Amazon OpenSearch Service introduces two new instances- OR2 and OM2, expanding the OpenSearch Optimized Instance family. The new generation OR2 instance delivers up to 26% higher indexing throughput compared to previous OR1 instances and 70% over R7ginstances. The new OM2 instance delivers up to 15% higher indexing throughput compared to OR1 instances and 66% over M7g instances in internal benchmarks.

The new generation OpenSearch Optimized instances use the same architecture as the OR1 instances, leveraging best-in-class cloud technologies like Amazon S3, to provide high durability, and improved price-performance for higher indexing throughput better for indexing heavy workload. Each OpenSearch Optimized instance is provisioned with compute, local instance storage for caching, and remote Amazon S3-based managed storage. OR2 and OM2 offers pay-as-you-go pricing and reserved instances, with a simple hourly rate for the instance, local instance storage, as well as the managed storage provisioned. OR2 instances come in sizes ‘medium’ through ‘16xlarge’, and offer compute, memory, and storage flexibility. OM2 instances come in sizes ‘large’ through ‘16xlarge’ Please refer to the Amazon OpenSearch Service pricing page for pricing details.

OR2 instance family is now available on Amazon OpenSearch Service across 10 regions globally: US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland, Frankfurt, Stockholm, Spain), and Asia Pacific (Sydney, Tokyo, Mumbai).

OM2 instance family is now available on Amazon OpenSearch Service across 6 regions globally: US East (N. Virginia, Ohio), US West (Oregon) and Europe (London, Frankfurt, Stockholm).
 

 

​Amazon OpenSearch Service introduces two new instances- OR2 and OM2, expanding the OpenSearch Optimized Instance family. The new generation OR2 instance delivers up to 26% higher indexing throughput compared to previous OR1 instances and 70% over R7ginstances. The new OM2 instance delivers up to 15% higher indexing throughput compared to OR1 instances and 66% over M7g instances in internal benchmarks. The new generation OpenSearch Optimized instances use the same architecture as the OR1 instances, leveraging best-in-class cloud technologies like Amazon S3, to provide high durability, and improved price-performance for higher indexing throughput better for indexing heavy workload. Each OpenSearch Optimized instance is provisioned with compute, local instance storage for caching, and remote Amazon S3-based managed storage. OR2 and OM2 offers pay-as-you-go pricing and reserved instances, with a simple hourly rate for the instance, local instance storage, as well as the managed storage provisioned. OR2 instances come in sizes ‘medium’ through ‘16xlarge’, and offer compute, memory, and storage flexibility. OM2 instances come in sizes ‘large’ through ‘16xlarge’ Please refer to the Amazon OpenSearch Service pricing page for pricing details. OR2 instance family is now available on Amazon OpenSearch Service across 10 regions globally: US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland, Frankfurt, Stockholm, Spain), and Asia Pacific (Sydney, Tokyo, Mumbai). OM2 instance family is now available on Amazon OpenSearch Service across 6 regions globally: US East (N. Virginia, Ohio), US West (Oregon) and Europe (London, Frankfurt, Stockholm).    

Amazon DataZone is now available in 2 additional commercial regions: Asia Pacific (Mumbai) and Europe (Paris).

Amazon DataZone is a fully managed data management service to catalog, discover, analyze, share, and govern data between data producers and consumers in your organization. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other business use case collaborators. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing-and-subscription workflow provides access-auditing capabilities across projects.

For more information on AWS Regions where Amazon DataZone is available in preview, see supported regions.

Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration for data and AI across your Lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone) and SageMaker Unified Studio, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or you could just ask Amazon Q Developer with natural language to find your data. For more information on AWS Regions where the next generation of SageMaker is available, see supported regions. To learn more about the next generation of SageMaker, visit the product webpage.
 

 

​Amazon DataZone is now available in 2 additional commercial regions: Asia Pacific (Mumbai) and Europe (Paris). Amazon DataZone is a fully managed data management service to catalog, discover, analyze, share, and govern data between data producers and consumers in your organization. With Amazon DataZone, data producers populate the business data catalog with structured data assets from AWS Glue Data Catalog and Amazon Redshift tables. Data consumers search and subscribe to data assets in the data catalog and share with other business use case collaborators. Consumers can analyze their subscribed data assets with tools—such as Amazon Redshift or Amazon Athena query editors—that are directly accessed from the Amazon DataZone portal. The integrated publishing-and-subscription workflow provides access-auditing capabilities across projects. For more information on AWS Regions where Amazon DataZone is available in preview, see supported regions. Additionally, Amazon DataZone powers governance in the next generation of Amazon SageMaker, which simplifies the discovery, governance, and collaboration for data and AI across your Lakehouse, AI models, and GenAI applications. With Amazon SageMaker Catalog (built on Amazon DataZone) and SageMaker Unified Studio, users can securely discover and access approved data and models using semantic search with generative AI–created metadata, or you could just ask Amazon Q Developer with natural language to find your data. For more information on AWS Regions where the next generation of SageMaker is available, see supported regions. To learn more about the next generation of SageMaker, visit the product webpage.    

Amazon EventBridge Scheduler now supports AWS PrivateLink, providing you access to Scheduler from within your Amazon Virtual Private Cloud (VPC) without using the public internet. This feature eliminates the need for an internet gateway, firewall rules, or proxy servers when accessing EventBridge Scheduler from a private subnet.

With Amazon EventBridge Scheduler, you can create billions of scheduled events and tasks that run across more than 270 AWS services, without provisioning or managing infrastructure. You can set up one-time schedules for specific dates and times, or create recurring schedules using cron and rate expressions, with support for time zones and daylight savings. With AWS PrivateLink support in EventBridge Scheduler, you can reduce the infrastructure required to create and manage your schedules when making API calls to Scheduler from within your VPC.

AWS PrivateLink support for EventBridge Scheduler is available in all AWS Regions where EventBridge Scheduler is offered. Using this feature incurs no additional cost, but standard AWS PrivateLink pricing applies.

For PrivateLink configuration instructions, refer to the AWS PrivateLink documentation. To learn more about Amazon EventBridge Scheduler and its capabilities, see the EventBridge documentation.
 

 

​Amazon EventBridge Scheduler now supports AWS PrivateLink, providing you access to Scheduler from within your Amazon Virtual Private Cloud (VPC) without using the public internet. This feature eliminates the need for an internet gateway, firewall rules, or proxy servers when accessing EventBridge Scheduler from a private subnet. With Amazon EventBridge Scheduler, you can create billions of scheduled events and tasks that run across more than 270 AWS services, without provisioning or managing infrastructure. You can set up one-time schedules for specific dates and times, or create recurring schedules using cron and rate expressions, with support for time zones and daylight savings. With AWS PrivateLink support in EventBridge Scheduler, you can reduce the infrastructure required to create and manage your schedules when making API calls to Scheduler from within your VPC. AWS PrivateLink support for EventBridge Scheduler is available in all AWS Regions where EventBridge Scheduler is offered. Using this feature incurs no additional cost, but standard AWS PrivateLink pricing applies. For PrivateLink configuration instructions, refer to the AWS PrivateLink documentation. To learn more about Amazon EventBridge Scheduler and its capabilities, see the EventBridge documentation.    

The next generation of Amazon SageMaker is now available in two additional AWS Regions: Asia Pacific (Mumbai), and Europe (Paris).

Amazon SageMaker is the center for all your data, analytics, and AI. Users can access all their data and tools from Amazon SageMaker Unified Studio, a single data and AI development environment that brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. Unified access to data is provided by Amazon SageMaker Lakehouse, and catalog and governance features are available via SageMaker Catalog (built on Amazon DataZone) to help you meet enterprise security requirements.

For more information on AWS Regions where the next generation of Amazon SageMaker is available, see supported regions.

To get started, see the following resources:

• SageMaker overview
• SageMaker documentation

 

​The next generation of Amazon SageMaker is now available in two additional AWS Regions: Asia Pacific (Mumbai), and Europe (Paris). Amazon SageMaker is the center for all your data, analytics, and AI. Users can access all their data and tools from Amazon SageMaker Unified Studio, a single data and AI development environment that brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI. Unified access to data is provided by Amazon SageMaker Lakehouse, and catalog and governance features are available via SageMaker Catalog (built on Amazon DataZone) to help you meet enterprise security requirements. For more information on AWS Regions where the next generation of Amazon SageMaker is available, see supported regions. To get started, see the following resources:

SageMaker overview
SageMaker documentation  

Amazon RDS for MySQL now supports MySQL Innovation Release 9.2 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.2 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases.

MySQL 9.2 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next major & minor release, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.2 release notes for more details about this release.

The Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment.

Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region.

 

​Amazon RDS for MySQL now supports MySQL Innovation Release 9.2 in the Amazon RDS Database Preview Environment, allowing you to evaluate the latest Innovation Release on Amazon RDS for MySQL. You can deploy MySQL 9.2 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database, making it simpler to set up, operate, and monitor databases. MySQL 9.2 is the latest Innovation Release from the MySQL community. MySQL Innovation releases include bug fixes, security patches, as well as new features. MySQL Innovation releases are supported by the community until the next major & minor release, whereas MySQL Long Term Support (LTS) Releases, such as MySQL 8.0 and MySQL 8.4, are supported by the community for up to eight years. Please refer to the MySQL 9.2 release notes for more details about this release. The Amazon RDS Database Preview Environment supports both Single-AZ and Multi-AZ deployments on the latest generation of instance classes. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the preview environment. Amazon RDS Database Preview Environment database instances are priced the same as production RDS instances created in the US East (Ohio) Region.  

Amazon Redshift announces the general availability of Query Editor V2 with Amazon Redshift in the AWS Mexico (Central) and Asia Pacific (Thailand) regions. Amazon Redshift Query Editor V2 makes data in your Amazon Redshift data warehouse and data lake more accessible with a web-based tool for SQL users such as data analysts, data scientists, and database developers. With Query Editor V2, users can explore, analyze, and collaborate on data. It reduces the operational costs of managing query tools by providing a web-based application that allows you to focus on exploring your data without managing your infrastructure.

The Amazon Redshift Query Editor V2 is a separate web-based SQL client application that you use to author and run queries on your Amazon Redshift data warehouse. You can use it to edit and run queries, visualize results, and share your work with your team. With Amazon Redshift Query Editor V2, you can create databases, schemas, tables, and user-defined functions (UDFs). In a tree-view panel, for each of your databases, you can view its schemas. For each schema, you can view its tables, views, UDFs, and stored procedures. The Amazon Redshift Query Editor V2 comes with sample data and notebooks available for you to be loaded into a sample database and corresponding schema. You can use it to load data into a database in an Amazon Redshift cluster or workgroup.

To learn more, see the documentation or the demo.

 

​Amazon Redshift announces the general availability of Query Editor V2 with Amazon Redshift in the AWS Mexico (Central) and Asia Pacific (Thailand) regions. Amazon Redshift Query Editor V2 makes data in your Amazon Redshift data warehouse and data lake more accessible with a web-based tool for SQL users such as data analysts, data scientists, and database developers. With Query Editor V2, users can explore, analyze, and collaborate on data. It reduces the operational costs of managing query tools by providing a web-based application that allows you to focus on exploring your data without managing your infrastructure. The Amazon Redshift Query Editor V2 is a separate web-based SQL client application that you use to author and run queries on your Amazon Redshift data warehouse. You can use it to edit and run queries, visualize results, and share your work with your team. With Amazon Redshift Query Editor V2, you can create databases, schemas, tables, and user-defined functions (UDFs). In a tree-view panel, for each of your databases, you can view its schemas. For each schema, you can view its tables, views, UDFs, and stored procedures. The Amazon Redshift Query Editor V2 comes with sample data and notebooks available for you to be loaded into a sample database and corresponding schema. You can use it to load data into a database in an Amazon Redshift cluster or workgroup. To learn more, see the documentation or the demo.  

Today, AWS announces the general availability of the scenarios capability of Amazon Q in QuickSight.

Amazon Q guides you through data analysis by uncovering hidden trends, making recommendations for your business, and intelligently suggesting next steps for deeper exploration—all in response to natural language interactions. Now anyone can explore past trends, forecast future scenarios, and model solutions without needing specialized skill, analyst support, or manual manipulation of data in spreadsheets. With its intuitive interface and step-by-step guidance, the scenarios capability of Amazon Q in QuickSight helps users perform complex data analysis up to 10x faster than spreadsheets. Whether you’re optimizing marketing budgets, streamlining supply chains, or analyzing investments, Amazon Q makes advanced data analysis accessible so you can make data-driven decisions across your organization. This capability is accessible from any Amazon QuickSight dashboard, so you can move seamlessly from visualizing data to asking what-if questions and comparing alternatives. Previous analyses can be easily modified, extended, and reused, helping you quickly adapt to changing business needs.

The scenarios capability is available to Amazon Q in QuickSight Pro users in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Europe (Ireland).

To learn more, visit Amazon Q in QuickSight and explore the documentation. 

 

​Today, AWS announces the general availability of the scenarios capability of Amazon Q in QuickSight.
Amazon Q guides you through data analysis by uncovering hidden trends, making recommendations for your business, and intelligently suggesting next steps for deeper exploration—all in response to natural language interactions. Now anyone can explore past trends, forecast future scenarios, and model solutions without needing specialized skill, analyst support, or manual manipulation of data in spreadsheets. With its intuitive interface and step-by-step guidance, the scenarios capability of Amazon Q in QuickSight helps users perform complex data analysis up to 10x faster than spreadsheets. Whether you’re optimizing marketing budgets, streamlining supply chains, or analyzing investments, Amazon Q makes advanced data analysis accessible so you can make data-driven decisions across your organization. This capability is accessible from any Amazon QuickSight dashboard, so you can move seamlessly from visualizing data to asking what-if questions and comparing alternatives. Previous analyses can be easily modified, extended, and reused, helping you quickly adapt to changing business needs.
The scenarios capability is available to Amazon Q in QuickSight Pro users in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Europe (Ireland).
To learn more, visit Amazon Q in QuickSight and explore the documentation.   

Starting today, AWS Elemental MediaConnect will support NDI® (Network Device Interface) outputs from MediaConnect flows. NDI is a high-quality and low-latency video connectivity technology, widely used in live production applications and supported by more than 500 hardware products and 300 software applications.

At launch, the MediaConnect support for NDI allows you to take an incoming transport stream source encoded as AVC or HEVC up to 1080p at 60FPS and output it as NDI High Bandwidth to a VPC. NDI enabled flows can simultaneously output NDI as well as transport stream-based outputs. NDI outputs will use the field-proven SpeedHQ codec and allow you to configure the quality between 100% and 200%. With NDI outputs, the process of connecting on-premises sources such as cameras for use in live cloud production is simpler to deploy, more scalable, and cost-effective using a pay-as-you-go pricing model.

NDI support is available in all regions where MediaConnect is currently deployed. For more information and details on pricing, please refer to the NDI documentation and the MediaConnect pricing page.

 

​Starting today, AWS Elemental MediaConnect will support NDI® (Network Device Interface) outputs from MediaConnect flows. NDI is a high-quality and low-latency video connectivity technology, widely used in live production applications and supported by more than 500 hardware products and 300 software applications. At launch, the MediaConnect support for NDI allows you to take an incoming transport stream source encoded as AVC or HEVC up to 1080p at 60FPS and output it as NDI High Bandwidth to a VPC. NDI enabled flows can simultaneously output NDI as well as transport stream-based outputs. NDI outputs will use the field-proven SpeedHQ codec and allow you to configure the quality between 100% and 200%. With NDI outputs, the process of connecting on-premises sources such as cameras for use in live cloud production is simpler to deploy, more scalable, and cost-effective using a pay-as-you-go pricing model. NDI support is available in all regions where MediaConnect is currently deployed. For more information and details on pricing, please refer to the NDI documentation and the MediaConnect pricing page.  

Today, AWS announced upgrades to the Amazon Q Business Slack and Teams integrations. The upgrades include the ability to create multiple Amazon Q Business integrations within a Slack workspace or Teams organization, free text feedback support, improved response and source formatting, and the support for larger file attachments with user queries.

With the support for multiple integrations, customers can now deploy and test up to ten integrations at a time in their Slack workspace or Teams organization and maintain separate integrations for testing, production, and different user groups. With the ability to monitor feedback, customers can review their users’ satisfaction and collect valuable feedback to help improve their applications’ performance and accuracy.

With the improvements to response and source formatting and larger file attachments, users can enjoy a more seamless experience when accessing Amazon Q Business in the context of their conversations and when sharing Amazon Q Business’s responses through Slack and Teams messages.

These new features are available on the Amazon Q Business Slack and Teams integrations in all regions where Amazon Q Business is available.

To learn more, visit the Amazon Q Business product page or review the documentation for detailed setup instructions and feature descriptions.
 

 

​Today, AWS announced upgrades to the Amazon Q Business Slack and Teams integrations. The upgrades include the ability to create multiple Amazon Q Business integrations within a Slack workspace or Teams organization, free text feedback support, improved response and source formatting, and the support for larger file attachments with user queries. With the support for multiple integrations, customers can now deploy and test up to ten integrations at a time in their Slack workspace or Teams organization and maintain separate integrations for testing, production, and different user groups. With the ability to monitor feedback, customers can review their users’ satisfaction and collect valuable feedback to help improve their applications’ performance and accuracy. With the improvements to response and source formatting and larger file attachments, users can enjoy a more seamless experience when accessing Amazon Q Business in the context of their conversations and when sharing Amazon Q Business’s responses through Slack and Teams messages. These new features are available on the Amazon Q Business Slack and Teams integrations in all regions where Amazon Q Business is available. To learn more, visit the Amazon Q Business product page or review the documentation for detailed setup instructions and feature descriptions.    

AWS Database Migration Service (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we are excited to announce that Schema Conversion now supports conversions from IBM Db2 for z/OS to Amazon Relational Database Service (RDS) for Db2.

Using Schema Conversion, you can automatically convert database objects from your IBM Db2 for z/OS source to an Amazon RDS for Db2 target, including stored procedures, functions, views, and other database structures. This is especially valuable for mainframe migrations as it simplifies complex processes by resolving syntax differences and compatibility issues between environments. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively.

To learn more refer to using IBM Db2 z/OS as a source for AWS DMS Schema Conversion and using IBM Db2 for z/OS as a source for AWS DMS. For AWS DMS Schema Conversion regional availability, please refer to the AWS Region Table.
 

 

​AWS Database Migration Service (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we are excited to announce that Schema Conversion now supports conversions from IBM Db2 for z/OS to Amazon Relational Database Service (RDS) for Db2. Using Schema Conversion, you can automatically convert database objects from your IBM Db2 for z/OS source to an Amazon RDS for Db2 target, including stored procedures, functions, views, and other database structures. This is especially valuable for mainframe migrations as it simplifies complex processes by resolving syntax differences and compatibility issues between environments. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively. To learn more refer to using IBM Db2 z/OS as a source for AWS DMS Schema Conversion and using IBM Db2 for z/OS as a source for AWS DMS. For AWS DMS Schema Conversion regional availability, please refer to the AWS Region Table.