You can now use Amazon MSK Replicator to replicate streaming data across Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in five additional AWS Regions: Asia Pacific (Thailand), Mexico (Central), Asia Pacific (Taipei), Canada West (Calgary), Europe (Spain).

MSK Replicator is a feature of Amazon MSK that enables you to reliably replicate data across Amazon MSK clusters in different or the same AWS Region(s) in a few clicks. With MSK Replicator, you can easily build regionally resilient streaming applications for increased availability and business continuity. MSK Replicator provides automatic asynchronous replication across MSK clusters, eliminating the need to write custom code, manage infrastructure, or setup cross-region networking. MSK Replicator automatically scales the underlying resources so that you can replicate data on-demand without having to monitor or scale capacity. MSK Replicator also replicates the necessary Kafka metadata including topic configurations, Access Control Lists (ACLs), and consumer group offsets. If an unexpected event occurs in a region, you can failover to the other AWS Region and seamlessly resume processing.

You can get started with MSK Replicator from the Amazon MSK console or the Amazon CLI. To learn more, visit the MSK Replicator product page, pricing page, and documentation.

​You can now use Amazon MSK Replicator to replicate streaming data across Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in five additional AWS Regions: Asia Pacific (Thailand), Mexico (Central), Asia Pacific (Taipei), Canada West (Calgary), Europe (Spain). MSK Replicator is a feature of Amazon MSK that enables you to reliably replicate data across Amazon MSK clusters in different or the same AWS Region(s) in a few clicks. With MSK Replicator, you can easily build regionally resilient streaming applications for increased availability and business continuity. MSK Replicator provides automatic asynchronous replication across MSK clusters, eliminating the need to write custom code, manage infrastructure, or setup cross-region networking. MSK Replicator automatically scales the underlying resources so that you can replicate data on-demand without having to monitor or scale capacity. MSK Replicator also replicates the necessary Kafka metadata including topic configurations, Access Control Lists (ACLs), and consumer group offsets. If an unexpected event occurs in a region, you can failover to the other AWS Region and seamlessly resume processing. You can get started with MSK Replicator from the Amazon MSK console or the Amazon CLI. To learn more, visit the MSK Replicator product page, pricing page, and documentation.  

Amazon Redshift now supports federated permissions across multi-warehouse architectures

Amazon Redshift now supports federated permissions, which simplify permissions management across multiple Redshift data warehouses. Customers are adopting multi-warehouse architectures to scale and isolate workloads and are looking for simplified, consistent permissions management across warehouses. With Redshift federated permissions, you define data permissions once from any Redshift warehouse and automatically enforce them across all warehouses in the account.

Amazon Redshift warehouses with federated permissions are auto-mounted in every Redshift warehouse, and you can use existing workforce identities with AWS IAM Identity Center or use existing IAM roles to query data across warehouses. Regardless of which warehouse is used for querying, row-level, column-level, and masking controls always apply automatically, delivering fine-grained access compliance. You can get started by registering a Redshift Serverless namespace or Redshift provisioned cluster with AWS Glue Data Catalog and start querying across warehouses using Redshift Query Editor V2, or any supported SQL client. You get horizontal scalability with multiple warehouses by allowing you to add new warehouses without increasing governance complexity, as new warehouses automatically enforce permission policies and analysts immediately see all databases from registered warehouses.

Amazon Redshift federated permissions is available at no additional cost in supported AWS regions. To learn more, visit the Amazon Redshift documentation.

​Amazon Redshift now supports federated permissions across multi-warehouse architectures Amazon Redshift now supports federated permissions, which simplify permissions management across multiple Redshift data warehouses. Customers are adopting multi-warehouse architectures to scale and isolate workloads and are looking for simplified, consistent permissions management across warehouses. With Redshift federated permissions, you define data permissions once from any Redshift warehouse and automatically enforce them across all warehouses in the account. Amazon Redshift warehouses with federated permissions are auto-mounted in every Redshift warehouse, and you can use existing workforce identities with AWS IAM Identity Center or use existing IAM roles to query data across warehouses. Regardless of which warehouse is used for querying, row-level, column-level, and masking controls always apply automatically, delivering fine-grained access compliance. You can get started by registering a Redshift Serverless namespace or Redshift provisioned cluster with AWS Glue Data Catalog and start querying across warehouses using Redshift Query Editor V2, or any supported SQL client. You get horizontal scalability with multiple warehouses by allowing you to add new warehouses without increasing governance complexity, as new warehouses automatically enforce permission policies and analysts immediately see all databases from registered warehouses. Amazon Redshift federated permissions is available at no additional cost in supported AWS regions. To learn more, visit the Amazon Redshift documentation.  

​The post La Inteligencia Artificial y la energía: Un informe pionero para el futuro sostenible de América Latina y el Caribe appeared first on Source LATAM.  

Amazon SageMaker now supports Amazon Athena for Apache Spark, bringing a new notebook experience and fast serverless Spark experience together within a unified workspace. Now, data engineers, analysts, and data scientists can easily query data, run Python code, develop jobs, train models, visualize data, and work with AI from one place, with no infrastructure to manage and second-level billing.

Athena for Apache Spark scales in seconds to support any workload, from interactive queries to petabyte-scale jobs. Athena for Apache Spark now runs on Spark 3.5.6, the same high-performance Spark engine available across AWS, optimized for open table formats including Apache Iceberg and Delta Lake. It brings you new debugging features, real-time monitoring in the Spark UI, and secure interactive cluster communication through Spark Connect. As you use these capabilities to work with your data, Athena for Spark now enforces table-level access controls defined in AWS Lake Formation.

Athena for Apache Spark is now available with Amazon SageMaker notebooks in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more, visit Apache Spark engine version 3.5, read the AWS News Blog or visit Amazon SageMaker documentation. Visit the Getting Started guide to try it from Amazon SageMaker notebooks.

​Amazon SageMaker now supports Amazon Athena for Apache Spark, bringing a new notebook experience and fast serverless Spark experience together within a unified workspace. Now, data engineers, analysts, and data scientists can easily query data, run Python code, develop jobs, train models, visualize data, and work with AI from one place, with no infrastructure to manage and second-level billing. Athena for Apache Spark scales in seconds to support any workload, from interactive queries to petabyte-scale jobs. Athena for Apache Spark now runs on Spark 3.5.6, the same high-performance Spark engine available across AWS, optimized for open table formats including Apache Iceberg and Delta Lake. It brings you new debugging features, real-time monitoring in the Spark UI, and secure interactive cluster communication through Spark Connect. As you use these capabilities to work with your data, Athena for Spark now enforces table-level access controls defined in AWS Lake Formation.
Athena for Apache Spark is now available with Amazon SageMaker notebooks in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more, visit Apache Spark engine version 3.5, read the AWS News Blog or visit Amazon SageMaker documentation. Visit the Getting Started guide to try it from Amazon SageMaker notebooks.  

Amazon EMR Serverless now supports Apache Spark 4.0.1 (preview). With Spark 4.0.1, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. This enables your teams to reduce technical debt and iterate more quickly, while ensuring data accuracy and consistency.

With Spark 4.0.1, you can build data pipelines with standard ANSI SQL, making it accessible to a larger set of users who don’t know programming languages like Python or Scala. Spark 4.0.1 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can strengthen compliance and governance through Apache Iceberg v3 table format, which provides transaction guarantees and tracks how your data changes over time, creating the audit trails you need for regulatory requirements. You can deploy real-time applications faster with improved streaming controls that let you manage complex stateful operations and monitor streaming jobs more easily. With this capability, you can support use cases like fraud detection and real-time personalization.

Apache Spark 4.0.1 is available in preview in all regions where EMR Serverless is available, excluding China and AWS GovCloud (US) regions. To learn more about Apache Spark 4.0.1 on Amazon EMR, visit the Amazon EMR Serverless release notes, or get started by creating an EMR application with Spark 4.0.1 from the AWS Management Console.

​Amazon EMR Serverless now supports Apache Spark 4.0.1 (preview). With Spark 4.0.1, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. This enables your teams to reduce technical debt and iterate more quickly, while ensuring data accuracy and consistency. With Spark 4.0.1, you can build data pipelines with standard ANSI SQL, making it accessible to a larger set of users who don’t know programming languages like Python or Scala. Spark 4.0.1 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can strengthen compliance and governance through Apache Iceberg v3 table format, which provides transaction guarantees and tracks how your data changes over time, creating the audit trails you need for regulatory requirements. You can deploy real-time applications faster with improved streaming controls that let you manage complex stateful operations and monitor streaming jobs more easily. With this capability, you can support use cases like fraud detection and real-time personalization. Apache Spark 4.0.1 is available in preview in all regions where EMR Serverless is available, excluding China and AWS GovCloud (US) regions. To learn more about Apache Spark 4.0.1 on Amazon EMR, visit the Amazon EMR Serverless release notes, or get started by creating an EMR application with Spark 4.0.1 from the AWS Management Console.  

Today, AWS Payments Cryptography announces support for hybrid post-quantum (PQ) TLS to secure API calls. With this launch, customers can future-proof transmissions of sensitive data and commands using ML-KEM post-quantum cryptography.

Enterprises operating highly regulated workloads wish to reduce post-quantum risks from “harvest now, decrypt later”. Long-lived data-in-transit can be recorded today, then decrypted in the future when a sufficiently capable quantum computer becomes available. With today’s launch, AWS Payment Cryptography joins data protection services such as AWS Key Management Service (KMS) in addressing this concern by supporting PQ-TLS.

To get started, simply ensure that your application depends on a version of AWS SDK or browser that supports PQ-TLS. For detailed guidance by language and platform, visit the PQ-TLS enablement documentation. Customers can also validate that ML-KEM was used to secure the TLS session for an API call by reviewing tlsDetails for the corresponding CloudTrail event in the console or a configured CloudTrail trail.

These capabilities are generally available in all AWS Regions at no added cost. To get started with PQ-TLS and Payment Cyptography, see our post-quantum TLS guide. For more information about PQC at AWS, please see PQC shared responsibility.

​Today, AWS Payments Cryptography announces support for hybrid post-quantum (PQ) TLS to secure API calls. With this launch, customers can future-proof transmissions of sensitive data and commands using ML-KEM post-quantum cryptography. Enterprises operating highly regulated workloads wish to reduce post-quantum risks from “harvest now, decrypt later”. Long-lived data-in-transit can be recorded today, then decrypted in the future when a sufficiently capable quantum computer becomes available. With today’s launch, AWS Payment Cryptography joins data protection services such as AWS Key Management Service (KMS) in addressing this concern by supporting PQ-TLS. To get started, simply ensure that your application depends on a version of AWS SDK or browser that supports PQ-TLS. For detailed guidance by language and platform, visit the PQ-TLS enablement documentation. Customers can also validate that ML-KEM was used to secure the TLS session for an API call by reviewing tlsDetails for the corresponding CloudTrail event in the console or a configured CloudTrail trail. These capabilities are generally available in all AWS Regions at no added cost. To get started with PQ-TLS and Payment Cyptography, see our post-quantum TLS guide. For more information about PQC at AWS, please see PQC shared responsibility.  

Amazon EMR 7.12 is now available featuring the new Apache Iceberg v3 table format with Apache Iceberg 1.10. This release enables you to reduce costs when deleting data, strengthen governance and compliance through better tracking for row level changes, and enhance data security with more granular data access control.

With Iceberg v3, you can delete data cost-effectively because Iceberg v3 marks deleted rows without rewriting entire files – speeding up your data pipelines while reducing storage costs. You get better governance and compliance capabilities through automatic tracking of every row’s creation and modification history, creating the audit trails needed for regulatory requirements and change data capture. You can enhance data security with table-level encryption, helping you meet privacy regulations for your most sensitive data.

With Apache Spark 3.5.6 included in this release, you can leverage these Iceberg 1.10 capabilities for building robust data lakehouse architectures on Amazon S3. This release also includes support for data governance operations across your Iceberg tables using AWS Lake Formation. In addition, this release also includes Apache Trino 476.

Amazon EMR 7.12 is available in all AWS Regions that support Amazon EMR. To learn more about Amazon EMR 7.12 release, visit the Amazon EMR 7.12 release documentation. 

​Amazon EMR 7.12 is now available featuring the new Apache Iceberg v3 table format with Apache Iceberg 1.10. This release enables you to reduce costs when deleting data, strengthen governance and compliance through better tracking for row level changes, and enhance data security with more granular data access control. With Iceberg v3, you can delete data cost-effectively because Iceberg v3 marks deleted rows without rewriting entire files – speeding up your data pipelines while reducing storage costs. You get better governance and compliance capabilities through automatic tracking of every row’s creation and modification history, creating the audit trails needed for regulatory requirements and change data capture. You can enhance data security with table-level encryption, helping you meet privacy regulations for your most sensitive data. With Apache Spark 3.5.6 included in this release, you can leverage these Iceberg 1.10 capabilities for building robust data lakehouse architectures on Amazon S3. This release also includes support for data governance operations across your Iceberg tables using AWS Lake Formation. In addition, this release also includes Apache Trino 476. Amazon EMR 7.12 is available in all AWS Regions that support Amazon EMR. To learn more about Amazon EMR 7.12 release, visit the Amazon EMR 7.12 release documentation.   

AWS Device Farm enables mobile and web developers to test their apps using real mobile devices and desktop browsers. Starting today, you can connect to a fully managed Appium endpoint using only a few lines of code and run interactive tests on multiple physical devices directly from your IDE or local machine. This feature also seamlessly works with third-party tools such as Appium Inspector — both hosted and local versions — for all actions including element inspection.

Support for live video and log streaming enables you to get faster test feedback within your local workflow. It complements our existing server-side execution which gives you the scale and control to run secure enterprise-grade workloads. Taken together, Device Farm now offers you the ability to author, inspect, debug, test, and release mobile apps faster, whether from your IDE, AWS Console, or other environments.

To learn more, see Appium Testing in AWS Device Farm Developer Guide.

​AWS Device Farm enables mobile and web developers to test their apps using real mobile devices and desktop browsers. Starting today, you can connect to a fully managed Appium endpoint using only a few lines of code and run interactive tests on multiple physical devices directly from your IDE or local machine. This feature also seamlessly works with third-party tools such as Appium Inspector — both hosted and local versions — for all actions including element inspection.
Support for live video and log streaming enables you to get faster test feedback within your local workflow. It complements our existing server-side execution which gives you the scale and control to run secure enterprise-grade workloads. Taken together, Device Farm now offers you the ability to author, inspect, debug, test, and release mobile apps faster, whether from your IDE, AWS Console, or other environments.
To learn more, see Appium Testing in AWS Device Farm Developer Guide.  

Today, we’re excited to announce the addition of Web Bot Auth (WBA) support in AWS WAF, providing a secure and standardized way to authenticate legitimate AI agents and automated tools accessing web applications. This new capability helps distinguish trusted bot traffic from potentially harmful automated access attempts.

Web Bot Auth is an authentication method that leverages cryptographic signatures in HTTP messages to verifythat a request comes from an automated bot. Web Bot Auth is used as a verification method for verified bots and signed agents. It relies on two active IETF drafts: a directory draft allowing the crawler to share their public keys, and a protocol draft defining how these keys should be used to attach crawler’s identity to HTTP requests.

AWS WAF now automatically allows verified AI agent traffic Verified WBA bots will now be automatically allowed by default, previously Category AI blocked unverified bots, this behavior is now refined to respect WBA verification.

To learn more, please review the documentation.

​Today, we’re excited to announce the addition of Web Bot Auth (WBA) support in AWS WAF, providing a secure and standardized way to authenticate legitimate AI agents and automated tools accessing web applications. This new capability helps distinguish trusted bot traffic from potentially harmful automated access attempts.
Web Bot Auth is an authentication method that leverages cryptographic signatures in HTTP messages to verifythat a request comes from an automated bot. Web Bot Auth is used as a verification method for verified bots and signed agents. It relies on two active IETF drafts: a directory draft allowing the crawler to share their public keys, and a protocol draft defining how these keys should be used to attach crawler’s identity to HTTP requests.
AWS WAF now automatically allows verified AI agent traffic Verified WBA bots will now be automatically allowed by default, previously Category AI blocked unverified bots, this behavior is now refined to respect WBA verification.
To learn more, please review the documentation.  

Amazon Aurora DSQL now supports a maximum storage limit of 256 TiB, doubling the previous limit of 128 TiB. Now, customers can store and manage larger datasets within a single database cluster, simplifying data management for large-scale applications. With Aurora DSQL, customers only pay for the storage they use and storage automatically scales with usage, ensuring that customers do not need to provision storage upfront.

All Aurora DSQL clusters by default have a storage limit of 10 TiB. Customers that desire clusters with higher storage limits can request a limit increase using either the Service Quotas console or AWS CLI. Visit the Service Quotas documentation for a step-by-step guide to requesting a quote increase.

The increased storage limits are available in all Regions where Aurora DSQL is available. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage and documentation.

​Amazon Aurora DSQL now supports a maximum storage limit of 256 TiB, doubling the previous limit of 128 TiB. Now, customers can store and manage larger datasets within a single database cluster, simplifying data management for large-scale applications. With Aurora DSQL, customers only pay for the storage they use and storage automatically scales with usage, ensuring that customers do not need to provision storage upfront. All Aurora DSQL clusters by default have a storage limit of 10 TiB. Customers that desire clusters with higher storage limits can request a limit increase using either the Service Quotas console or AWS CLI. Visit the Service Quotas documentation for a step-by-step guide to requesting a quote increase. The increased storage limits are available in all Regions where Aurora DSQL is available. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage and documentation.