Amazon RDS for SQL Server now offers enhanced control over backup and restore operations with new custom parameters. This update allows database administrators to fine-tune their processes, potentially improving efficiency and reducing operation times. The new parameters are available for the rds_backup_database, rds_restore_database, and rds_restore_log stored procedures.

You can now specify the BLOCKSIZE, MAXTRANSFERSIZE, and BUFFERCOUNT parameters for backup and restore operations. These granular controls can help optimize performance based on your specific database characteristics and workload patterns. These customizable parameters are particularly useful when customer backups are incompatible with the default settings used by Amazon RDS for SQL Server. By allowing users to fine-tune these performance-related factors, the feature provides greater flexibility to accommodate unique database requirements and operating environments.

Customers can specify these new parameters in all AWS commercial Regions where Amazon RDS for SQL Server databases are available, including the AWS GovCloud (US) Regions.

Amazon RDS makes it simple to set up, operate, and scale SQL Server deployments in the cloud. See Amazon RDS for SQL Server Pricing for up-to-date pricing of instances, storage, data transfer and regional availability.
 

 

​Amazon RDS for SQL Server now offers enhanced control over backup and restore operations with new custom parameters. This update allows database administrators to fine-tune their processes, potentially improving efficiency and reducing operation times. The new parameters are available for the rds_backup_database, rds_restore_database, and rds_restore_log stored procedures. You can now specify the BLOCKSIZE, MAXTRANSFERSIZE, and BUFFERCOUNT parameters for backup and restore operations. These granular controls can help optimize performance based on your specific database characteristics and workload patterns. These customizable parameters are particularly useful when customer backups are incompatible with the default settings used by Amazon RDS for SQL Server. By allowing users to fine-tune these performance-related factors, the feature provides greater flexibility to accommodate unique database requirements and operating environments. Customers can specify these new parameters in all AWS commercial Regions where Amazon RDS for SQL Server databases are available, including the AWS GovCloud (US) Regions. Amazon RDS makes it simple to set up, operate, and scale SQL Server deployments in the cloud. See Amazon RDS for SQL Server Pricing for up-to-date pricing of instances, storage, data transfer and regional availability.    

Today, AWS Resource Groups is adding support for an additional 405 resource types for tag-based Resource Groups. Customers can now use Resource Groups to group and manage resources from services such as Bedrock, Chime, and Quicksight.

AWS Resource Groups enables you to model, manage and automate tasks on large numbers of AWS resources by using tags to logically group your resources. You can create logical collections of resources such as applications, projects, and cost centers, and manage them on dimensions such as cost, performance, and compliance in AWS services such as myApplications, AWS Systems Manager and Amazon CloudWatch.

Resource Groups expanded resource type coverage is available in all AWS Regions, including the AWS GovCloud (US) Regions. You can access AWS Resource Groups through the AWS Management Console, the AWS SDK APIs, and the AWS CLI.

For more information about grouping resources, see the AWS Resource Groups user guide and the list of supported resource types. To get started, visit AWS Resource Groups console.
 

 

​Today, AWS Resource Groups is adding support for an additional 405 resource types for tag-based Resource Groups. Customers can now use Resource Groups to group and manage resources from services such as Bedrock, Chime, and Quicksight. AWS Resource Groups enables you to model, manage and automate tasks on large numbers of AWS resources by using tags to logically group your resources. You can create logical collections of resources such as applications, projects, and cost centers, and manage them on dimensions such as cost, performance, and compliance in AWS services such as myApplications, AWS Systems Manager and Amazon CloudWatch. Resource Groups expanded resource type coverage is available in all AWS Regions, including the AWS GovCloud (US) Regions. You can access AWS Resource Groups through the AWS Management Console, the AWS SDK APIs, and the AWS CLI. For more information about grouping resources, see the AWS Resource Groups user guide and the list of supported resource types. To get started, visit AWS Resource Groups console.    

Amazon Bedrock Guardrails enable you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies. Starting today, we are excited to announce that Amazon Bedrock Guardrails are even more cost-effective with reduced pricing by up to 85%.

Amazon Bedrock Guardrails help you build safe, generative AI applications by filtering undesirable content, redacting personally identifiable information (PII), and enhancing content safety and privacy. You can configure policies for content filters, denied topics, word filters, PII redaction, contextual grounding checks, and Automated Reasoning checks (preview), to tailor safeguards to your specific use cases and responsible AI policies.

We are reducing the prices for content filters by 80% to $0.15 per 1,000 text units and for denied topics by 85% to $0.15 per 1,000 text units. With this price reduction, Bedrock Guardrails will help you accelerate the use of responsible AI across all your generative AI applications.

These pricing changes are already in effect starting December 1, 2024 in all AWS regions where Amazon Bedrock Guardrails is supported today. To learn more about Amazon Bedrock Guardrails, see the product page and the technical documentation.

 

​Amazon Bedrock Guardrails enable you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies. Starting today, we are excited to announce that Amazon Bedrock Guardrails are even more cost-effective with reduced pricing by up to 85%. Amazon Bedrock Guardrails help you build safe, generative AI applications by filtering undesirable content, redacting personally identifiable information (PII), and enhancing content safety and privacy. You can configure policies for content filters, denied topics, word filters, PII redaction, contextual grounding checks, and Automated Reasoning checks (preview), to tailor safeguards to your specific use cases and responsible AI policies. We are reducing the prices for content filters by 80% to $0.15 per 1,000 text units and for denied topics by 85% to $0.15 per 1,000 text units. With this price reduction, Bedrock Guardrails will help you accelerate the use of responsible AI across all your generative AI applications. These pricing changes are already in effect starting December 1, 2024 in all AWS regions where Amazon Bedrock Guardrails is supported today. To learn more about Amazon Bedrock Guardrails, see the product page and the technical documentation.  

Today, AWS announces three new updates to AWS IoT Core for LoRaWAN: IPv6 support, enhanced Firmware Update Over-The-Air (FUOTA) with advanced logging capabilities, and console-based gateway firmware updates, improving fleet management, scalability, reliability, and user experience for Internet of Things (IoT) applications.

AWS IoT Core for LoRaWAN is a fully-managed cloud service that makes it easy to connect, manage, and monitor wireless devices that use low-power, long-range wide area network (LoRaWAN) technology. With the new feature updates, developers can now assign IPv6 address to their LoRaWAN-based devices and gateways and coexist with other IPv4 devices in the same network, simplifying network configurations and management, while improving the security posture of their solutions. The FUOTA enhancements enable selective multicast transmission to specific gateways, reducing airtime competition and file corruption risks, while advanced logging capabilities monitor FUOTA progress, file retrieval, transition status, and errors. The AWS IoT Core console now provides a streamlined interface for managing firmware updates for LoRaWAN gateways using Configuration and Update Server (CUPS protocol), simplifying firmware uploads, update scheduling, and progress tracking.

These updates are available in all AWS IoT Core for LoRaWAN-supported regions. For detailed guidance and implementation instructions, visit the AWS IoT Core for LoRaWAN Developer Guide.

 

​Today, AWS announces three new updates to AWS IoT Core for LoRaWAN: IPv6 support, enhanced Firmware Update Over-The-Air (FUOTA) with advanced logging capabilities, and console-based gateway firmware updates, improving fleet management, scalability, reliability, and user experience for Internet of Things (IoT) applications. AWS IoT Core for LoRaWAN is a fully-managed cloud service that makes it easy to connect, manage, and monitor wireless devices that use low-power, long-range wide area network (LoRaWAN) technology. With the new feature updates, developers can now assign IPv6 address to their LoRaWAN-based devices and gateways and coexist with other IPv4 devices in the same network, simplifying network configurations and management, while improving the security posture of their solutions. The FUOTA enhancements enable selective multicast transmission to specific gateways, reducing airtime competition and file corruption risks, while advanced logging capabilities monitor FUOTA progress, file retrieval, transition status, and errors. The AWS IoT Core console now provides a streamlined interface for managing firmware updates for LoRaWAN gateways using Configuration and Update Server (CUPS protocol), simplifying firmware uploads, update scheduling, and progress tracking. These updates are available in all AWS IoT Core for LoRaWAN-supported regions. For detailed guidance and implementation instructions, visit the AWS IoT Core for LoRaWAN Developer Guide.  

AWS is announcing the general availability of two new Amazon EC2 High Memory U7i instances with 6TiB and 8TiB of memory. U7i-6tb and U7i-8tb are powered by 4th Generation Intel Xeon Scalable processors and offer 448 vCPUs, delivering up to 35% better performance and up to 15% better price performance versus comparable AWS EC2 High Memory U-1 instances. These instances extend the U7i instance family, providing customers greater flexibility to select the right instance for the right workload. U7i instances are ideal to run large in-memory databases such as SAP HANA, Oracle, and SQL Server.

U7i instances are built on the AWS Nitro system, a collection of AWS designed hardware and lightweight Nitro hypervisor which delivers practically all of the compute and memory resources of the host hardware to your instances. This frees up additional memory for your workloads which boosts performance and lowers the $/GiB memory costs.

These instances are certified by SAP for running SAP S/4HANA, SAP BW/4HANA, Business Suite on HANA, Data Mart Solutions on HANA, and Business Warehouse on HANA in production environments. For details, see the SAP HANA Hardware Directory.

These new instances are available in the following AWS Regions: US East (N. Virginia), US West (Oregon), and Asia Pacific (Seoul). Customers can use these instances with On Demand and Savings Plan purchase options. To learn more, visit the U7i instances page.
 

 

​AWS is announcing the general availability of two new Amazon EC2 High Memory U7i instances with 6TiB and 8TiB of memory. U7i-6tb and U7i-8tb are powered by 4th Generation Intel Xeon Scalable processors and offer 448 vCPUs, delivering up to 35% better performance and up to 15% better price performance versus comparable AWS EC2 High Memory U-1 instances. These instances extend the U7i instance family, providing customers greater flexibility to select the right instance for the right workload. U7i instances are ideal to run large in-memory databases such as SAP HANA, Oracle, and SQL Server. U7i instances are built on the AWS Nitro system, a collection of AWS designed hardware and lightweight Nitro hypervisor which delivers practically all of the compute and memory resources of the host hardware to your instances. This frees up additional memory for your workloads which boosts performance and lowers the $/GiB memory costs. These instances are certified by SAP for running SAP S/4HANA, SAP BW/4HANA, Business Suite on HANA, Data Mart Solutions on HANA, and Business Warehouse on HANA in production environments. For details, see the SAP HANA Hardware Directory. These new instances are available in the following AWS Regions: US East (N. Virginia), US West (Oregon), and Asia Pacific (Seoul). Customers can use these instances with On Demand and Savings Plan purchase options. To learn more, visit the U7i instances page.    

Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best foundation models for your use case. Amazon Bedrock offers a choice of using an LLM-as-a-judge, programmatic evaluation, and human evaluation. You can use an LLM-as-a-judge for metrics such as correctness, completeness, and coherence, as well as responsible AI metrics such as answer refusal and harmfulness. Programmatic evaluation offers algorithms for metrics such as accuracy, robustness, and toxicity. Additionally, for those metrics or subjective and custom metrics, such as friendliness or style, you can set up a human evaluation workflow with a few clicks. Human evaluation leverages your own employees or an AWS-managed team as reviewers. Model evaluation provides built-in curated datasets or you can bring your own datasets. Now, customers can evaluate models in the Europe (Zurich).

Model Evaluation on Amazon Bedrock is now available in these regions, and evaluation type availability varies by region.

To learn more about Model Evaluation on Amazon Bedrock, see the Amazon Bedrock Evaluations page. To get started, sign in to Amazon Bedrock on the AWS Management Console or use the Amazon Bedrock APIs.

 

​Model Evaluation on Amazon Bedrock allows you to evaluate, compare, and select the best foundation models for your use case. Amazon Bedrock offers a choice of using an LLM-as-a-judge, programmatic evaluation, and human evaluation. You can use an LLM-as-a-judge for metrics such as correctness, completeness, and coherence, as well as responsible AI metrics such as answer refusal and harmfulness. Programmatic evaluation offers algorithms for metrics such as accuracy, robustness, and toxicity. Additionally, for those metrics or subjective and custom metrics, such as friendliness or style, you can set up a human evaluation workflow with a few clicks. Human evaluation leverages your own employees or an AWS-managed team as reviewers. Model evaluation provides built-in curated datasets or you can bring your own datasets. Now, customers can evaluate models in the Europe (Zurich). Model Evaluation on Amazon Bedrock is now available in these regions, and evaluation type availability varies by region. To learn more about Model Evaluation on Amazon Bedrock, see the Amazon Bedrock Evaluations page. To get started, sign in to Amazon Bedrock on the AWS Management Console or use the Amazon Bedrock APIs.  

Amazon EC2 U7in-24tb instances are now available in AWS GovCloud (US-West) Region. U7in-24tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids) delivering up to 135% more compute performance over existing U-1 instances. U7in-24tb instances offer 24TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment.

U7in-24tb instance supports 896 vCPUs, the most vCPUs in the AWS cloud and support up to 100Gbps Elastic Block Storage (EBS), enabling customers to load data faster into memory and improve their backup speed. U7in-24tb instances instances deliver up to 200Gbps of network bandwidth and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases such as SAP HANA, Oracle, or SQL Server.

To learn more about U7i instances, visit the High Memory instances page.
 

 

​Amazon EC2 U7in-24tb instances are now available in AWS GovCloud (US-West) Region. U7in-24tb instances are part of AWS 7th generation and are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids) delivering up to 135% more compute performance over existing U-1 instances. U7in-24tb instances offer 24TiB of DDR5 memory enabling customers to scale transaction processing throughput in a fast-growing data environment. U7in-24tb instance supports 896 vCPUs, the most vCPUs in the AWS cloud and support up to 100Gbps Elastic Block Storage (EBS), enabling customers to load data faster into memory and improve their backup speed. U7in-24tb instances instances deliver up to 200Gbps of network bandwidth and support ENA Express. U7i instances are ideal for customers using mission-critical in-memory databases such as SAP HANA, Oracle, or SQL Server. To learn more about U7i instances, visit the High Memory instances page.    

Starting today, Amazon EC2 Hpc6id instances are available in additional AWS Region Europe (Paris). These instances are optimized to efficiently run memory bandwidth-bound, data-intensive high performance computing (HPC) workloads, such as finite element analysis and seismic reservoir simulations. With EC2 Hpc6id instances, you can lower the cost of your HPC workloads while taking advantage of the elasticity and scalability of AWS.

EC2 Hpc6id instances are powered by 64 cores of 3rd Generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.5 GHz, 1,024 GB of memory, and up to 15.2 TB of local NVMe solid state drive (SSD) storage. EC2 Hpc6id instances, built on the AWS Nitro System, offer 200 Gbps Elastic Fabric Adapter (EFA) networking for high-throughput inter-node communications that enable your HPC workloads to run at scale. The AWS Nitro System is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software. It delivers high performance, high availability, and high security while reducing virtualization overhead.

To learn more about EC2 Hpc6id instances, see the product detail page.

 

​Starting today, Amazon EC2 Hpc6id instances are available in additional AWS Region Europe (Paris). These instances are optimized to efficiently run memory bandwidth-bound, data-intensive high performance computing (HPC) workloads, such as finite element analysis and seismic reservoir simulations. With EC2 Hpc6id instances, you can lower the cost of your HPC workloads while taking advantage of the elasticity and scalability of AWS. EC2 Hpc6id instances are powered by 64 cores of 3rd Generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.5 GHz, 1,024 GB of memory, and up to 15.2 TB of local NVMe solid state drive (SSD) storage. EC2 Hpc6id instances, built on the AWS Nitro System, offer 200 Gbps Elastic Fabric Adapter (EFA) networking for high-throughput inter-node communications that enable your HPC workloads to run at scale. The AWS Nitro System is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software. It delivers high performance, high availability, and high security while reducing virtualization overhead. To learn more about EC2 Hpc6id instances, see the product detail page.  

Starting today, Amazon EC2 Hpc7a instances are available in additional AWS Region Europe (Paris). EC2 Hpc7a instances are powered by 4th generation AMD EPYC processors with up to 192 cores, and 300 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for fast and low-latency internode communications. Hpc7a instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in memory.

Hpc7a instances are ideal for compute-intensive, tightly coupled, latency-sensitive high performance computing (HPC) workloads, such as computational fluid dynamics (CFD), weather forecasting, and multiphysics simulations, helping you scale more efficiently on fewer nodes. To optimize HPC instances networking for tightly coupled workloads, you can access these instances in a single Availability Zone within a Region.

To learn more, see Amazon Hpc7a instances.

 

​Starting today, Amazon EC2 Hpc7a instances are available in additional AWS Region Europe (Paris). EC2 Hpc7a instances are powered by 4th generation AMD EPYC processors with up to 192 cores, and 300 Gbps of Elastic Fabric Adapter (EFA) network bandwidth for fast and low-latency internode communications. Hpc7a instances feature Double Data Rate 5 (DDR5) memory, which enables high-speed access to data in memory. Hpc7a instances are ideal for compute-intensive, tightly coupled, latency-sensitive high performance computing (HPC) workloads, such as computational fluid dynamics (CFD), weather forecasting, and multiphysics simulations, helping you scale more efficiently on fewer nodes. To optimize HPC instances networking for tightly coupled workloads, you can access these instances in a single Availability Zone within a Region. To learn more, see Amazon Hpc7a instances.  

Amazon Aurora PostgreSQL is now available as a quick create vector store in Amazon Bedrock Knowledge Bases. With the new Aurora quick create option, developers and data scientists building generative AI applications can select Aurora PostgreSQL as their vector store with one click to deploy an Aurora Serverless cluster preconfigured with pgvector in minutes. Aurora Serverless is an on-demand, autoscaling configuration where capacity is adjusted automatically based on application demand, making it ideal as a developer vector store.

Knowledge Bases securely connects foundation models (FMs) running in Bedrock to your company data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, context-specific, and accurate responses that make your FM more knowledgeable about your business. To implement RAG, organizations must convert data into embeddings (vectors) and store these embeddings in a vector store for similarity search in generative artificial intelligence (AI) applications. Aurora PostgreSQL, with the pgvector extension, has been supported as a vector store in Knowledge Bases for existing Aurora databases. With the new quick create integration with Knowledge Bases, Aurora is now easier to set up as a vector store for use with Bedrock.

The quick create option in Bedrock Knowledge Bases is available in these regions with the exception of AWS GovCloud (US-West) which is planned for Q4 2024. To learn more about RAG with Amazon Bedrock and Aurora, see Amazon Bedrock Knowledge Bases.

Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started using Amazon Aurora PostgreSQL as a vector store for Amazon Bedrock Knowledge Bases, take a look at our documentation.

 

​Amazon Aurora PostgreSQL is now available as a quick create vector store in Amazon Bedrock Knowledge Bases. With the new Aurora quick create option, developers and data scientists building generative AI applications can select Aurora PostgreSQL as their vector store with one click to deploy an Aurora Serverless cluster preconfigured with pgvector in minutes. Aurora Serverless is an on-demand, autoscaling configuration where capacity is adjusted automatically based on application demand, making it ideal as a developer vector store. Knowledge Bases securely connects foundation models (FMs) running in Bedrock to your company data sources for Retrieval Augmented Generation (RAG) to deliver more relevant, context-specific, and accurate responses that make your FM more knowledgeable about your business. To implement RAG, organizations must convert data into embeddings (vectors) and store these embeddings in a vector store for similarity search in generative artificial intelligence (AI) applications. Aurora PostgreSQL, with the pgvector extension, has been supported as a vector store in Knowledge Bases for existing Aurora databases. With the new quick create integration with Knowledge Bases, Aurora is now easier to set up as a vector store for use with Bedrock. The quick create option in Bedrock Knowledge Bases is available in these regions with the exception of AWS GovCloud (US-West) which is planned for Q4 2024. To learn more about RAG with Amazon Bedrock and Aurora, see Amazon Bedrock Knowledge Bases. Amazon Aurora combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. To get started using Amazon Aurora PostgreSQL as a vector store for Amazon Bedrock Knowledge Bases, take a look at our documentation.