AWS CodePipeline V2 type pipeline introduces CodeBuild rule and Commands rule that customers can use in their stage level condition to gate a pipeline execution. You can use CodeBuild rule to start a CodeBuild build or Commands rule to run simple shell commands before exiting a stage, when all actions in the stage have completed successfully, or when any action in the stage has failed.

These new rules will provide more flexibility to your deployment process and enable more release safety controls. With these two rules, you can run integration tests as a stage level condition when your deployment completes and automatically roll back or fail your deployment when the integration tests fail. You can also run custom cleanup scripts using these new rules when the stage execution fails.

To learn more about using these rules in stage level conditions in your pipeline, visit our documentation. For more information about AWS CodePipeline, visit our product page. This feature is available in all regions where AWS CodePipeline is supported.
 

 

​AWS CodePipeline V2 type pipeline introduces CodeBuild rule and Commands rule that customers can use in their stage level condition to gate a pipeline execution. You can use CodeBuild rule to start a CodeBuild build or Commands rule to run simple shell commands before exiting a stage, when all actions in the stage have completed successfully, or when any action in the stage has failed. These new rules will provide more flexibility to your deployment process and enable more release safety controls. With these two rules, you can run integration tests as a stage level condition when your deployment completes and automatically roll back or fail your deployment when the integration tests fail. You can also run custom cleanup scripts using these new rules when the stage execution fails. To learn more about using these rules in stage level conditions in your pipeline, visit our documentation. For more information about AWS CodePipeline, visit our product page. This feature is available in all regions where AWS CodePipeline is supported.    

AWS Graviton4-based R8g database instances are now generally available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in Europe (Ireland), Europe (Spain), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo) regions. R8g instances offer larger instance sizes, up to 48xlarge and features an 8:1 ratio of memory to vCPU, and the latest DDR5 memory. Graviton4-based instances provide up to a 40% performance improvement and up to 29% price/performance improvement for on-demand pricing over Graviton3-based instances of equivalent sizes on Amazon Aurora databases, depending on database engine, version, and workload.

AWS Graviton4 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. R8g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, R8g DB instances offer up to 192 vCPU, up to 50Gbps enhanced networking bandwidth, and up to 40Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

You can spin up Gravitona4 R8g database instances in the Amazon RDS Management Console or using the AWS CLI. Upgrading a database instance to Graviton4 requires a simple instance type modification. For more details, refer to the Aurora documentation.

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.

 

​AWS Graviton4-based R8g database instances are now generally available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in Europe (Ireland), Europe (Spain), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo) regions. R8g instances offer larger instance sizes, up to 48xlarge and features an 8:1 ratio of memory to vCPU, and the latest DDR5 memory. Graviton4-based instances provide up to a 40% performance improvement and up to 29% price/performance improvement for on-demand pricing over Graviton3-based instances of equivalent sizes on Amazon Aurora databases, depending on database engine, version, and workload. AWS Graviton4 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. R8g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, R8g DB instances offer up to 192 vCPU, up to 50Gbps enhanced networking bandwidth, and up to 40Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). You can spin up Gravitona4 R8g database instances in the Amazon RDS Management Console or using the AWS CLI. Upgrading a database instance to Graviton4 requires a simple instance type modification. For more details, refer to the Aurora documentation. 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 Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB now supports AWS Graviton4-based M8g database instances in the Europe (Spain), Europe (Stockholm), and Europe (London) Regions and R8g database instances in Europe (Ireland), Europe (Spain), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo) regions. 

Graviton4-based instances provide up to a 40% performance improvement and up to 29% price/performance improvement for on-demand pricing over Graviton3-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload.

M8g and R8g database instances are available on Amazon RDS for PostgreSQL version 17.1 and higher, 16.1 and higher, 15.2 and higher, 14.5 and higher, and 13.8 and higher. M8g and R8g database instances are available on Amazon RDS for MySQL version 8.0.32 and higher, and Amazon RDS for MariaDB version 11.4.3 and higher, 10.11.7 and higher, 10.6.13 and higher, 10.5.20 and higher, and 10.4.29 and higher. For more details on these instances and supported versions for each region, refer to the Amazon RDS User Guide. Get started by creating a fully managed M8g or R8g database instance using the Amazon RDS Management Console.

For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.

 

​Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB now supports AWS Graviton4-based M8g database instances in the Europe (Spain), Europe (Stockholm), and Europe (London) Regions and R8g database instances in Europe (Ireland), Europe (Spain), Europe (Stockholm), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo) regions.  Graviton4-based instances provide up to a 40% performance improvement and up to 29% price/performance improvement for on-demand pricing over Graviton3-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload. M8g and R8g database instances are available on Amazon RDS for PostgreSQL version 17.1 and higher, 16.1 and higher, 15.2 and higher, 14.5 and higher, and 13.8 and higher. M8g and R8g database instances are available on Amazon RDS for MySQL version 8.0.32 and higher, and Amazon RDS for MariaDB version 11.4.3 and higher, 10.11.7 and higher, 10.6.13 and higher, 10.5.20 and higher, and 10.4.29 and higher. For more details on these instances and supported versions for each region, refer to the Amazon RDS User Guide. Get started by creating a fully managed M8g or R8g database instance using the Amazon RDS Management Console. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.  

Amazon Kinesis Data Streams now allows customers to make API requests over Internet Protocol version 6 (IPv6). Customers now have the option of using either IPv6 or IPv4 when sending requests over dual-stack public endpoints.

Kinesis Data Streams allows users to capture, process, and store data streams in real time at any scale. IPv6 increases the number of available addresses by several orders of magnitude, so customers will no longer need to manage overlapping address spaces. Many devices and networks today already use IPv6, and now they can easily write to and read from data streams.

Support for IPv6 with Kinesis Data Streams is available in all Regions where Kinesis Data Streams is available, except for AWS GovCloud (US) and China Regions. See here for a full listing of our Regions. To learn more about Kinesis Data Streams, please refer to our Developer Guide.

 

​Amazon Kinesis Data Streams now allows customers to make API requests over Internet Protocol version 6 (IPv6). Customers now have the option of using either IPv6 or IPv4 when sending requests over dual-stack public endpoints. Kinesis Data Streams allows users to capture, process, and store data streams in real time at any scale. IPv6 increases the number of available addresses by several orders of magnitude, so customers will no longer need to manage overlapping address spaces. Many devices and networks today already use IPv6, and now they can easily write to and read from data streams. Support for IPv6 with Kinesis Data Streams is available in all Regions where Kinesis Data Streams is available, except for AWS GovCloud (US) and China Regions. See here for a full listing of our Regions. To learn more about Kinesis Data Streams, please refer to our Developer Guide.  

Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. Today, we are excited to announce that Amazon EMR Serverless Streaming jobs, which enables you to continuously analyze and process streaming data, is now available in the AWS GovCloud (US) Regions.

Streaming has become vital for businesses to gain continuous insights from data sources like sensors, IoT devices, and web logs. However, processing streaming data can be challenging due to requirements such as high availability, resilience to failures, and integration with streaming services. Amazon EMR Serverless Streaming jobs has built-in features to addresses these challenges. It offers high availability through multi-AZ (Availability Zone) resiliency by automatically failing over to healthy AZs. It also offers increased resiliency through automatic job retries on failures and log management features like log rotation and compaction, preventing the accumulation of log files that might lead to job failures. In addition, Amazon EMR Serverless Streaming jobs support processing data from streaming services like self-managed Apache Kafka clusters, Amazon Managed Streaming for Apache Kafka, and now is integrated with Amazon Kinesis Data Streams using a new built-in Amazon Kinesis Data Streams Connector, making it easier to build end-to-end streaming pipelines.

To get started, visit the Amazon EMR Serverless Streaming jobs page in the Amazon EMR Serverless User Guide.

 

​Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. Today, we are excited to announce that Amazon EMR Serverless Streaming jobs, which enables you to continuously analyze and process streaming data, is now available in the AWS GovCloud (US) Regions. Streaming has become vital for businesses to gain continuous insights from data sources like sensors, IoT devices, and web logs. However, processing streaming data can be challenging due to requirements such as high availability, resilience to failures, and integration with streaming services. Amazon EMR Serverless Streaming jobs has built-in features to addresses these challenges. It offers high availability through multi-AZ (Availability Zone) resiliency by automatically failing over to healthy AZs. It also offers increased resiliency through automatic job retries on failures and log management features like log rotation and compaction, preventing the accumulation of log files that might lead to job failures. In addition, Amazon EMR Serverless Streaming jobs support processing data from streaming services like self-managed Apache Kafka clusters, Amazon Managed Streaming for Apache Kafka, and now is integrated with Amazon Kinesis Data Streams using a new built-in Amazon Kinesis Data Streams Connector, making it easier to build end-to-end streaming pipelines. To get started, visit the Amazon EMR Serverless Streaming jobs page in the Amazon EMR Serverless User Guide.  

Amazon EMR Serverless is now a FedRAMP High authorized service in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Federal agencies, public sector organizations and other enterprises with FedRAMP High compliance requirements can now leverage EMR Serverless to run Apache Spark and Hive workloads.

Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. The Federal Risk and Authorization Management Program (FedRAMP) is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services.

To get started with Amazon EMR Serverless, visit the User Guide.
 

 

​Amazon EMR Serverless is now a FedRAMP High authorized service in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Federal agencies, public sector organizations and other enterprises with FedRAMP High compliance requirements can now leverage EMR Serverless to run Apache Spark and Hive workloads. Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. The Federal Risk and Authorization Management Program (FedRAMP) is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services. To get started with Amazon EMR Serverless, visit the User Guide.    

Today, we are excited to announce the general availability of Amazon Data Firehose (Firehose) integration with Amazon S3 Tables, a feature that enables customers to deliver real-time streaming data into Amazon S3 Tables without requiring any code development or multi-step processes.

Firehose can acquire streaming data from Amazon Kinesis Data Streams, Amazon MSK, Direct PUT API, and AWS Services such as AWS WAF web ACL logs, Amazon VPC Flow Logs. It can then deliver this data to destinations like Amazon S3, Amazon Redshift, OpenSearch, Splunk, Snowflake, and others for analytics. Now, with the Amazon S3 Table integration, customers can stream data from any of these sources directly into Amazon S3 Tables. As a serverless service, Firehose allows customers to simply setup a stream by configuring the source and destination properties, and pay based on bytes processed.

The new feature also enables customers to route records in a data stream to different Amazon S3 tables based on the content of the incoming record. Additionally, customers can automate processing for data correction and right-to-forget scenarios by applying row-level update or delete operations in the destination S3 tables.

To get started, visit Amazon Data Firehose documentation and console.
 

 

​Today, we are excited to announce the general availability of Amazon Data Firehose (Firehose) integration with Amazon S3 Tables, a feature that enables customers to deliver real-time streaming data into Amazon S3 Tables without requiring any code development or multi-step processes. Firehose can acquire streaming data from Amazon Kinesis Data Streams, Amazon MSK, Direct PUT API, and AWS Services such as AWS WAF web ACL logs, Amazon VPC Flow Logs. It can then deliver this data to destinations like Amazon S3, Amazon Redshift, OpenSearch, Splunk, Snowflake, and others for analytics. Now, with the Amazon S3 Table integration, customers can stream data from any of these sources directly into Amazon S3 Tables. As a serverless service, Firehose allows customers to simply setup a stream by configuring the source and destination properties, and pay based on bytes processed. The new feature also enables customers to route records in a data stream to different Amazon S3 tables based on the content of the incoming record. Additionally, customers can automate processing for data correction and right-to-forget scenarios by applying row-level update or delete operations in the destination S3 tables. To get started, visit Amazon Data Firehose documentation and console.    

Amazon S3 Access Grants now authenticate based on the union of both Identity Provider (IdP) and AWS Identity and Access Management (IAM) permissions. This means customers can use AWS machine learning and analytics services such as Amazon SageMaker Unified Studio, Amazon Redshift, and AWS Glue to request access to their S3 data, and S3 Access Grants will grant access to their data after evaluating both their IdP and IAM permissions.

Now, S3 Access Grants evaluate both IAM and IdP permissions so you no longer have to choose between identity contexts when requesting access to S3. With just a few clicks in the AWS Management Console or a few lines of code using the AWS SDK, you can map S3 permissions to users and groups in an existing corporate directory, such as Entra ID and Okta, or to an IAM user or role. S3 Access Grants automatically update S3 permissions based on end user group membership as users are added and removed from groups in the IdP.

Amazon S3 Access Grants are available in all AWS Regions where AWS IAM Identity Center is available. For pricing details, visit Amazon S3 pricing. To learn more about S3 Access Grants, visit the S3 User Guide.
 

 

​Amazon S3 Access Grants now authenticate based on the union of both Identity Provider (IdP) and AWS Identity and Access Management (IAM) permissions. This means customers can use AWS machine learning and analytics services such as Amazon SageMaker Unified Studio, Amazon Redshift, and AWS Glue to request access to their S3 data, and S3 Access Grants will grant access to their data after evaluating both their IdP and IAM permissions. Now, S3 Access Grants evaluate both IAM and IdP permissions so you no longer have to choose between identity contexts when requesting access to S3. With just a few clicks in the AWS Management Console or a few lines of code using the AWS SDK, you can map S3 permissions to users and groups in an existing corporate directory, such as Entra ID and Okta, or to an IAM user or role. S3 Access Grants automatically update S3 permissions based on end user group membership as users are added and removed from groups in the IdP. Amazon S3 Access Grants are available in all AWS Regions where AWS IAM Identity Center is available. For pricing details, visit Amazon S3 pricing. To learn more about S3 Access Grants, visit the S3 User Guide.    

AWS Wickr is excited to announce a redesigned admin experience that’s now fully integrated with the AWS Management Console. We’ve made updates to provide a more intuitive layout, easier navigation, and a more accessible experience. The new console uses the AWS Cloudscape design system for front-end components to give you the consistent and familiar experience you get with the AWS Management Console.

AWS Wickr is a security-first messaging and collaboration service designed to keep internal and external communications secure, private, and compliant. It protects one-to-one and group messaging, voice and video calling, file sharing, screen sharing, and location sharing with end-to-end encryption. Customers have full administrative control to enforce information governance policies, configure ephemeral messaging, and to log both internal and external conversations in an AWS Wickr network to a private data store for data retention and auditing purposes.

AWS Wickr is available in commercial AWS Regions including US East (N. Virginia), Canada (Central), Asia Pacific (Malaysia, Singapore, Sydney, and Tokyo), and Europe (London, Frankfurt, Stockholm, and Zurich). It is also available in GovCloud (US-West) as Department of Defense Impact Level 5 (DoD IL5)-authorized AWS WickrGov.

The new console experience will be made available in phases over the coming weeks. Administrators will still be able to access the classic console for a limited period to ensure a smooth transition to the new experience. To learn more and get started, see the following resources:

• AWS Wickr Administration Guide
• AWS Wickr Product Details

 

​AWS Wickr is excited to announce a redesigned admin experience that’s now fully integrated with the AWS Management Console. We’ve made updates to provide a more intuitive layout, easier navigation, and a more accessible experience. The new console uses the AWS Cloudscape design system for front-end components to give you the consistent and familiar experience you get with the AWS Management Console. AWS Wickr is a security-first messaging and collaboration service designed to keep internal and external communications secure, private, and compliant. It protects one-to-one and group messaging, voice and video calling, file sharing, screen sharing, and location sharing with end-to-end encryption. Customers have full administrative control to enforce information governance policies, configure ephemeral messaging, and to log both internal and external conversations in an AWS Wickr network to a private data store for data retention and auditing purposes. AWS Wickr is available in commercial AWS Regions including US East (N. Virginia), Canada (Central), Asia Pacific (Malaysia, Singapore, Sydney, and Tokyo), and Europe (London, Frankfurt, Stockholm, and Zurich). It is also available in GovCloud (US-West) as Department of Defense Impact Level 5 (DoD IL5)-authorized AWS WickrGov. The new console experience will be made available in phases over the coming weeks. Administrators will still be able to access the classic console for a limited period to ensure a smooth transition to the new experience. To learn more and get started, see the following resources:

AWS Wickr Administration Guide
AWS Wickr Product Details  

Amazon Bedrock now supports fine-tuning for Meta’s Llama 3.2 models (1B, 3B, 11B, and 90B), enabling businesses to customize these generative AI models with their own data. Llama 3.2 models are available in various sizes, from small (1B and 3B) to medium-sized multimodal models (11B and 90B). Llama 3.2 11B and 90B models are the first in the Llama series to support both text and vision tasks, achieved by integrating image encoder representations into the language model. Fine-tuning allows you to adapt Llama 3.2 models for domain-specific tasks, enhancing performance for specialized use cases.

The Llama 3.2 90B model excels in advanced reasoning, long-form text generation, coding, multilingual translation, and image reasoning tasks such as captioning, visual question answering, and document analysis. The Llama 3.2 11B model is designed for content creation, conversational AI, and enterprise applications, with strong performance in text summarization, sentiment analysis, and visual understanding. For resource-constrained scenarios, the lightweight Llama 3.2 1B and 3B models enable on-device applications, excelling in tasks like text summarization, classification, and retrieval while ensuring low latency and enhanced privacy. By fine-tuning Llama 3.2 models in Amazon Bedrock, businesses can further enhance their capabilities for specialized applications, improving accuracy and relevance without needing to build models from scratch.

You can fine-tune Llama 3.2 models in Amazon Bedrock in the US West (Oregon) AWS Region. For pricing, visit the Amazon Bedrock pricing page. To get started, see the Amazon Bedrock user guide and visit the Amazon Bedrock console.
 

 

​Amazon Bedrock now supports fine-tuning for Meta’s Llama 3.2 models (1B, 3B, 11B, and 90B), enabling businesses to customize these generative AI models with their own data. Llama 3.2 models are available in various sizes, from small (1B and 3B) to medium-sized multimodal models (11B and 90B). Llama 3.2 11B and 90B models are the first in the Llama series to support both text and vision tasks, achieved by integrating image encoder representations into the language model. Fine-tuning allows you to adapt Llama 3.2 models for domain-specific tasks, enhancing performance for specialized use cases. The Llama 3.2 90B model excels in advanced reasoning, long-form text generation, coding, multilingual translation, and image reasoning tasks such as captioning, visual question answering, and document analysis. The Llama 3.2 11B model is designed for content creation, conversational AI, and enterprise applications, with strong performance in text summarization, sentiment analysis, and visual understanding. For resource-constrained scenarios, the lightweight Llama 3.2 1B and 3B models enable on-device applications, excelling in tasks like text summarization, classification, and retrieval while ensuring low latency and enhanced privacy. By fine-tuning Llama 3.2 models in Amazon Bedrock, businesses can further enhance their capabilities for specialized applications, improving accuracy and relevance without needing to build models from scratch. You can fine-tune Llama 3.2 models in Amazon Bedrock in the US West (Oregon) AWS Region. For pricing, visit the Amazon Bedrock pricing page. To get started, see the Amazon Bedrock user guide and visit the Amazon Bedrock console.