offers simplified resource management, with no upfront commitments or additional costs, so you only pay for the database capacity used. It provides the same MongoDB compatible-APIs and capabilities as Amazon DocumentDB, including read replicas, Performance Insights, and I/O-Optimized. Amazon DocumentDB Serverless is ideal for a broad set of applications with variable, multi-tenant, or mixed use (read/write) workloads. For example, enterprises that have thousands of applications, or software as a service (SaaS) vendors that have multi-tenant environments with hundreds or thousands of databases, can use Amazon DocumentDB Serverless to manage database capacity across their entire fleet of databases. Additionally, you can build agentic AI applications that benefit from its native vector search and serverless adaptability to handle dynamically invoked agentic AI workflows. Amazon DocumentDB Serverless is available starting with Amazon DocumentDB 5.0 for both new and existing clusters.

For pricing details and region availability, visit Amazon DocumentDB Pricing.

To learn more about Amazon DocumentDB Serverless, see the overviewdocumentation, and AWS News blog. Get started in just a few steps in the AWS Management Console.

 

​Today, AWS announces the general availability of Amazon DocumentDB Serverless, an on-demand, auto-scaling configuration for Amazon DocumentDB (with MongoDB compatibility). Amazon DocumentDB is a serverless, fully managed, MongoDB API–compatible document database service. Amazon DocumentDB Serverless automatically scales capacity up or down in fine-grained increments based on your application’s demand, offering up to 90% cost savings compared to provisioning for peak capacity.
For applications with variable workloads, Amazon DocumentDB Serverless offers simplified resource management, with no upfront commitments or additional costs, so you only pay for the database capacity used. It provides the same MongoDB compatible-APIs and capabilities as Amazon DocumentDB, including read replicas, Performance Insights, and I/O-Optimized. Amazon DocumentDB Serverless is ideal for a broad set of applications with variable, multi-tenant, or mixed use (read/write) workloads. For example, enterprises that have thousands of applications, or software as a service (SaaS) vendors that have multi-tenant environments with hundreds or thousands of databases, can use Amazon DocumentDB Serverless to manage database capacity across their entire fleet of databases. Additionally, you can build agentic AI applications that benefit from its native vector search and serverless adaptability to handle dynamically invoked agentic AI workflows. Amazon DocumentDB Serverless is available starting with Amazon DocumentDB 5.0 for both new and existing clusters.
For pricing details and region availability, visit Amazon DocumentDB Pricing.
To learn more about Amazon DocumentDB Serverless, see the overview, documentation, and AWS News blog. Get started in just a few steps in the AWS Management Console.  

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Catalyst: Basecamp Research aprovecha la IA de Microsoft y NVIDIA para descubrir los secretos de la biodiversidad

julio 31, 2025

Catalyst: Basecamp Research aprovecha la IA de Microsoft y NVIDIA para descubrir los secretos de la biodiversidad

Una mujer sostiene una tablet en un invernadero

Por: Microsoft for Startups.

Una iniciativa innovadora promete revolucionar nuestra comprensión de la biodiversidad y sus aplicaciones, y está impulsada por Microsoft y NVIDIA. Presentamos Basecamp Research.

El equipo de investigación de Basecamp tiene la misión de digitalizar la naturaleza, a través de transformar el material biológico en datos para descubrir los secretos del mundo natural. Este ambicioso proyecto tiene como objetivo cerrar la brecha entre la biotecnología y la biodiversidad, para crear la base de datos de secuencias de proteínas biológicas más grande y de más rápido crecimiento del mundo, que contiene más de 9.800 millones de nuevas secuencias y más de un millón de especies recién descubiertas, lo que amplía el árbol de la vida conocido en más de diez veces en comparación con todas las bases de datos públicas combinadas.

Esta escala no es solo un hito científico, es un avance que permite a los investigadores modelar la evolución misma, para brindar una ventana a cómo la vida en la Tierra se ha adaptado y diversificado durante miles de millones de años.

Cuando miras una cucharada de tierra, podría haber tantos seres vivos en esa cucharada de tierra como humanos en el planeta.

—Marlon Clark, líder de colaboración e innovación, Basecamp Research

Basecamp Research no solo construye una lista más larga de especies catalogadas, sino que crea una base de datos de una escala y complejidad diferentes por completo. Este conjunto de datos integral permite que los modelos avanzados de IA descubran las reglas y mecanismos de la evolución, identifiquen nuevas proteínas y vías y diseñen nuevas soluciones biológicas para los desafíos de la medicina, la sostenibilidad y más.

La evolución es la fuerza más poderosa de la biología, y al comprender cómo la naturaleza la usa para resolver problemas, no podemos subestimar el impacto que esto tendrá en los avances de la biología.

—John Finn, director científico, Basecamp Research

Como parte del  programa Microsoft for Startups y NVIDIA Inception, Basecamp Research tiene las herramientas y los recursos necesarios para descubrir los secretos de la biodiversidad e impulsar la innovación en biotecnología. Al combinar la base de nube escalable de Azure con la innovación de IA de pila completa de NVIDIA, Basecamp Research allana el camino para una nueva era de descubrimiento biológico e inspira a otros a explorar el increíble potencial de la naturaleza. Microsoft y NVIDIA impulsan los modelos de IA de Basecamp Research para procesar y analizar datos a mayores velocidades, lo que acelera aún más su investigación y les permite abordar desafíos biológicos complejos.

El enfoque de la startup es convertir material biológico en datos a través de la secuenciación del ADN, un proceso que revela los intrincados detalles de los organismos que habitan nuestro planeta. Estos datos se utilizan para construir una base de datos completa de más de 10 mil millones de secuencias de proteínas novedosas que no solo catalogan la diversidad de la vida, sino que también entrenan una nueva familia de modelos de base, para aprovechar los conocimientos sobre cómo funcionan estos organismos e interactúan con su entorno, lo que sienta las bases para inspirar nuevas innovaciones en biotecnología.

No solo usamos nuestra base de datos para comenzar a identificar nuevas proteínas que podamos usar. También usamos los modelos de IA que construimos para ayudarnos a comenzar a evolucionar esas proteínas para que tengan las características que queremos sin hacer millones de variantes.

—John Finn, director científico, Basecamp Research

Microsoft y NVIDIA: Impulsar el próximo salto en el descubrimiento biológico

La misión de Basecamp Research está impulsada por una combinación única de Microsoft Azure y la plataforma de IA NVIDIA de pila completa. Juntas, estas tecnologías proporcionan la infraestructura escalable, las herramientas avanzadas de IA y la computación acelerada necesarias para procesar y analizar el conjunto de datos biológicos más grande y diverso del mundo.  

Al aprovechar la potencia de los servicios en la nube de Azure, Basecamp Research puede procesar y analizar grandes cantidades de datos biológicos de manera eficiente y eficaz. La escalabilidad y flexibilidad de Azure permiten al equipo controlar los conjuntos de datos masivos generados a partir de la secuenciación de ADN y otros análisis biológicos.

Una de las ventajas clave de Azure es su capacidad para admitir cargas de trabajo de informática de alto rendimiento (HPC, por sus siglas en inglés) aceleradas por NVIDIA. Esto es crucial para Basecamp Research, ya que su trabajo implica tareas computacionales complejas que requieren una potencia de procesamiento significativa. Las funcionalidades de HPC de Azure permiten al equipo ejecutar simulaciones y análisis a gran escala, acelerando sus procesos de investigación y detección.

Además, las herramientas de inteligencia artificial y aprendizaje automático de Azure y NVIDIA son fundamentales para ayudar a Basecamp Research a obtener información significativa de sus datos. Al aprovechar Azure Machine Learning y el marco NVIDIA BioNeMo, el equipo puede crear, entrenar e implementar modelos de inteligencia artificial sofisticados que pueden predecir e identificar patrones en datos biológicos. Esto les permite descubrir nuevos conocimientos biológicos y desarrollar soluciones innovadoras inspiradas en la naturaleza.

Hacer que los aprendizajes sean accesibles

El equipo de investigación de Basecamp también se compromete a garantizar que los beneficios de su trabajo se compartan con las comunidades con las que colaboran. Mejoran la capacidad local mediante la construcción de laboratorios, el intercambio de datos y la capacitación de científicos, y devuelven los ingresos a estas comunidades cuando sus datos conducen al éxito comercial.

Desde el trabajo que realizan hasta la tecnología que eligen, el proyecto Basecamp Research es un testimonio del poder de la colaboración y la innovación. Al romper el «muro de datos» que ha limitado el progreso en las ciencias de la vida, la base de datos de Basecamp potencia la biología generativa, a través de la utilización de la IA para diseñar, generar y anotar proteínas, vías y terapias con un nivel de precisión y creatividad que antes era imposible

En esencia, Basecamp Research como empresa se basa en esta idea de que la biología tiene las respuestas y el proceso de evolución ha llevado a este sistema complejo en verdad notable que no debería funcionar y, sin embargo, lo hace. De manera fundamental, dependemos mucho de la biodiversidad y poder estudiarla y comprenderla es una de las cosas más importantes que puedes hacer.

—Phoebe Oldach, vicepresidenta de crecimiento de datos, Basecamp Research

Para profundizar en este fascinante recorrido y presenciar el trabajo innovador del equipo de investigación de Basecamp, vean el video completo. Para otras aplicaciones intrigantes de la tecnología de Microsoft y NVIDIA, sigan la serie Catalyst.

Empiecen a usar Microsoft for Startups hoy mismo

The post Catalyst: Basecamp Research aprovecha la IA de Microsoft y NVIDIA para descubrir los secretos de la biodiversidad appeared first on Source LATAM.

 

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Amazon Aurora MySQL database clusters now support up to 256 TiB of storage volume

Amazon Aurora MySQL-Compatible Edition now supports a maximum storage limit of 256 TiB, doubling the previous limit of 128 TiB. This enhancement allows customers to store and manage even larger datasets within a single Aurora database cluster simplifying data management for large-scale applications and supporting the growing data needs of modern applications. Customers only pay for the storage they use, with no need for upfront provisioning of the full 256 TiB.

To access the increased storage limit, upgrade your cluster to supported database versions. Once upgraded, Aurora storage will automatically scale up to 256 TiB capacity based on the amount of data in the cluster volume. Visit technical documentation to learn more about supported versions. This new storage volume capacity is available in all AWS regions where Aurora MySQL and Aurora PostgreSQL is available. 

Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.

 

​Amazon Aurora MySQL-Compatible Edition now supports a maximum storage limit of 256 TiB, doubling the previous limit of 128 TiB. This enhancement allows customers to store and manage even larger datasets within a single Aurora database cluster simplifying data management for large-scale applications and supporting the growing data needs of modern applications. Customers only pay for the storage they use, with no need for upfront provisioning of the full 256 TiB. To access the increased storage limit, upgrade your cluster to supported database versions. Once upgraded, Aurora storage will automatically scale up to 256 TiB capacity based on the amount of data in the cluster volume. Visit technical documentation to learn more about supported versions. This new storage volume capacity is available in all AWS regions where Aurora MySQL and Aurora PostgreSQL is available.  Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.  

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Amazon Aurora MySQL 3.10 (compatible with MySQL 8.0.42) is now generally available

Starting today, Amazon Aurora MySQL – Compatible Edition 3 (with MySQL 8.0 compatibility) will support MySQL 8.0.42 through Aurora MySQL v3.10. In addition to several security enhancements and bug fixes, MySQL 8.0.42 contains performance improvements for parallel replication using writeset dependency tracking, as well as enhanced debugging capabilities within the InnoDB storage engine.

Aurora MySQL 3.10 includes an increase in maximum storage capacity from 128 TiB to 256 TiB, allowing customers to manage larger database workloads within a single database cluster. Aurora MySQL 3.10 also introduces in-memory relay log optimization that improves binary log replication performance by caching relay log content in memory, reducing commit latency and minimizing storage I/O operations on binlog replicas. For more details, refer to the Aurora MySQL 3.10 and MySQL 8.0.42 release notes.

To upgrade to Aurora MySQL 3.10, you can initiate a minor version upgrade manually by modifying your DB cluster, or you can enable the “Auto minor version upgrade” option when creating or modifying a DB cluster. This release is available in all AWS regions where Aurora MySQL is available.

Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other Amazon Web Services services. To get started with Amazon Aurora, take a look at our getting started page.

 

​Starting today, Amazon Aurora MySQL – Compatible Edition 3 (with MySQL 8.0 compatibility) will support MySQL 8.0.42 through Aurora MySQL v3.10. In addition to several security enhancements and bug fixes, MySQL 8.0.42 contains performance improvements for parallel replication using writeset dependency tracking, as well as enhanced debugging capabilities within the InnoDB storage engine. Aurora MySQL 3.10 includes an increase in maximum storage capacity from 128 TiB to 256 TiB, allowing customers to manage larger database workloads within a single database cluster. Aurora MySQL 3.10 also introduces in-memory relay log optimization that improves binary log replication performance by caching relay log content in memory, reducing commit latency and minimizing storage I/O operations on binlog replicas. For more details, refer to the Aurora MySQL 3.10 and MySQL 8.0.42 release notes. To upgrade to Aurora MySQL 3.10, you can initiate a minor version upgrade manually by modifying your DB cluster, or you can enable the “Auto minor version upgrade” option when creating or modifying a DB cluster. This release is available in all AWS regions where Aurora MySQL is available. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other Amazon Web Services services. To get started with Amazon Aurora, take a look at our getting started page.  

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Amazon CloudFront introduces new origin response timeout controls

Amazon CloudFront now offers two capabilities to enhance origin timeout controls: a response completion timeout and support for custom response timeout values for Amazon S3 origins. These enhancements provide more granular control over origin response timeouts, allowing you to deliver consistent and reliable user experiences regardless of variation in network conditions or origin performance.

Previously, you could configure a response timeout to control the amount of time CloudFront waits for your origin to send the first packet, as well as the amount of time CloudFront waits for subsequent packets. If your origin times out, CloudFront resets the response timeout and tries again based on the configured number of retries. With the new response completion timeout, you can now additionally configure the maximum amount of time CloudFront should wait for a complete response from your origin across all packets and retries. This allows you to control the cumulative response time for latency sensitive workloads such as media streaming or API calls. When using Amazon S3 as your origin, you can now also set custom response timeout values instead of using the default value of 30 seconds. These capabilities provide you with more control over how CloudFront handles slow or unresponsive origins.

CloudFront supports response completion timeout, and custom response timeout values for Amazon S3 origins, across all CloudFront edge locations excluding the AWS China (Beijing) region. You can configure origin timeouts using the CloudFront console, API, and AWS CloudFormation at no additional charge. To learn more, visit the CloudFront Developer Guide.

 

​Amazon CloudFront now offers two capabilities to enhance origin timeout controls: a response completion timeout and support for custom response timeout values for Amazon S3 origins. These enhancements provide more granular control over origin response timeouts, allowing you to deliver consistent and reliable user experiences regardless of variation in network conditions or origin performance. Previously, you could configure a response timeout to control the amount of time CloudFront waits for your origin to send the first packet, as well as the amount of time CloudFront waits for subsequent packets. If your origin times out, CloudFront resets the response timeout and tries again based on the configured number of retries. With the new response completion timeout, you can now additionally configure the maximum amount of time CloudFront should wait for a complete response from your origin across all packets and retries. This allows you to control the cumulative response time for latency sensitive workloads such as media streaming or API calls. When using Amazon S3 as your origin, you can now also set custom response timeout values instead of using the default value of 30 seconds. These capabilities provide you with more control over how CloudFront handles slow or unresponsive origins. CloudFront supports response completion timeout, and custom response timeout values for Amazon S3 origins, across all CloudFront edge locations excluding the AWS China (Beijing) region. You can configure origin timeouts using the CloudFront console, API, and AWS CloudFormation at no additional charge. To learn more, visit the CloudFront Developer Guide.  

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Amazon Managed Service for Prometheus increases default active series limit to 50M per workspace

Amazon Managed Service for Prometheus now offers a higher default limit of 50M active time series per workspace, up from 10M. This increase eliminates the need for limit increase requests up to 50M series. Customers can still request limit increases for up to 1 billion active series per workspace. An Amazon Managed Service for Prometheus workspace is a logical space dedicated to the storage and querying of Prometheus metrics.

The new limit increase is already applied to your current workspace, and is available in all AWS regions where Amazon Managed Service for Prometheus is generally available

Check out the Amazon Managed Service for Prometheus user guide for detailed documentation. To learn more about Amazon Managed Service for Prometheus, visit the product page and pricing page.

 

​Amazon Managed Service for Prometheus now offers a higher default limit of 50M active time series per workspace, up from 10M. This increase eliminates the need for limit increase requests up to 50M series. Customers can still request limit increases for up to 1 billion active series per workspace. An Amazon Managed Service for Prometheus workspace is a logical space dedicated to the storage and querying of Prometheus metrics. The new limit increase is already applied to your current workspace, and is available in all AWS regions where Amazon Managed Service for Prometheus is generally available.  Check out the Amazon Managed Service for Prometheus user guide for detailed documentation. To learn more about Amazon Managed Service for Prometheus, visit the product page and pricing page.  

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AWS Entity Resolution launches advanced matching using Levenshtein, Cosine, and Soundex

Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement.

AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes.

AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these AWS Regions. To learn more, visit AWS Entity Resolution.

 

​Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement. AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes. AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these AWS Regions. To learn more, visit AWS Entity Resolution.  

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Database Insights adds support for fleets of Aurora Limitless databases

CloudWatch Database Insights announces support of fleet monitoring for Amazon Aurora PostgreSQL Limitless databases. Database Insights is a database observability solution that provides a curated experience designed for DevOps engineers, application developers, and database administrators (DBAs) to expedite database troubleshooting and gain a holistic view into their database fleet health.

Database Insights consolidates logs and metrics from your applications, your databases, and the operating systems on which they run into a unified view in the console. Using its pre-built dashboards, and automated telemetry collection, you can monitor fleet health across all your database types in one place, and drill down seamlessly from fleet overview to individual instance analysis.

Database Insights offers two curated monitoring views: a fleet health dashboard for estate-wide visibility and an instance dashboard for detailed performance analysis. Aurora Limitless PostgreSQL databases were previously supported through instance-level monitoring — enabling you to track load distribution across shard groups. We’re now extending this capability to include fleet-level monitoring, which allows you to view the overall health of your entire database fleets, including Aurora clusters, RDS instances, and Aurora Limitless PostgreSQL databases, all from a single unified dashboard.

You can get started with Database Insights for Aurora Limitless by enabling it on your Limitless databases using the Aurora service console, AWS APIs, and SDKs.

Database Insights for Aurora Limitless is available in all regions where Aurora Limitless is available and applies a new ACU-based pricing – see pricing page for details. For further information, visit the Database Insights documentation.

 

​CloudWatch Database Insights announces support of fleet monitoring for Amazon Aurora PostgreSQL Limitless databases. Database Insights is a database observability solution that provides a curated experience designed for DevOps engineers, application developers, and database administrators (DBAs) to expedite database troubleshooting and gain a holistic view into their database fleet health. Database Insights consolidates logs and metrics from your applications, your databases, and the operating systems on which they run into a unified view in the console. Using its pre-built dashboards, and automated telemetry collection, you can monitor fleet health across all your database types in one place, and drill down seamlessly from fleet overview to individual instance analysis. Database Insights offers two curated monitoring views: a fleet health dashboard for estate-wide visibility and an instance dashboard for detailed performance analysis. Aurora Limitless PostgreSQL databases were previously supported through instance-level monitoring — enabling you to track load distribution across shard groups. We’re now extending this capability to include fleet-level monitoring, which allows you to view the overall health of your entire database fleets, including Aurora clusters, RDS instances, and Aurora Limitless PostgreSQL databases, all from a single unified dashboard. You can get started with Database Insights for Aurora Limitless by enabling it on your Limitless databases using the Aurora service console, AWS APIs, and SDKs. Database Insights for Aurora Limitless is available in all regions where Aurora Limitless is available and applies a new ACU-based pricing – see pricing page for details. For further information, visit the Database Insights documentation.