Amazon SageMaker Catalog integrates with Amazon S3 Tables, making it easy to discover, share, and govern S3 Tables for users to access and query the data with all Apache Iceberg–compatible tools and engines. With Amazon SageMaker Catalog, built on Amazon DataZone, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or just ask Amazon Q Developer with natural language to find your data.

S3 Tables deliver the first cloud object store with built-in Apache Iceberg support. Data publishers can onboard S3 tables to SageMaker Lakehouse and enhance their discoverability by adding them to the SageMaker Catalog. Publishers have the flexibility to either directly publish tables or enrich them with valuable business metadata, making it easier for all users to understand and find the data they need. On the consumption side, users can search for relevant tables, request access through a subscription workflow (subject to publisher approval), and leverage this data for advanced analytics and AI development projects. This end-to-end workflow significantly improves data accessibility, governance, and utilization of S3 Tables across the organization.

SageMaker Catalog with S3 Tables support is available in all AWS Regions where Amazon SageMaker is available.

To learn more, visit Amazon SageMaker. Get started with S3 Tables and publish using user documentation.
 

 

​Amazon SageMaker Catalog integrates with Amazon S3 Tables, making it easy to discover, share, and govern S3 Tables for users to access and query the data with all Apache Iceberg–compatible tools and engines. With Amazon SageMaker Catalog, built on Amazon DataZone, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or just ask Amazon Q Developer with natural language to find your data. S3 Tables deliver the first cloud object store with built-in Apache Iceberg support. Data publishers can onboard S3 tables to SageMaker Lakehouse and enhance their discoverability by adding them to the SageMaker Catalog. Publishers have the flexibility to either directly publish tables or enrich them with valuable business metadata, making it easier for all users to understand and find the data they need. On the consumption side, users can search for relevant tables, request access through a subscription workflow (subject to publisher approval), and leverage this data for advanced analytics and AI development projects. This end-to-end workflow significantly improves data accessibility, governance, and utilization of S3 Tables across the organization. SageMaker Catalog with S3 Tables support is available in all AWS Regions where Amazon SageMaker is available. To learn more, visit Amazon SageMaker. Get started with S3 Tables and publish using user documentation.    

Today, AWS announces the general availability of migration assessment capabilities in AWS Transform. Migration assessment in AWS Transform analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Simply upload your infrastructure data and AWS Transform will deliver a comprehensive analysis that typically takes weeks in just minutes.

Powered by agentic AI, AWS Transform removes weeks of manual analysis by providing instant visibility into your infrastructure and automatically discovering cost optimization opportunities. AWS Transform produces a business case including key highlights from your server inventory, a summary of current infrastructure, multiple TCO scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included), and tenancy options.

AWS Transform for migration assessments is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).

Ready to get started? Visit the AWS Transform web experience or read our blog post to learn more.

 

​Today, AWS announces the general availability of migration assessment capabilities in AWS Transform. Migration assessment in AWS Transform analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Simply upload your infrastructure data and AWS Transform will deliver a comprehensive analysis that typically takes weeks in just minutes.
Powered by agentic AI, AWS Transform removes weeks of manual analysis by providing instant visibility into your infrastructure and automatically discovering cost optimization opportunities. AWS Transform produces a business case including key highlights from your server inventory, a summary of current infrastructure, multiple TCO scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included), and tenancy options.
AWS Transform for migration assessments is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).
Ready to get started? Visit the AWS Transform web experience or read our blog post to learn more.  

AWS Transform for .NET, previewed as “Amazon Q Developer transformation capabilities for .NET porting,” is now generally available. As the first agentic AI service for modernizing .NET applications at scale, AWS Transform helps you to modernize Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. It supports transforming a wide range of .NET project types including MVC, WCF, Web API, class libraries, console apps, and unit test projects.

The agentic transformation begins with a code assessment of your repositories from GitHub, GitLab, or Bitbucket. It identifies .NET versions, project types, and interproject dependencies and generates a tailored modernization plan. You can customize and prioritize the transformation sequence based on your business objectives or architectural complexity before initiating the AI-powered modernization process. Once started, AWS Transform for .NET automatically converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository. It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through the AWS Transform dashboards or interactive chat and receive email notifications with links to transformed .NET code. For workloads that need further human input, your developers can continue refinement using the Visual Studio extension in AWS Transform. The scalable experience of AWS Transform enables consistent modernization across a large application portfolio while moving to cross-platform .NET, unlocking performance, portability, and long-term maintainability.

AWS Transform for .NET is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt). 

To learn more, read the blog, visit the webpage, or review the documentation.

 

​AWS Transform for .NET, previewed as “Amazon Q Developer transformation capabilities for .NET porting,” is now generally available. As the first agentic AI service for modernizing .NET applications at scale, AWS Transform helps you to modernize Windows .NET applications to be Linux-ready up to four times faster than traditional methods and realize up to 40% savings in licensing costs. It supports transforming a wide range of .NET project types including MVC, WCF, Web API, class libraries, console apps, and unit test projects.
The agentic transformation begins with a code assessment of your repositories from GitHub, GitLab, or Bitbucket. It identifies .NET versions, project types, and interproject dependencies and generates a tailored modernization plan. You can customize and prioritize the transformation sequence based on your business objectives or architectural complexity before initiating the AI-powered modernization process. Once started, AWS Transform for .NET automatically converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository. It provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through the AWS Transform dashboards or interactive chat and receive email notifications with links to transformed .NET code. For workloads that need further human input, your developers can continue refinement using the Visual Studio extension in AWS Transform. The scalable experience of AWS Transform enables consistent modernization across a large application portfolio while moving to cross-platform .NET, unlocking performance, portability, and long-term maintainability.
AWS Transform for .NET is now available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt). 
To learn more, read the blog, visit the webpage, or review the documentation.  

AWS Transform for mainframe, previewed as “Amazon Q Developer transformation capabilities for mainframe” at re:Invent 2024, is now generally available. AWS Transform is the first agentic AI service for modernizing mainframe applications at scale—accelerating modernization of IBM z/OS applications from years to months.

Powered by a specialized AI agent leveraging 19 years of AWS experience, AWS Transform streamlines the entire transformation process—from initial analysis and planning to code documentation and refactoring—helping organizations to modernize faster, reduce risk and cost, and achieve better outcomes in the cloud.

This release introduces significant new capabilities. Enhanced analysis features help teams identify cyclomatic complexity, homonyms, and duplicate IDs across codebases, with new export and import functions for file classification and in-UI file viewing and comparison. Documentation generation now supports larger codebases with improved performance and recovery capabilities, including an AI-powered chat experience for querying generated documentation. 

Teams can use improved decomposition features to manage dependencies and domain creation, while new deployment templates streamline environment setup for modernized applications. The service also introduces flexible job management, allowing teams to modify objectives and focus on specific transformation steps during reruns. 

AWS Transform for mainframe is available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).

To learn more, read the blog post, register for the upcoming launch webinar, or get started in the AWS Transform web experience.

 

​AWS Transform for mainframe, previewed as “Amazon Q Developer transformation capabilities for mainframe” at re:Invent 2024, is now generally available. AWS Transform is the first agentic AI service for modernizing mainframe applications at scale—accelerating modernization of IBM z/OS applications from years to months.
Powered by a specialized AI agent leveraging 19 years of AWS experience, AWS Transform streamlines the entire transformation process—from initial analysis and planning to code documentation and refactoring—helping organizations to modernize faster, reduce risk and cost, and achieve better outcomes in the cloud.
This release introduces significant new capabilities. Enhanced analysis features help teams identify cyclomatic complexity, homonyms, and duplicate IDs across codebases, with new export and import functions for file classification and in-UI file viewing and comparison. Documentation generation now supports larger codebases with improved performance and recovery capabilities, including an AI-powered chat experience for querying generated documentation. 
Teams can use improved decomposition features to manage dependencies and domain creation, while new deployment templates streamline environment setup for modernized applications. The service also introduces flexible job management, allowing teams to modify objectives and focus on specific transformation steps during reruns. 
AWS Transform for mainframe is available in the following AWS Regions: US East (N. Virginia) and Europe (Frankfurt).
To learn more, read the blog post, register for the upcoming launch webinar, or get started in the AWS Transform web experience.  

At re:Invent 2024, AWS introduced the preview of Amazon Q Developer transformation capabilities for VMware. That innovation has evolved into AWS Transform for VMware—a first-of-its-kind agentic AI service that’s now generally available. Powered by large language models, graph neural networks, and the deep experience of AWS in enterprise workload migrations, AWS Transform simplifies VMware modernization at scale. Customers and partners can now move faster, reduce migration risk, and modernize with confidence.

VMware environments have long been foundational to enterprise IT, but rising costs and vendor uncertainty are prompting organizations to rethink their strategies. Despite the urgency, VMware workload migration has historically been slow and error-prone. AWS Transform changes that. With agentic AI, AWS Transform automates the full modernization lifecycle—from discovery and dependency mapping to network translation and Amazon Elastic Compute Cloud (Amazon EC2) optimization. Certain tasks that once took weeks can now be completed in minutes. In testing, AWS generated migration wave plans for 500 VMs in just 15 minutes and performed networking translations up to 80x faster than traditional methods. Partners in pilot programs have cut execution times by up to 90%. 

Beyond speed, AWS Transform delivers precision and transparency. A shared workspace brings together infrastructure teams, app owners, partners, and AWS experts to resolve blockers and maintain alignment. Built-in human-in-the-loop controls confirm all artifacts are validated before execution. As enterprises aim to break free from legacy constraints and tap into the value of their data, AWS Transform offers a streamlined path to modern, cloud-native architectures. Customers can seamlessly integrate with 200+ AWS services—including analytics, serverless, and generative AI—to accelerate innovation and reduce long-term costs.

Start your VMware modernization journey with AWS Transform. Read the launch blog, explore the documentation, register for the launch webinar, or check out the interactive demo.
 

 

​At re:Invent 2024, AWS introduced the preview of Amazon Q Developer transformation capabilities for VMware. That innovation has evolved into AWS Transform for VMware—a first-of-its-kind agentic AI service that’s now generally available. Powered by large language models, graph neural networks, and the deep experience of AWS in enterprise workload migrations, AWS Transform simplifies VMware modernization at scale. Customers and partners can now move faster, reduce migration risk, and modernize with confidence.
VMware environments have long been foundational to enterprise IT, but rising costs and vendor uncertainty are prompting organizations to rethink their strategies. Despite the urgency, VMware workload migration has historically been slow and error-prone. AWS Transform changes that. With agentic AI, AWS Transform automates the full modernization lifecycle—from discovery and dependency mapping to network translation and Amazon Elastic Compute Cloud (Amazon EC2) optimization. Certain tasks that once took weeks can now be completed in minutes. In testing, AWS generated migration wave plans for 500 VMs in just 15 minutes and performed networking translations up to 80x faster than traditional methods. Partners in pilot programs have cut execution times by up to 90%. 
Beyond speed, AWS Transform delivers precision and transparency. A shared workspace brings together infrastructure teams, app owners, partners, and AWS experts to resolve blockers and maintain alignment. Built-in human-in-the-loop controls confirm all artifacts are validated before execution. As enterprises aim to break free from legacy constraints and tap into the value of their data, AWS Transform offers a streamlined path to modern, cloud-native architectures. Customers can seamlessly integrate with 200+ AWS services—including analytics, serverless, and generative AI—to accelerate innovation and reduce long-term costs.
Start your VMware modernization journey with AWS Transform. Read the launch blog, explore the documentation, register for the launch webinar, or check out the interactive demo.    

Etiquetas:

• Seguridad

The post Por qué la armonización internacional de las regulaciones de ciberseguridad debe ser una prioridad appeared first on Source LATAM.

 

​The post Por qué la armonización internacional de las regulaciones de ciberseguridad debe ser una prioridad appeared first on Source LATAM.  

Amazon Relational Database Service (Amazon RDS) for Oracle now supports the April 2025 Release Update (RU) for Oracle Database versions 19c and 21c. These RUs include bug and security fixes and are available for RDS for Oracle Standard Edition 2 and Enterprise Edition. Review the Oracle release notes for April RU for details.

We recommend upgrading to this RU as it includes security fixes. You can upgrade with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. You can also enable auto minor version upgrade (AmVU) to automatically upgrade your database instances. Learn more about upgrading your database instances from the Amazon RDS User Guide.

This new minor version is available in all AWS regions where Amazon RDS for Oracle is available. See Amazon RDS for Oracle Pricing for pricing details and regional availability.

 

​Amazon Relational Database Service (Amazon RDS) for Oracle now supports the April 2025 Release Update (RU) for Oracle Database versions 19c and 21c. These RUs include bug and security fixes and are available for RDS for Oracle Standard Edition 2 and Enterprise Edition. Review the Oracle release notes for April RU for details. We recommend upgrading to this RU as it includes security fixes. You can upgrade with just a few clicks in the Amazon RDS Management Console or by using the AWS SDK or CLI. You can also enable auto minor version upgrade (AmVU) to automatically upgrade your database instances. Learn more about upgrading your database instances from the Amazon RDS User Guide. This new minor version is available in all AWS regions where Amazon RDS for Oracle is available. See Amazon RDS for Oracle Pricing for pricing details and regional availability.  

Today, AWS HealthOmics announces enhancements to its Common Workflow Language (CWL) support by automatically generating comprehensive outputs.json mapping files for every workflow run. With this launch, HealthOmics now provides researchers and bioinformaticians with a complete catalog of all outputs generated by workflow runs along with their precise locations in Amazon S3. 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 new output mapping capability simplifies downstream process automation and validation of run outputs, enabling more efficient data analysis pipelines. Customers can now easily track and access all workflow results without manual tracking or custom parsing scripts, saving time and reducing the possibility of errors when working with complex, multi-step bioinformatics workflows at scale.

CWL output mapping files 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 AWS HealthOmics and this new feature, see the AWS HealthOmics documentation.
 

 

​Today, AWS HealthOmics announces enhancements to its Common Workflow Language (CWL) support by automatically generating comprehensive outputs.json mapping files for every workflow run. With this launch, HealthOmics now provides researchers and bioinformaticians with a complete catalog of all outputs generated by workflow runs along with their precise locations in Amazon S3. 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 new output mapping capability simplifies downstream process automation and validation of run outputs, enabling more efficient data analysis pipelines. Customers can now easily track and access all workflow results without manual tracking or custom parsing scripts, saving time and reducing the possibility of errors when working with complex, multi-step bioinformatics workflows at scale. CWL output mapping files 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 AWS HealthOmics and this new feature, see the AWS HealthOmics documentation.    

Amazon Bedrock Guardrails announces support for cross-region inference, an optional feature that enables customers to seamlessly manage traffic bursts by utilizing compute across different AWS regions.

Bedrock Guardrails provides configurable safeguards to help detect and block harmful content and prompt attacks, define topics to deny and disallow specific topics, and helps redact personally identifiable information (PII) such as personal data from input prompts and model responses. Additionally, Bedrock Guardrails helps detect and block model hallucinations, and identify, correct, and explain factual claims in model responses using Automated Reasoning checks. Guardrails can be applied across any foundation model including those hosted with Amazon Bedrock, self-hosted models, and third-party models outside Bedrock using the ApplyGuardrail API, providing a consistent user experience and helping to standardize safety and privacy controls.

By using cross-region inference, Amazon Bedrock Guardrails customers will be able to get consistent throughput and enhanced resilience during periods of peak demand. By opting in, customers no longer have to spend time and effort predicting demand fluctuations. Instead, cross-region inference dynamically routes traffic across multiple regions, ensuring optimal availability for each request and smoother performance during high-usage periods.

There’s no additional routing cost for using cross-region inference with Amazon Bedrock Guardrails. Please find the list of supported regions here.

To learn more about the feature and how to get started, refer to the technical documentation.
 

 

​Amazon Bedrock Guardrails announces support for cross-region inference, an optional feature that enables customers to seamlessly manage traffic bursts by utilizing compute across different AWS regions. Bedrock Guardrails provides configurable safeguards to help detect and block harmful content and prompt attacks, define topics to deny and disallow specific topics, and helps redact personally identifiable information (PII) such as personal data from input prompts and model responses. Additionally, Bedrock Guardrails helps detect and block model hallucinations, and identify, correct, and explain factual claims in model responses using Automated Reasoning checks. Guardrails can be applied across any foundation model including those hosted with Amazon Bedrock, self-hosted models, and third-party models outside Bedrock using the ApplyGuardrail API, providing a consistent user experience and helping to standardize safety and privacy controls. By using cross-region inference, Amazon Bedrock Guardrails customers will be able to get consistent throughput and enhanced resilience during periods of peak demand. By opting in, customers no longer have to spend time and effort predicting demand fluctuations. Instead, cross-region inference dynamically routes traffic across multiple regions, ensuring optimal availability for each request and smoother performance during high-usage periods. There’s no additional routing cost for using cross-region inference with Amazon Bedrock Guardrails. Please find the list of supported regions here. To learn more about the feature and how to get started, refer to the technical documentation.    

Amazon Relational Database Service (Amazon RDS) for Oracle now supports the Spatial Patch Bundle (SPB) for the April 2025 Release Update (RU) for Oracle Database version 19c. This update delivers important fixes for Oracle Spatial and Graph functionality, helping ensure reliable and optimal performance for your spatial operations.

You can now create new DB instances or upgrade existing ones to engine version ‘19.0.0.0.ru-2025-04.spb-1.r1’. The SPB engine version will be visible in the AWS Console by selecting the «Spatial Patch Bundle Engine Versions» checkbox in the engine version selector, making it simple to identify and implement the latest spatial patches for your database environment.

To learn more about Oracle SPBs supported on Amazon RDS for each engine version, see the Amazon RDS for Oracle Release notes. For more information about the AWS Regions where Amazon RDS for Oracle is available, see the AWS Region table.
 

 

​Amazon Relational Database Service (Amazon RDS) for Oracle now supports the Spatial Patch Bundle (SPB) for the April 2025 Release Update (RU) for Oracle Database version 19c. This update delivers important fixes for Oracle Spatial and Graph functionality, helping ensure reliable and optimal performance for your spatial operations. You can now create new DB instances or upgrade existing ones to engine version ‘19.0.0.0.ru-2025-04.spb-1.r1’. The SPB engine version will be visible in the AWS Console by selecting the «Spatial Patch Bundle Engine Versions» checkbox in the engine version selector, making it simple to identify and implement the latest spatial patches for your database environment. To learn more about Oracle SPBs supported on Amazon RDS for each engine version, see the Amazon RDS for Oracle Release notes. For more information about the AWS Regions where Amazon RDS for Oracle is available, see the AWS Region table.