AWS announces the general availability of Amazon SageMaker Unified Studio, a single data and AI development environment that brings together functionality and tools from AWS analytics and AI/ML services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. This launch includes simplified permissions management that makes it easier to bring existing AWS resources to the unified studio. SageMaker Unified Studio allows you to find, access, and query data and AI assets across your organization, then collaborate in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications. Unified access to your data is provided by Amazon SageMaker Lakehouse and governance capabilities are built in via Amazon SageMaker Catalog.

Amazon Q Developer is now generally available in SageMaker Unified Studio, providing generative AI-powered assistance across the development lifecycle. Amazon Q Developer streamlines development by offering natural language, conversational interfaces that simplify tasks like writing SQL queries, building ETL jobs, troubleshooting, and generating real-time code suggestions. The Free Tier of Amazon Q Developer is available by default in SageMaker Unified Studio; customers with existing Amazon Q Developer Pro Tier subscriptions can access additional features.

Selected capabilities from Amazon Bedrock are also generally available in SageMaker Unified Studio. You can rapidly prototype, customize, and share generative AI applications using high-performing foundation models and advanced features such as Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Amazon Bedrock Agents, and Amazon Bedrock Flows to create tailored solutions aligned to your requirements and responsible AI guidelines.

See Supported Regions for a list of AWS Regions where SageMaker Unified Studio is generally available. To learn more about SageMaker Unified Studio and how it can accelerate data and AI development, see the Amazon SageMaker Unified Studio webpage or documentation. You can start using SageMaker Unified Studio today by selecting “Amazon SageMaker” in the AWS Console.

 

​AWS announces the general availability of Amazon SageMaker Unified Studio, a single data and AI development environment that brings together functionality and tools from AWS analytics and AI/ML services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. This launch includes simplified permissions management that makes it easier to bring existing AWS resources to the unified studio. SageMaker Unified Studio allows you to find, access, and query data and AI assets across your organization, then collaborate in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications. Unified access to your data is provided by Amazon SageMaker Lakehouse and governance capabilities are built in via Amazon SageMaker Catalog.
Amazon Q Developer is now generally available in SageMaker Unified Studio, providing generative AI-powered assistance across the development lifecycle. Amazon Q Developer streamlines development by offering natural language, conversational interfaces that simplify tasks like writing SQL queries, building ETL jobs, troubleshooting, and generating real-time code suggestions. The Free Tier of Amazon Q Developer is available by default in SageMaker Unified Studio; customers with existing Amazon Q Developer Pro Tier subscriptions can access additional features.
Selected capabilities from Amazon Bedrock are also generally available in SageMaker Unified Studio. You can rapidly prototype, customize, and share generative AI applications using high-performing foundation models and advanced features such as Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Amazon Bedrock Agents, and Amazon Bedrock Flows to create tailored solutions aligned to your requirements and responsible AI guidelines.
See Supported Regions for a list of AWS Regions where SageMaker Unified Studio is generally available. To learn more about SageMaker Unified Studio and how it can accelerate data and AI development, see the Amazon SageMaker Unified Studio webpage or documentation. You can start using SageMaker Unified Studio today by selecting “Amazon SageMaker” in the AWS Console.  

Amazon Bedrock’s capabilities are now generally available within Amazon SageMaker Unified Studio, offering a governed collaborative environment that empowers developers to rapidly create and customize generative AI applications. This intuitive interface caters to developers of all skill levels, providing seamless access to Amazon Bedrock’s high-performance foundation models (FMs) and advanced customization tools for collaborative development of tailored generative AI applications.

Amazon Bedrock can be accessed through the AWS Management Console, APIs, or Amazon SageMaker Unified Studio. Its integration in Amazon SageMaker Unified Studio eliminates barriers between data, tools, and developers in the generative AI development process. Teams gain a unified development experience by accessing familiar JupyterLab environments and analytics tools while seamlessly incorporating Amazon Bedrock’s powerful generative AI capabilities—all within the same workspace. Developers can harness Retrieval Augmented Generation (RAG) to build Knowledge Bases from proprietary data sources, utilize Agents and Flows for complex task automation, and implement Guardrails for responsible AI development. This consolidated workspace streamlines complexity, enabling faster prototyping, iteration, and deployment of production-ready, responsible generative AI applications that align with specific business requirements.

Amazon Bedrock in SageMaker Unified Studio can now be accessed in all 12 regions where SageMaker Unified Studio is available, including in Europe, South America, Asia Pacific, US East, US West. For more information on supported regions, please refer to the Amazon SageMaker Unified Studio regions guide.

Learn more about Amazon Bedrock’s capabilities in Amazon SageMaker Unified Studio by visiting the capability page, and get started by enabling a “Generative AI application development” project profile using this admin guide.

 

​Amazon Bedrock’s capabilities are now generally available within Amazon SageMaker Unified Studio, offering a governed collaborative environment that empowers developers to rapidly create and customize generative AI applications. This intuitive interface caters to developers of all skill levels, providing seamless access to Amazon Bedrock’s high-performance foundation models (FMs) and advanced customization tools for collaborative development of tailored generative AI applications. Amazon Bedrock can be accessed through the AWS Management Console, APIs, or Amazon SageMaker Unified Studio. Its integration in Amazon SageMaker Unified Studio eliminates barriers between data, tools, and developers in the generative AI development process. Teams gain a unified development experience by accessing familiar JupyterLab environments and analytics tools while seamlessly incorporating Amazon Bedrock’s powerful generative AI capabilities—all within the same workspace. Developers can harness Retrieval Augmented Generation (RAG) to build Knowledge Bases from proprietary data sources, utilize Agents and Flows for complex task automation, and implement Guardrails for responsible AI development. This consolidated workspace streamlines complexity, enabling faster prototyping, iteration, and deployment of production-ready, responsible generative AI applications that align with specific business requirements. Amazon Bedrock in SageMaker Unified Studio can now be accessed in all 12 regions where SageMaker Unified Studio is available, including in Europe, South America, Asia Pacific, US East, US West. For more information on supported regions, please refer to the Amazon SageMaker Unified Studio regions guide. Learn more about Amazon Bedrock’s capabilities in Amazon SageMaker Unified Studio by visiting the capability page, and get started by enabling a “Generative AI application development” project profile using this admin guide.  

Amazon S3 Tables now offer table management APIs that are compatible with the Apache Iceberg REST Catalog standard, enabling any Iceberg-compatible application to easily create, update, list, and delete tables in an S3 table bucket.

These new table management APIs, that map directly to S3 Tables operations, make it easier for you to get started with S3 Tables if you have a custom catalog implementation, need only basic read and write access to tabular data in a single S3 table bucket, or use an APN partner-provided catalog. For unified data management across all of your tabular data, data governance, and fine-grained access controls, you can use S3 Tables with SageMaker Lakehouse.

The new table management APIs are available in all AWS Regions where S3 Tables are available, at no additional cost. To learn more about S3 Tables, visit the documentation and product page. To learn more about SageMaker Lakehouse, visit the product page.

 

​Amazon S3 Tables now offer table management APIs that are compatible with the Apache Iceberg REST Catalog standard, enabling any Iceberg-compatible application to easily create, update, list, and delete tables in an S3 table bucket. These new table management APIs, that map directly to S3 Tables operations, make it easier for you to get started with S3 Tables if you have a custom catalog implementation, need only basic read and write access to tabular data in a single S3 table bucket, or use an APN partner-provided catalog. For unified data management across all of your tabular data, data governance, and fine-grained access controls, you can use S3 Tables with SageMaker Lakehouse. The new table management APIs are available in all AWS Regions where S3 Tables are available, at no additional cost. To learn more about S3 Tables, visit the documentation and product page. To learn more about SageMaker Lakehouse, visit the product page.  

AWS AppSync Events is a fully managed service that allows developers to create secure and performant WebSocket APIs. Starting today, developers can use their AppSync Events APIs to publish events directly over WebSocket connections, complementing the existing HTTP API publishing capability. This enhancement enables applications to both publish and subscribe to events using a single WebSocket connection, streamlining the implementation of real-time features.

The new WebSocket publishing capability simplifies the development of collaborative applications such as chat systems, multiplayer games, and shared document editing. Developers can now maintain a single connection for bi-directional communication, reducing complexity and improving performance by eliminating the need to manage separate connections for publishing and subscribing to events. This approach helps reduce latency in real-time interactive applications by removing the overhead of establishing new HTTP connections for each event publication.

This feature is now available in all AWS Regions where AWS AppSync is supported.

To get started, developers can use their favorite WebSocket client. For more information, view our new blog post and visit the AWS AppSync documentation for detailed implementation examples and best practices.

 

​AWS AppSync Events is a fully managed service that allows developers to create secure and performant WebSocket APIs. Starting today, developers can use their AppSync Events APIs to publish events directly over WebSocket connections, complementing the existing HTTP API publishing capability. This enhancement enables applications to both publish and subscribe to events using a single WebSocket connection, streamlining the implementation of real-time features. The new WebSocket publishing capability simplifies the development of collaborative applications such as chat systems, multiplayer games, and shared document editing. Developers can now maintain a single connection for bi-directional communication, reducing complexity and improving performance by eliminating the need to manage separate connections for publishing and subscribing to events. This approach helps reduce latency in real-time interactive applications by removing the overhead of establishing new HTTP connections for each event publication. This feature is now available in all AWS Regions where AWS AppSync is supported. To get started, developers can use their favorite WebSocket client. For more information, view our new blog post and visit the AWS AppSync documentation for detailed implementation examples and best practices.  

Amazon S3 Tables now support create and query table operations directly from the S3 console using Amazon Athena. With this new feature, you can now create a table, populate it with data, and query it with just a few steps in the S3 console.

To get started, enable S3 Tables integration with Amazon SageMaker Lakehouse, which allows AWS analytics services to automatically discover and access your S3 Tables data. Then, select a table bucket and select “Create table with Athena”, or select an existing table and select “Query table with Athena”.

As the first cloud object store with built-in Apache Iceberg support, S3 Tables offer the easiest way to store tabular data at scale. You can access S3 Tables with AWS analytics services through the now generally available SageMaker Lakehouse integration, as well as Apache Iceberg-compatible open source engines like Apache Spark and Apache Flink.

This support is available in all AWS Regions where S3 Tables are available. To learn more about S3 Tables, visit the product page and documentation. To learn more about the integration between S3 Tables and SageMaker Lakehouse, read the AWS News Blog.
 

 

​Amazon S3 Tables now support create and query table operations directly from the S3 console using Amazon Athena. With this new feature, you can now create a table, populate it with data, and query it with just a few steps in the S3 console. To get started, enable S3 Tables integration with Amazon SageMaker Lakehouse, which allows AWS analytics services to automatically discover and access your S3 Tables data. Then, select a table bucket and select “Create table with Athena”, or select an existing table and select “Query table with Athena”. As the first cloud object store with built-in Apache Iceberg support, S3 Tables offer the easiest way to store tabular data at scale. You can access S3 Tables with AWS analytics services through the now generally available SageMaker Lakehouse integration, as well as Apache Iceberg-compatible open source engines like Apache Spark and Apache Flink. This support is available in all AWS Regions where S3 Tables are available. To learn more about S3 Tables, visit the product page and documentation. To learn more about the integration between S3 Tables and SageMaker Lakehouse, read the AWS News Blog.    

AWS announces the general availability of one new larger sizes (48xlarge) on Amazon EC2 I8g instances in US East(N. Virginia) and US West(Oregon) regions. The new size expand the I8g portfolio supporting up to 192vCPUs, providing additional compute options to scale-up existing workloads or run larger sized applications that need additional CPU and memory. I8g instances are powered by AWS Graviton4 processors that deliver up to 60% better compute performance compared to previous generation I4g instances. I8g instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 65% better real-time storage performance per TB while offering up to 50% lower storage I/O latency and up to 60% lower storage I/O latency variability. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads.

I8g instances offer instance sizes up to 48xlarge, 1,536 GiB of memory, and 45 TB instance storage. They are ideal for real-time applications like relational databases, non-relational databases, streaming databases, search queries and data analytic.

To learn more, see Amazon I8g instances. To learn how to migrate your workloads to AWS Graviton-based instances, see the Getting started with Graviton. To get started, see the AWS Management Console.
 

 

​AWS announces the general availability of one new larger sizes (48xlarge) on Amazon EC2 I8g instances in US East(N. Virginia) and US West(Oregon) regions. The new size expand the I8g portfolio supporting up to 192vCPUs, providing additional compute options to scale-up existing workloads or run larger sized applications that need additional CPU and memory. I8g instances are powered by AWS Graviton4 processors that deliver up to 60% better compute performance compared to previous generation I4g instances. I8g instances use the latest third generation AWS Nitro SSDs, local NVMe storage that deliver up to 65% better real-time storage performance per TB while offering up to 50% lower storage I/O latency and up to 60% lower storage I/O latency variability. These instances are built on the AWS Nitro System, which offloads CPU virtualization, storage, and networking functions to dedicated hardware and software enhancing the performance and security for your workloads. I8g instances offer instance sizes up to 48xlarge, 1,536 GiB of memory, and 45 TB instance storage. They are ideal for real-time applications like relational databases, non-relational databases, streaming databases, search queries and data analytic. To learn more, see Amazon I8g instances. To learn how to migrate your workloads to AWS Graviton-based instances, see the Getting started with Graviton. To get started, see the AWS Management Console.    

AWS Amplify Hosting is excited to offer Skew Protection, a powerful feature that guarantees version consistency across your deployments. This feature ensures frontend requests are always routed to the correct server backend version—eliminating version skew and making deployments more reliable.

You can enable this feature at the branch level in the Amplify Console under App Settings → Branch Settings. There is no additional cost associated with this feature and it is available to all customers.

This feature is available in all 20 AWS Amplify Hosting regions: US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Osaka) Asia Pacific (Seoul), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Milan), Europe (Ireland), Europe (London), Europe (Paris), Middle East (Bahrain) and South America (São Paulo).

To get started, check out our blog post. Or read the documentation.
 

 

​AWS Amplify Hosting is excited to offer Skew Protection, a powerful feature that guarantees version consistency across your deployments. This feature ensures frontend requests are always routed to the correct server backend version—eliminating version skew and making deployments more reliable. You can enable this feature at the branch level in the Amplify Console under App Settings → Branch Settings. There is no additional cost associated with this feature and it is available to all customers. This feature is available in all 20 AWS Amplify Hosting regions: US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Osaka) Asia Pacific (Seoul), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (Milan), Europe (Ireland), Europe (London), Europe (Paris), Middle East (Bahrain) and South America (São Paulo). To get started, check out our blog post. Or read the documentation.    

Today, Amazon Web Services (AWS) announces the availability of Amazon GuardDuty Malware Protection for Amazon S3 in AWS GovCloud (US) regions. This expansion of GuardDuty Malware Protection allows you to scan newly uploaded objects to Amazon S3 buckets for potential malware, viruses, and other suspicious uploads and take action to isolate them before they are ingested into downstream processes.

GuardDuty helps customers protect millions of Amazon S3 buckets and AWS accounts. GuardDuty Malware Protection for Amazon S3 is fully managed by AWS, alleviating the operational complexity and overhead that normally comes with managing a data-scanning pipeline, with compute infrastructure operated on your behalf. This feature also gives application owners more control over the security of their organization’s S3 buckets; they can enable GuardDuty Malware Protection for S3 even if core GuardDuty is not enabled in the account. Application owners are automatically notified of the scan results using Amazon EventBridge to build downstream workflows, such as isolation to a quarantine bucket, or define bucket policies using tags that prevent users or applications from accessing certain objects.

GuardDuty Malware Protection for Amazon S3 is available in all AWS Regions where GuardDuty is available, excluding China Regions.
 

 

​Today, Amazon Web Services (AWS) announces the availability of Amazon GuardDuty Malware Protection for Amazon S3 in AWS GovCloud (US) regions. This expansion of GuardDuty Malware Protection allows you to scan newly uploaded objects to Amazon S3 buckets for potential malware, viruses, and other suspicious uploads and take action to isolate them before they are ingested into downstream processes. GuardDuty helps customers protect millions of Amazon S3 buckets and AWS accounts. GuardDuty Malware Protection for Amazon S3 is fully managed by AWS, alleviating the operational complexity and overhead that normally comes with managing a data-scanning pipeline, with compute infrastructure operated on your behalf. This feature also gives application owners more control over the security of their organization’s S3 buckets; they can enable GuardDuty Malware Protection for S3 even if core GuardDuty is not enabled in the account. Application owners are automatically notified of the scan results using Amazon EventBridge to build downstream workflows, such as isolation to a quarantine bucket, or define bucket policies using tags that prevent users or applications from accessing certain objects. GuardDuty Malware Protection for Amazon S3 is available in all AWS Regions where GuardDuty is available, excluding China Regions.    

Amazon S3 reduces pricing for S3 object tagging by 35% in all AWS Regions to $0.0065 per 10,000 tags per month. Object tags are key-value pairs applied to S3 objects that can be created, updated, or deleted at any time during the lifetime of the object.

S3 object tags help you logically group data for a variety of reasons such as to apply IAM policies to provide fine-grained access, to specify tag-based filters to manage object lifecycle rules, and to selectively replicate data to another AWS Region. Additionally, in AWS Regions where S3 Metadata is available, you can easily capture and query custom metadata that is stored as object tags.

S3 object tags are available in all AWS Regions including the AWS China and AWS GovCloud (US) Regions. This new pricing takes effect automatically in the monthly billing cycle starting on March 1, 2025. To learn more about object tags, refer to the documentation. For more pricing details, visit the S3 pricing page.

 

​Amazon S3 reduces pricing for S3 object tagging by 35% in all AWS Regions to $0.0065 per 10,000 tags per month. Object tags are key-value pairs applied to S3 objects that can be created, updated, or deleted at any time during the lifetime of the object. S3 object tags help you logically group data for a variety of reasons such as to apply IAM policies to provide fine-grained access, to specify tag-based filters to manage object lifecycle rules, and to selectively replicate data to another AWS Region. Additionally, in AWS Regions where S3 Metadata is available, you can easily capture and query custom metadata that is stored as object tags. S3 object tags are available in all AWS Regions including the AWS China and AWS GovCloud (US) Regions. This new pricing takes effect automatically in the monthly billing cycle starting on March 1, 2025. To learn more about object tags, refer to the documentation. For more pricing details, visit the S3 pricing page.  

Customers can use regional processing profiles for Amazon Nova understanding models (Amazon Nova Lite, Amazon Nova Micro, and Amazon Nova Pro) in the Europe (Milan) and Europe (Spain) regions.

Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models (LLMs) and other FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, as well as Amazon via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance.

To get started, visit the Amazon Bedrock page and see the Amazon Bedrock documentation for more details.
 

 

​Customers can use regional processing profiles for Amazon Nova understanding models (Amazon Nova Lite, Amazon Nova Micro, and Amazon Nova Pro) in the Europe (Milan) and Europe (Spain) regions. Amazon Bedrock is a fully managed service that offers a choice of high-performing large language models (LLMs) and other FMs from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, as well as Amazon via a single API. Amazon Bedrock also provides a broad set of capabilities customers need to build generative AI applications with security, privacy, and responsible AI built in. These capabilities help you build tailored applications for multiple use cases across different industries, helping organizations unlock sustained growth from generative AI while ensuring customer trust and data governance. To get started, visit the Amazon Bedrock page and see the Amazon Bedrock documentation for more details.