AWS Graviton3-based R7g database instances as well as R7i database instances are now available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in Asia Pacific (Malaysia) Region. AWS Graviton3 instances provide up to 30% performance improvement and up to 20% price/performance improvement over Graviton2 instances for Amazon Aurora, depending on the database engine version and workload. R7i instances offer larger instance sizes, up to 48xlarge and features an 8:1 ratio of memory to vCPU, and the latest DDR5 memory.

You can spin up an R7g or R7i database instances in the Amazon RDS Management Console or using the AWS CLI. Upgrading a database instance to either option 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 Graviton3-based R7g database instances as well as R7i database instances are now available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in Asia Pacific (Malaysia) Region. AWS Graviton3 instances provide up to 30% performance improvement and up to 20% price/performance improvement over Graviton2 instances for Amazon Aurora, depending on the database engine version and workload. R7i instances offer larger instance sizes, up to 48xlarge and features an 8:1 ratio of memory to vCPU, and the latest DDR5 memory. You can spin up an R7g or R7i database instances in the Amazon RDS Management Console or using the AWS CLI. Upgrading a database instance to either option 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 (Amazon RDS) for Oracle now offers Oracle Database Standard Edition 2 (SE2) with the License-Included (LI) purchase option in additional AWS Regions for R6i instance class. RDS for Oracle R6i LI instances are now available in Asia Pacific (Malaysia) and Canada West (Calgary).

In the LI service model, you don’t need to separately purchase Oracle licenses. Amazon RDS for Oracle LI pricing includes the software license, the underlying hardware resources, and all database management capabilities. Simply launch an Oracle SE2 instance in the AWS Management Console or using the AWS CLI and specify the License-Included option. Configuration details for available instance types can be found on the Amazon RDS for Oracle Instance Types page.

Amazon RDS for Oracle allows you to set up, operate, and scale Oracle database deployments in the cloud. See Amazon RDS for Oracle Pricing for up-to-date pricing and regional availability.
 

 

​Amazon Relational Database Service (Amazon RDS) for Oracle now offers Oracle Database Standard Edition 2 (SE2) with the License-Included (LI) purchase option in additional AWS Regions for R6i instance class. RDS for Oracle R6i LI instances are now available in Asia Pacific (Malaysia) and Canada West (Calgary). In the LI service model, you don’t need to separately purchase Oracle licenses. Amazon RDS for Oracle LI pricing includes the software license, the underlying hardware resources, and all database management capabilities. Simply launch an Oracle SE2 instance in the AWS Management Console or using the AWS CLI and specify the License-Included option. Configuration details for available instance types can be found on the Amazon RDS for Oracle Instance Types page. Amazon RDS for Oracle allows you to set up, operate, and scale Oracle database deployments in the cloud. See Amazon RDS for Oracle Pricing for up-to-date pricing and regional availability.    

Today, we are announcing the support of the open-source GraphRAG Toolkit, a new capability that enhances Generative AI applications by providing more comprehensive, relevant and explainable responses using RAG techniques combined with graph data. The toolkit provides an open-source framework for automating the construction of a graph from unstructured data, and composing question-answering strategies that query this graph when answering user questions.

Previously, customers faced challenges in conducting exhaustive, multi-step searches across disparate content. By identifying key entities across documents, GraphRAG delivers insights that leverage relationships within the data, enabling improved responses to end users. For example, financial analysts can ask a financial analysis chatbot for the sales forecast of a manufacturing company. Developers building Generative AI applications can enable GraphRAG via this new open-source Python toolkit by specifying their data sources and choosing Amazon Neptune Database or Neptune Analytics as their graph store and Amazon OpenSearch serverless as the vector store. This will automatically generate and store vector embeddings in the selected vector store, along with a graph representation of entities and their relationships in the selected graph store.

The GraphRAG Toolkit is an open source project. Its code base is open for inspection, modification, and extension, and is therefore highly adaptable for specific or niche requirements. With its initial release, the toolkit provides graph store implementations for both Neptune Analytics and Neptune Database, and vector store implementations for Neptune Analytics and OpenSearch Serverless, and it uses FMs hosted in Amazon Bedrock. To learn more, visit the User Guide.
 

 

​Today, we are announcing the support of the open-source GraphRAG Toolkit, a new capability that enhances Generative AI applications by providing more comprehensive, relevant and explainable responses using RAG techniques combined with graph data. The toolkit provides an open-source framework for automating the construction of a graph from unstructured data, and composing question-answering strategies that query this graph when answering user questions. Previously, customers faced challenges in conducting exhaustive, multi-step searches across disparate content. By identifying key entities across documents, GraphRAG delivers insights that leverage relationships within the data, enabling improved responses to end users. For example, financial analysts can ask a financial analysis chatbot for the sales forecast of a manufacturing company. Developers building Generative AI applications can enable GraphRAG via this new open-source Python toolkit by specifying their data sources and choosing Amazon Neptune Database or Neptune Analytics as their graph store and Amazon OpenSearch serverless as the vector store. This will automatically generate and store vector embeddings in the selected vector store, along with a graph representation of entities and their relationships in the selected graph store. The GraphRAG Toolkit is an open source project. Its code base is open for inspection, modification, and extension, and is therefore highly adaptable for specific or niche requirements. With its initial release, the toolkit provides graph store implementations for both Neptune Analytics and Neptune Database, and vector store implementations for Neptune Analytics and OpenSearch Serverless, and it uses FMs hosted in Amazon Bedrock. To learn more, visit the User Guide.    

Amazon SNS now supports high-throughput mode for SNS FIFO topics, with default throughput matching SNS standard topics across all regions. When you enable high-throughput mode, SNS FIFO topics will maintain order within message group, while reducing the de-duplication scope to the message-group level. With this change, you can leverage up to 30K messages per second (MPS) per account by default in US East (N. Virginia) Region, and 9K MPS per account in US West (Oregon) Region and Europe (Ireland) Region, and request quota increases for additional throughput in any region.

Amazon SNS FIFO topics provides message ordering, message grouping, and de-duplication when delivering to Amazon SQS queues. By default, SNS FIFO topics provide 300 MPS per message group ID, and 3K MPS per topic, and topic level de-duplication. To get higher throughput, you can distribute your messages across message groups, and enable high-throughput mode by setting the FifoThroughputScope topic attribute to MessageGroup.

We now have increased default limits for SNS FIFO topics across all commercial and the AWS GovCloud (US) Regions.

To get started, see the following resources:

• High Throughput FIFO topics, in the Amazon SNS Developer Guide
• Service Quotas for Amazon SNS, in the AWS General Reference.

 

​Amazon SNS now supports high-throughput mode for SNS FIFO topics, with default throughput matching SNS standard topics across all regions. When you enable high-throughput mode, SNS FIFO topics will maintain order within message group, while reducing the de-duplication scope to the message-group level. With this change, you can leverage up to 30K messages per second (MPS) per account by default in US East (N. Virginia) Region, and 9K MPS per account in US West (Oregon) Region and Europe (Ireland) Region, and request quota increases for additional throughput in any region. Amazon SNS FIFO topics provides message ordering, message grouping, and de-duplication when delivering to Amazon SQS queues. By default, SNS FIFO topics provide 300 MPS per message group ID, and 3K MPS per topic, and topic level de-duplication. To get higher throughput, you can distribute your messages across message groups, and enable high-throughput mode by setting the FifoThroughputScope topic attribute to MessageGroup. We now have increased default limits for SNS FIFO topics across all commercial and the AWS GovCloud (US) Regions. To get started, see the following resources:

High Throughput FIFO topics, in the Amazon SNS Developer Guide
Service Quotas for Amazon SNS, in the AWS General Reference.  

You can now split your tests and run them across multiple, parallel-running compute environments. Based on your sharding strategy, CodeBuild will divide your tests and run them across the specified number of parallel environments. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages.

As the number of tests in a project grows, the total testing time also increases when using a single compute resource. Running tests in parallel across multiple compute resources reduces the overall testing duration in your CI/CD pipeline. This leads to faster feedback cycles and improved developer productivity.

The parallel testing feature is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page.

To learn more about CodeBuild’s test splitting, please visit our documentation. To learn more about how to get started with CodeBuild, visit the AWS CodeBuild product page.

 

​You can now split your tests and run them across multiple, parallel-running compute environments. Based on your sharding strategy, CodeBuild will divide your tests and run them across the specified number of parallel environments. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces ready-to-deploy software packages. As the number of tests in a project grows, the total testing time also increases when using a single compute resource. Running tests in parallel across multiple compute resources reduces the overall testing duration in your CI/CD pipeline. This leads to faster feedback cycles and improved developer productivity. The parallel testing feature is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page. To learn more about CodeBuild’s test splitting, please visit our documentation. To learn more about how to get started with CodeBuild, visit the AWS CodeBuild product page.  

Today, we are excited to announce that Amazon Connect screen recording is now available in AWS GovCloud (US-West), expanding its reach to government and public sector customers. This feature, which enables customers to record their agent screens during customer interactions for quality assurance purposes, was previously available in all commercial AWS regions where Amazon Connect is live. With this launch, we’re bringing the same powerful capabilities to our AWS GovCloud (US) customers.

Amazon Connect screen recording is a valuable tool for contact centers looking to enhance their quality assurance processes. By capturing agent desktop activities during customer interactions, supervisors and quality assurance teams can gain deeper insights into agent performance, adherence to procedures, and opportunities for improvement. This feature helps organizations maintain high standards of customer service, ensure compliance with regulations, and identify areas for agent training and development.

To get started with Amazon Connect screen recording in AWS GovCloud (US-West) or any supported region, visit the Amazon Connect console or refer to our documentation. For more information about Amazon Connect and its features, please visit the Amazon Connect product page.
 

 

​Today, we are excited to announce that Amazon Connect screen recording is now available in AWS GovCloud (US-West), expanding its reach to government and public sector customers. This feature, which enables customers to record their agent screens during customer interactions for quality assurance purposes, was previously available in all commercial AWS regions where Amazon Connect is live. With this launch, we’re bringing the same powerful capabilities to our AWS GovCloud (US) customers. Amazon Connect screen recording is a valuable tool for contact centers looking to enhance their quality assurance processes. By capturing agent desktop activities during customer interactions, supervisors and quality assurance teams can gain deeper insights into agent performance, adherence to procedures, and opportunities for improvement. This feature helps organizations maintain high standards of customer service, ensure compliance with regulations, and identify areas for agent training and development. To get started with Amazon Connect screen recording in AWS GovCloud (US-West) or any supported region, visit the Amazon Connect console or refer to our documentation. For more information about Amazon Connect and its features, please visit the Amazon Connect product page.    

Amazon S3 Tables are now available in five additional AWS Regions: Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and Asia Pacific (Tokyo).

S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables are specifically optimized for analytics workloads, resulting in up to 3x faster query performance through continual table optimization compared to unmanaged Iceberg tables, and up to 10x higher transactions per second compared to Iceberg tables stored in general purpose S3 buckets.

S3 Tables integration with AWS Glue Data Catalog is in preview, allowing you to stream, query, and visualize data using AWS Analytics services such as Amazon Data Firehose, Athena, Redshift, EMR, and QuickSight. Additionally, S3 Tables perform continual table maintenance to automatically expire old snapshots and related data files to reduce storage cost over time.

S3 Tables are now generally available in eight AWS Regions. For pricing details, visit the S3 pricing page. To learn more, visit the product page, documentation, and AWS News Blog.

 

​Amazon S3 Tables are now available in five additional AWS Regions: Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Stockholm), and Asia Pacific (Tokyo). S3 Tables deliver the first cloud object store with built-in Apache Iceberg support, and the easiest way to store tabular data at scale. S3 Tables are specifically optimized for analytics workloads, resulting in up to 3x faster query performance through continual table optimization compared to unmanaged Iceberg tables, and up to 10x higher transactions per second compared to Iceberg tables stored in general purpose S3 buckets. S3 Tables integration with AWS Glue Data Catalog is in preview, allowing you to stream, query, and visualize data using AWS Analytics services such as Amazon Data Firehose, Athena, Redshift, EMR, and QuickSight. Additionally, S3 Tables perform continual table maintenance to automatically expire old snapshots and related data files to reduce storage cost over time. S3 Tables are now generally available in eight AWS Regions. For pricing details, visit the S3 pricing page. To learn more, visit the product page, documentation, and AWS News Blog.  

Amazon Redshift extends support for Hexagonal Hierarchical Geospatial Indexing System, H3 for short, by adding two new H3 functions to Amazon Redshift’s previously announced H3 Indexing support in February 2024. H3 Indexing increases the performance of spatial queries at scale since the location information is pre-indexed. See this Amazon Big Data Blog on Amazon Redshift H3 Indexing for more information on the benefits and use-cases of H3 Indexing.

H3_Center returns the centroid of an H3 cell ID from an input index, which can be used to compute the geometric center of an arbitrary area that can be represented by H3 indexed cells, for example by finding the H3 cell with the smallest average distance to all other cells in the set representing the arbitrary area. While, H3_Boundary returns the boundary of an H3 cell ID from an input index.

With this announcement, Amazon Redshift now supports two new H3 Indexing related spatial functions: H3_Center, H3_Boundary, which are now available in all AWS regions where Amazon Redshift is available.

For more information or to get started with Amazon Redshift spatial analytics, see the documentation and the tutorial.

 

​Amazon Redshift extends support for Hexagonal Hierarchical Geospatial Indexing System, H3 for short, by adding two new H3 functions to Amazon Redshift’s previously announced H3 Indexing support in February 2024. H3 Indexing increases the performance of spatial queries at scale since the location information is pre-indexed. See this Amazon Big Data Blog on Amazon Redshift H3 Indexing for more information on the benefits and use-cases of H3 Indexing. H3_Center returns the centroid of an H3 cell ID from an input index, which can be used to compute the geometric center of an arbitrary area that can be represented by H3 indexed cells, for example by finding the H3 cell with the smallest average distance to all other cells in the set representing the arbitrary area. While, H3_Boundary returns the boundary of an H3 cell ID from an input index. With this announcement, Amazon Redshift now supports two new H3 Indexing related spatial functions: H3_Center, H3_Boundary, which are now available in all AWS regions where Amazon Redshift is available. For more information or to get started with Amazon Redshift spatial analytics, see the documentation and the tutorial.  

AWS User Notifications is now supported on AWS CloudFormation, allowing you to easily provision and manage User Notification resources as part of your application’s infrastructure-as-code (IaC). You can easily use User Notification to Configure Notifications to be sent via the Console Notifications Center, email, AWS Chatbot, or mobile push notifications to the AWS Console Mobile App to keep you informed about important events like Amazon CloudWatch Alarms.

With this new capability, you can define Notification Configurations as part of your IaC practices. Specify notification configurations for specific resource types within your CloudFormation templates. For example, set up notifications to trigger when an Auto Scaling group scales out, an ELB is provisioned, or an RDS database is modified. You have granular control over which events will trigger notifications and who should receive them.

This integration simplifies cloud operations by consolidating monitoring configuration right alongside your IaC templates. No more context switching between services – your notifications are defined declaratively within your infrastructure code.

Setting up stack notifications is easy – just add the Notifications or NotificationContacts resource type (e.g., AWS::Notifications::NotificationConfiguration) in your CloudFormation template to configure the desired events and channels. For more information, visit the Notifications and NotificationsContacts resource type reference. To learn more about User Notifications, visit the product page and documentation.
 

 

​AWS User Notifications is now supported on AWS CloudFormation, allowing you to easily provision and manage User Notification resources as part of your application’s infrastructure-as-code (IaC). You can easily use User Notification to Configure Notifications to be sent via the Console Notifications Center, email, AWS Chatbot, or mobile push notifications to the AWS Console Mobile App to keep you informed about important events like Amazon CloudWatch Alarms. With this new capability, you can define Notification Configurations as part of your IaC practices. Specify notification configurations for specific resource types within your CloudFormation templates. For example, set up notifications to trigger when an Auto Scaling group scales out, an ELB is provisioned, or an RDS database is modified. You have granular control over which events will trigger notifications and who should receive them. This integration simplifies cloud operations by consolidating monitoring configuration right alongside your IaC templates. No more context switching between services – your notifications are defined declaratively within your infrastructure code. Setting up stack notifications is easy – just add the Notifications or NotificationContacts resource type (e.g., AWS::Notifications::NotificationConfiguration) in your CloudFormation template to configure the desired events and channels. For more information, visit the Notifications and NotificationsContacts resource type reference. To learn more about User Notifications, visit the product page and documentation.    

Amazon Connect outbound campaigns can now be configured to connect a customer who has picked up a campaign call to an available agent in under two seconds while also supporting automatic call classification. This enhancement helps organizations support regulatory compliance with telemarketing laws such as the U.S. Telephone Consumer Protection Act (TCPA), while increasing customer engagement and agent productivity. Call classification automatically categorizes the outcome of a call using machine learning (ML), rather than requiring agent input, helping improve agent productivity and drive efficient customer engagement. To optimize outbound call performance, customers are encouraged to follow Amazon Connect’s best practices to reduce connection latency.

With Amazon Connect outbound campaigns, companies only pay-as-they-go for outbound campaigns processing and for associated channels usage. Amazon Connect outbound campaigns is available in US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt) and Europe (London) AWS regions. To learn more, visit our webpage.

 

​Amazon Connect outbound campaigns can now be configured to connect a customer who has picked up a campaign call to an available agent in under two seconds while also supporting automatic call classification. This enhancement helps organizations support regulatory compliance with telemarketing laws such as the U.S. Telephone Consumer Protection Act (TCPA), while increasing customer engagement and agent productivity. Call classification automatically categorizes the outcome of a call using machine learning (ML), rather than requiring agent input, helping improve agent productivity and drive efficient customer engagement. To optimize outbound call performance, customers are encouraged to follow Amazon Connect’s best practices to reduce connection latency. With Amazon Connect outbound campaigns, companies only pay-as-they-go for outbound campaigns processing and for associated channels usage. Amazon Connect outbound campaigns is available in US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Sydney), Canada (Central), Europe (Frankfurt) and Europe (London) AWS regions. To learn more, visit our webpage.