Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift is now generally available, enabling near real-time analytics and machine learning (ML) on petabytes of transactional data. With this launch, you can create multiple zero-ETL integrations from a single Amazon RDS database, and you can apply data filtering for each integration to include or exclude specific databases and tables, tailoring replication to your needs. You can also use AWS CloudFormation to automate the configuration and deployment of resources needed for zero-ETL integration. Zero-ETL integrations make it simpler to analyze data from Amazon RDS to Amazon Redshift by removing the need for you to build and manage complex data pipelines and helping you derive holistic insights across many applications. Within seconds of data being written to Amazon RDS for MySQL, the data is replicated to Amazon Redshift. Using zero-ETL, you can enhance data analysis on near real-time data with the rich analytics capabilities of Amazon Redshift, including integrated ML, Spark support, and materialized views. This zero-ETL integration is available for RDS for MySQL versions 8.0.32 and later, Amazon Redshift Serverless, and Amazon Redshift RA3 instance types in supported AWS Regions.  To learn more about this zero-ETL integration, visit the documentation for Amazon RDS and Amazon Redshift. To get started with the latest enhancements in minutes, check out the AWS News Blog.

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Hong Kong) and Israel (Tel Aviv) regions. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.  

Today, we are announcing the availability of sticky session routing on Amazon SageMaker Inference which helps customers improve the performance and user experience of their generative AI applications by leveraging their previously processed information. Amazon SageMaker makes it easier to deploy ML models including foundation models (FMs) to make inference requests at the best price performance for any use case. By enabling sticky sessions, all requests for the same session will be routed to the same instance, allowing your ML application to reuse previously processed information to reduce latency and improve user experience. This is particularly valuable when customers want to use large data payloads or have the need for seamless interactive experiences. By leveraging their previous inference requests, customers can now take advantage of this feature to build innovative state-aware AI applications on SageMaker. To do this customers will have to create a session id with their first request and then use that session id to indicate that SageMaker should route all the subsequent requests to the same instance. Sessions can also be deleted when done to free up resources for new sessions. This feature is available in all regions where SageMaker is available. You can learn more about deploying models on SageMaker here and more about this feature in our documentation.

AWS Network Firewall now supports AWS PrivateLink. Customers can now access and manage their Network Firewalls privately, without going through the public internet. AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises applications, securely over the Amazon network. When AWS PrivateLink is used with AWS Network Firewall, all management and control traffic between clients and Network Firewall flows over a private network. AWS Network Firewall is a managed firewall service that makes it easy to deploy essential network protections for all your Amazon VPCs. Customers can use AWS PrivateLink with Network Firewall in regions where AWS Network Firewall is available today, including the AWS GovCloud (US) Regions. For more information about the AWS Regions where AWS Network Firewall is available, see the AWS Region table. To learn more about configuring AWS Network Firewall, please refer to the service documentation.

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R6in and R6idn instances are available in AWS Region Asia Pacific (Sydney). These sixth-generation network optimized instances, powered by 3rd Generation Intel Xeon Scalable processors and built on the AWS Nitro System, deliver up to 200Gbps network bandwidth, 2x more network bandwidth, and up to 2x higher packet-processing performance over comparable fifth-generation instances. Customers can use R6in and R6idn instances to scale the performance and throughput of network-intensive workloads such as memory-intensive SQL and NoSQL databases, distributed web scale in-memory caches (Memcached, Redis), in-memory databases (SAP HANA), and real-time big data analytics (Apache Hadoop, Apache Spark). R6in and R6idn instances are available in 10 different instance sizes including metal, with up to 128 vCPUs and 1024 GiB of memory. They deliver up to 100 Gbps of Amazon Elastic Block Store (EBS) bandwidth, and up to 400K IOPS. R6in and R6idn instances offer Elastic Fabric Adapter (EFA) networking support on 32xlarge and metal sizes. R6idn instances offer up to 7.6 TB of high-speed, low-latency instance storage. With this regional expansion, R6in and R6idn instances are available in the following AWS Regions: US East (Ohio), US East (N. Virginia, Oregon), Europe (Ireland, Frankfurt, Stockholm), Asia Pacific (Singapore, Sydney, Tokyo), and AWS GovCloud (US-West). Customers can purchase the new instances through Savings Plans, Reserved, On-Demand, and Spot instances. To learn more, see R6in and R6idn instances page. To get started, see AWS Management Console, AWS Command Line Interface (AWS CLI), and AWS SDKs.  

The AV1 video codec is now supported in the AWS Elemental Media Services. You can use AV1 in AWS Elemental MediaLive, MediaPackage, MediaTailor, and MediaConvert to produce both live and on-demand streams with ad insertion. This launch enables live streaming and packaging of AV1 encoded content, preparation of AV1 VOD assets and ads, and ad insertion into AV1 encoded videos. AV1 provides a lower bitrate with a similar viewing experience when compared to HEVC and AVC, reducing both the bandwidth required to deliver live events and CDN costs. AV1 can also deliver better video quality for viewing on mobile devices and in network constrained environments. AWS Media Services enable you to transport, prepare, process, and deliver live and on-demand content in the cloud. These managed services let you build and adapt video workflows quickly, eliminate capacity planning, easily scale with growth, and benefit from pay-as-you-go pricing. Connect with other AWS services and third-party applications for live and on demand video streaming, media storage, machine learning, content protection, advertising and monetization, and more. To learn how the AV1 codec can reduce bandwidth and improve the viewing experience, read the blog post. For more information about live AV1 pricing, please review the AWS Elemental MediaLive pricing page. To learn more about the services, please visit the AWS Media Services page.

Today, AWS announces the general availability of AWS Elemental MediaLive Anywhere, which allows you to run live video transcoding on your on-premises hardware. MediaLive Anywhere brings the cloud control and pay-as-you-go pricing of AWS Elemental MediaLive to compute resources you manage. With MediaLive Anywhere, you can take advantage of MediaLive's centralized configuration, control, and monitoring capabilities while processing live video on premises close to video sources and outputs. With MediaLive Anywhere, you deploy the same broadcast-grade video encoding engine used in AWS Elemental MediaLive on your hardware. MediaLive Anywhere supports a wide range of hardware configurations and can ingest video from multicast, SDI, and standard internet-based transport protocols. You get a consistent set of APIs, channel profiles, logs, and monitoring metrics across your cloud and on-premises live video workflows. AWS Elemental MediaLive Anywhere is available in all AWS Regions where AWS Elemental MediaLive is available. To learn more, visit the AWS Elemental MediaLive Anywhere page.

Starting today, AWS Application Migration Service (AWS MGN) provides an action for installing the Trend Micro Vision One Server & Workload Protection Agent on your migrated instances. For each migrated server, you can choose to automatically install the agent to support your security needs. Application Migration Service minimizes time-intensive, error-prone manual processes by automating the conversion of your source servers to run natively on AWS. It also helps simplify modernization of your migrated applications by allowing you to select preconfigured and custom optimization options during migration. This feature is now available in all of the Commercial regions where Application Migration Service is available. Access the AWS Regional Services List for the most up-to-date availability information. To start using Application Migration Service for free, sign in through the AWS Management Console. For more information, visit the Application Migration Service product page. For more information on Trend Micro and to create a trial account, visit the Trend Micro sign-up page.  

Today, AWS Backup announces a new AWS Backup Audit Manager control, allowing you to audit and check if a resource's backup data is in a logically air-gapped vault. With this launch, you can assess if a backup was copied inside a logically air-gapped within a specified time frame, and evaluate whether it meets your business or compliance requirements. To use the new AWS Backup Audit Manager control, resources are inside a logically air-gapped vault, simply enable it in your existing AWS Backup Audit Manager frameworks or create a new framework. Once you enable this control, AWS Backup Audit Manager automatically runs it on a daily basis, monitoring if a backup was successfully copied inside your logically air-gapped vault based on the specified time frame for your selected resources. You can also generate auditor-ready reports to help prove compliance of your data protection policies to organizational or industry-specific regulatory requirements. Get started with AWS Backup Audit Manager using the AWS Management console, SDKs, or CLI. AWS Backup Audit Manager is available in US East (Ohio, N. Virginia), US West (N. California, Oregon), Canada (Central), Europe (Frankfurt, Ireland, London, Milan, Paris, Stockholm), South America (Sao Paulo), Middle East (Bahrain), Africa (Cape Town), and Asia Pacific (Hong Kong, Mumbai, Seoul, Singapore, Sydney, Tokyo). To learn more about AWS Backup Audit Manager, visit the AWS Backup product page and documentation. For more information on AWS Backup availability and pricing, see the AWS Regional Services List and pricing page.  

Amazon Elastic Container Registry (ECR) now supports dual-layer server-side encryption in the AWS GovCloud (US) Regions. This capability allows you to apply two independent layers of server-side encryption to images stored in Amazon ECR. Dual-layer server-side encryption with keys stored in AWS Key Management Service (DSSE-KMS) enables you to meet stronger compliance and regulatory requirements of applying multiple layers of encryption to your container images. ECR supports server-side encryption of ECR images using either Amazon S3-managed encryption keys or keys stored in Amazon Key Management Service (KMS). This often meets your security requirements as it protects data at rest, however, if you operate in highly regulated environments that require rigorous security standards, you may require a second layer of encryption for your images. Now with DSSE-KMS, you can easily apply two layers of encryption and control the keys used for both layers. Once this feature is enabled, ECR automatically encrypts your images twice when pushed and decrypts twice when pulled using your encryption keys managed by Amazon Key Management Service (KMS). AWS KMS is a simple to use key management service that makes it easy for you to create, manage, and control keys by setting permissions per key and specifying key rotation schedules. DSSE-KMS with ECR is available for use in the AWS GovCloud (US) Regions at an additional cost. For pricing information, visit the Amazon ECR pricing page. To learn more about all available encryption options on Amazon ECR and get started with this feature, visit our user guide.

AWS Glue Data Catalog now supports storage optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Each write to an Iceberg table creates a new snapshot, or version, of a table. In addition, any failures during writing to Iceberg tables will create data files that aren't referenced in snapshots known as “orphan” files that further increases storage costs. AWS Glue catalog’s new storage optimizations along with automated compaction will help you reduce metadata overhead, control storage costs and improve query performance. With this launch, you can enable AWS Glue catalog table optimization to include snapshot and orphan data management. You can optimize Amazon S3 layout by providing configuration such as default retention period and days to keep orphan files. Once enabled, AWS Glue catalog periodically monitors tables, removes snapshots from table metadata, removes the Amazon S3 data files, and orphan files that are no longer needed. You can view history of number of data, manifest, manifest lists and orphan files deleted from the table optimization tab in the Glue catalog console. In addition to the AWS console, customers can also use AWS CLI or AWS SDKs to enable table optimization of Apache Iceberg tables. Automatic optimization for Iceberg tables is available in 13 AWS regions US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland, London, Frankfurt, Stockholm), Asia Pacific (Tokyo, Seoul, Mumbai, Singapore, Sydney), South America (São Paulo). To learn more, read the blog, and visit the AWS Glue Data Catalog documentation.  

The AWS access portal provides AWS IAM Identity Center users with single sign-on access to all their assigned AWS applications and AWS accounts. Today, AWS IAM Identity Center added support for user preferences on language and visual mode in the AWS access portal. When customers need to access AWS applications and resources through the AWS access portal, having the ability to work with their preferred language and visual mode improves their efficiency and comfort. AWS access portal now supports 12 different language options, allowing users to work in their most natural and comfortable language. Users can now switch the visual mode of their AWS access portal to dark mode, helping reduce eye strain and improving readability in bright environments. AWS access portal inherits language and visual mode preferences from browser settings by default and allow users to customize further if needed. The language and visual mode preferences for AWS access portal are available in all AWS regions where AWS IAM Identity Center is available. IAM Identity Center helps customers connect or create their workforce identities, and manage their access to multiple AWS applications and AWS accounts. IAM Identity Center is available to customers at no additional cost. To learn more about IAM Identity Center, visit the product detail page. To get started with using the AWS access portal, please refer to the user guide.  

Amazon EventBridge Pipes now supports AWS Key Management Service (KMS) customer managed keys, allowing you to encrypt Pipes filter patterns, enrichment parameters, and target parameters with your own keys instead of default AWS owned keys. Using keys that you create, own, and manage can satisfy your organization’s security and governance requirements. Amazon EventBridge lets you use events to connect application components, making it easier to build scalable event-driven applications. EventBridge Pipes provides a simple, consistent, and cost-effective way to create point-to-point integrations between event producers and consumers. Pipes enables you to send data from one of 6 different event sources to any of the 20+ targets supported by the EventBridge Event Bus, including HTTPS endpoints through EventBridge API Destinations and event buses themselves. With support for Customer Managed Keys, you have more fine-grained security control over your Pipe’s configuration data to more easily meet your organization's regulatory and compliance requirements. You can also audit and track usage of your encryption keys with AWS CloudTrail. Customer managed key support for EventBridge Pipes is available in all AWS Regions where EventBridge Pipes is available. To get started, follow the directions provided in the EventBridge Pipes documentation. To learn more about customer managed keys, visit the AWS Key Management Service documentation.  

Amazon Redshift now lets you alter sort keys of tables replicated through zero-ETL integration. Sort keys play a crucial role in determining the physical sorting order of rows within a table, and optimizing them can significantly enhance query performance, especially for queries using range-bound filters on sort key columns. Amazon Redshift's zero-ETL integration helps you derive holistic insights across many applications and break down data silos in your organization, making it simpler to analyze data from different operational databases. You can now modify the sort keys of your tables replicated through the zero-ETL integration, and achieve faster and more efficient querying of your replicated data in Amazon Redshift. Furthermore, you can even set the sort key of zero-ETL tables to AUTO and allow Amazon Redshift to observe your workload and automatically set a sort key based on your evolving workload and data patterns. To learn more and get started with zero-ETL integration, visit the getting started guides for Amazon Redshift. To learn more about how Amazon Redshift sort’s data, see documentation.

We are excited to announce the general availability of Amazon EKS support in SageMaker HyperPod which enables customers to run and manage their Kubernetes workloads on SageMaker HyperPod, a purpose-built infrastructure for foundation model (FM) development which reduces time to train models by up to 40%. Many customers use Kubernetes to orchestrate their ML workflows due to its portability, scalability, and rich ecosystem of tools. These customers want to continue using Kubernetes’ familiar interface, but still want an automated way to manage hardware failures. EKS support in HyperPod combines the benefits of SageMaker HyperPod offering self-healing performant clusters with the containerization capabilities of Amazon EKS, a managed Kubernetes service. With this launch, customers can run deep health checks during cluster creation to reduce failures during training. Further, HyperPod automatically replaces faulty nodes and resumes training from your last checkpoint on both AWS Trainium and Nvidia GPU at a scale of more than a thousand accelerators. Customers have the flexibility to use either the new HyperPod CLI, or their preferred tools, to submit, manage, and monitor workloads. The persistent cluster environment offers ssm access and the ability to customize the cluster. EKS orchestrated HyperPod clusters also integrate with CloudWatch Container Insights to provide out-of-the-box observability, by auto-discovering HyperPod node health status and visualizing them in curated dashboards. This release is generally available in the AWS Regions where SageMaker HyperPod is available except Europe (London). To learn more, see the following list of resources: Webpage, AWS News Blog, Documentation, Github repository.

Starting today, Amazon EMR on EC2 offers improved reliability and cluster launch experience for instance fleet clusters through enhanced subnet selection. With this feature, EMR on EC2 reduces cluster launch failures caused due to IP address shortages. Amazon EMR is a cloud big data platform for data processing, interactive analysis, and machine learning using open-source frameworks such as Apache Spark, Apache Hive, and Presto. Previously, the subnet selection for EMR clusters only considered the available IP addresses for the core instance fleet. Amazon EMR now employs subnet filtering at cluster launch and selects one of the subnets that have adequate available IP addresses to successfully launch all instance fleets. If EMR cannot find a subnet with sufficient IP addresses to launch the whole cluster, it will prioritize the subnet that can at least launch the core and primary instance fleets. In this scenario, EMR will also publish a CloudWatch warning event to notify the user. If none of the configured subnets can be used to provision core and primary fleet, EMR will fail the cluster launch and provide a critical error event. These CloudWatch events enables you to monitor your clusters and take remedial actions as necessary. Customers will benefit from this feature on all EMR 5.12.1 and later releases when launching EMR instance fleet clusters using allocation strategies. No further action is needed from your end. This capability is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon EMR on EC2 is available. To learn more, please refer to the documentation here.

Amazon CloudWatch Container Insights now auto-discovers the health status of your SageMaker HyperPod nodes running on EKS and visualizes them in curated dashboards to help you monitor your node availability for operational excellence. Using out-of-the-box dashboards, you can identify unhealthy nodes easily and mitigate quickly to achieve efficient training durations. Container Insights works with SageMaker to collect deep health check test results for HyperPod nodes and displays them in preset dashboards to help you understand the health and performance of your nodes, and identify if they are ready for scheduling. Container Insights assists you in optimizing training durations by classifying failing nodes as “pending reboot” and “pending replacement,” and guiding you on maintaining node health in case automatic node replacement is disabled. If auto-recovery is enabled, you can gain visibility into your node mutations, delays in your training jobs, and understand how your tasks resume from the last check-point. Getting started with Container Insights is easy. You can onboard either by installing CloudWatch Observability EKS Add-on or the latest CloudWatch agent into your clusters, or upgrading your Helm charts with the latest CloudWatch Agent version. Once configured you can navigate to Container Insights console and view your SageMaker Hyperpod node health status out-of-the-box. SageMaker HyperPod node health observability is now available in Container Insights for EKS in all commercial regions where SageMaker HyperPod is present. HyperPod node health metrics follow observation based pricing – see Container Insights pricing page for details. For further information, see the Container Insights user guide.

Amazon MSK Replicator now supports a new configuration that enables you to preserve original Kafka topic names while replicating streaming data across Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters. Amazon MSK Replicator is a feature of Amazon MSK that lets you reliably replicate data across MSK clusters in the same or different AWS region(s) with just a few clicks. The new configuration reduces the need for you to reconfigure client applications during setup and makes it even more simple to operate multi-cluster streaming architectures, while continuing to benefit from MSK Replicator’s reliability. With Amazon MSK Replicator, you can easily build regionally resilient streaming applications for business continuity, share data with partners, aggregate data from multiple clusters for analytics, and serve clients globally with lower latency. With the new configuration, you can retain topic names during replication while automatically avoiding the risk of infinite replication loops that comes with using third-party or open-source tools for replication. If you setup active-passive cluster architecture to build regionally resilient streaming applications, where one cluster handles live traffic while another acts as a standby, the new configuration also streamlines the failover process. Applications can seamlessly failover to the standby cluster without requiring reconfiguration, as topic names remain intact. Support for the new configuration is available in all regions where Amazon MSK Replicator is available. To see all the regions where Amazon MSK Replicator is available, see the AWS Region table. To learn more, visit our developer guide or product page.  

You can now run OpenSearch version 2.15 in Amazon OpenSearch Service. With OpenSearch 2.15, we have made several improvements in the areas of search performance, query optimization, and added capabilities to help you to build AI-powered applications with greater flexibility and ease. This launch includes radial search that allows you to search points in a vector space that reside within a specified maximum distance or minimum score threshold from a query point, offering greater flexibility for various applications like anomaly detection and geospatial searches. In addition, this release includes performance optimizations like two-phase processor for neural sparse search, and conditional scoring logic and optimized data handling for hybrid search. These performance improvements now allow you to run complex queries on larger datasets more efficiently. OpenSearch now supports reindex workflow, allowing users to enable vector and hybrid search on existing indexes to reduce time and resources spent on re-indexing from source indexes. In addition, you can configure remote models to serve as guardrails to detect harmful, offensive, or inappropriate content (toxicity) more accurately. Finally, a new ML inference processor enables users to enrich ingest pipelines using inferences from OpenSearch-provided pretrained models. For information on upgrading to OpenSearch 2.15, please see the documentation. OpenSearch 2.15 is now available in all AWS Regions where Amazon OpenSearch Service is available.  

AWS Elastic Beanstalk now supports dual-stack public service endpoints and dual-stack VPC endpoints, including VPC endpoints integrated with AWS PrivateLink. This capability allows you to configure your Elastic Beanstalk VPC endpoints to accept dual-stack incoming traffic (via IPv6 and IPV4). You can also send requests to the Elastic Beanstalk service using the AWS CLI or the AWS SDK specifying an IPv4 endpoint or a dual-stack endpoint. For a list of public endpoints, see Elastic Beanstalk service endpoints in the Amazon Web Services General Reference. Elastic Beanstalk support for IPv6 and IPv4 dual-stack functionality is available in all of the AWS Commercial Regions and AWS GovCloud (US) Regions that Elastic Beanstalk supports. For a complete list of regions and service offerings, see AWS Regions. For more information about Elastic Beanstalk dual-stack traffic support, see IPV6 support in the AWS Elastic Beanstalk Developer Guide.

AWS Graviton3-based R7g database instances are now generally available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in 15 additional regions, including US West (N. California), Canada (Central), South America (Sao Paulo), Europe (Stockholm), Europe (Frankfurt), Europe (London), Europe (Milan), Europe (Spain), Asia Pacific (Mumbai), Asia Pacific (Hyderabad), Asia Pacific (Seoul), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Hong Kong). 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. AWS Graviton3 processors are custom-designed AWS Graviton processors built on the AWS Nitro System. The Graviton3 processors offer several improvements over the second-generation Graviton processors. R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). You can spin up Graviton3 R7g database instances in the Amazon RDS Management Console or using the AWS CLI. Upgrading a database instance to Graviton3 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.

Today, we’re announcing the general availability of secondary sensor support for AWS IoT SiteWise. Through an integration with AWS Partner CloudRail, customers can now ingest data from over 12,000 sensors from vendors like ifm, SICK, Turck, and Pepperl+Fuchs using either IO-Link or Modbus TCP/IP protocols. Secondary sensors enable data collection from isolated brownfield equipment and for customers to digitally integrate it with their other operational data. Previously, ingesting data from brownfield equipment required either upgrading equipment or manual processes for data collection resulting in manual errors, additional cost and time to value. Through a simple drop-down selection in the AWS Console, users add AWS Partner CloudRail software as a data source on their AWS IoT SiteWise Edge gateway and configure the desired sensor signals and protocols in the partner application. After deploying configurations, the equipment data flows to AWS IoT SiteWise Edge for local monitoring, storage, and access and on to AWS IoT SiteWise for integration with other industrial data and other AWS Cloud services. AWS IoT SiteWise is a managed service that makes it easy to collect, store, organize and monitor data from industrial equipment at scale. AWS IoT SiteWise Edge extends cloud capabilities to on-premises applications. This feature is generally available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Seoul), Asia Pacific (Sydney) and Canada (Central). To learn more, visit the AWS IoT SiteWise user guide.

Starting today, you can use RTMP (Real-Time Messaging Protocol) and the encrypted version, RTMPS, to broadcast to your Amazon Interactive Video Service (Amazon IVS) stages. This new protocol complements the currently supported WHIP (WebRTC-HTTP Ingestion Protocol). RTMP ingest enhances compatibility with a wide range of software and hardware encoders for increased flexibility in your broadcasting. Amazon IVS is a managed live streaming solution that is designed to be quick and easy to set up, and ideal for creating interactive video experiences. Video ingest and delivery are available around the world over a managed network of infrastructure optimized for live video. Visit the AWS region table for a full list of AWS Regions where the Amazon IVS console and APIs for control and creation of video streams are available. To learn more, please visit the Amazon IVS RTMP ingest documentation page.  

Today, we’re announcing the general availability of expanded industrial protocol support for AWS IoT SiteWise. Through a new integration with AWS Partner Litmus, customers can now ingest data from 100+ additional industrial protocols including proprietary protocols from companies like Allen-Bradley, Beckhoff, Emerson, Fanuc, Mitsubishi, Omron, and Yaskawa along with many others. Previously, ingesting data from these protocols required acquiring, provisioning, and configuring infrastructure and middleware for data collection resulting in additional cost and time to value. Through a simple drop-down selection in the AWS Console, users add AWS Partner Litmus Edge software as a data source on their AWS IoT SiteWise Edge gateway. Users then configure the protocols, build data flows, and configure data processing in the partner application. After configurations are deployed, the equipment data flows to AWS IoT SiteWise Edge for local monitoring, storage, and access. It is also sent to AWS IoT SiteWise for integration with other industrial data and usage in other AWS Cloud services. AWS IoT SiteWise is a managed service that makes it easy to collect, store, organize and monitor data from industrial equipment at scale. AWS IoT SiteWise Edge extends the cloud capabilities to on-premises applications. This new feature is generally available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Seoul), Asia Pacific (Sydney) and Canada (Central). To learn more, visit the AWS IoT SiteWise user guide.  

AWS Resource Access Manager (AWS RAM) now supports AWS PrivateLink, allowing you to create and manage your resource shares from within your Amazon Virtual Private Cloud (VPC) without traversing the public internet. AWS RAM helps you securely share your resources across your organization, with specific organizational units (OUs), or with individual AWS accounts. You can centrally create a resource and then share that resource using AWS RAM to reduce the operational overhead of managing resources in a multi-account environment. AWS RAM support for AWS PrivateLink is available in the AWS Commercial Regions, the AWS GovCloud (US) Regions, and the China Regions. To get started with using AWS RAM to share resources, visit the AWS Resource Access Manager Console.

You can now monitor your sources and get instant visual feedback for AWS Elemental MediaConnect flows with thumbnail images via the AWS Management Console or API. Thumbnails are also available in the Workflow Monitor tool. Input thumbnails provide a visual representation of your live content. Rather than relying solely on metadata or metrics, you can now glance at a thumbnail to verify that your sources are operating as expected. This makes it easier to detect issues, troubleshoot problems, confirm the right input source is being sent, and ensure the quality of your live video. For more information on thumbnail access, visit the MediaConnect documentation. Input thumbnails in MediaConnect are available at no additional cost. AWS Elemental MediaConnect is a reliable, secure, and flexible transport service for live video that enables broadcasters and content owners to build live video workflows and securely share live content with partners and customers. MediaConnect helps customers transport high-value live video streams into, through, and out of the AWS Cloud. MediaConnect can function as a standalone service or as part of a larger video workflow with other AWS Elemental Media Services, a family of services that form the foundation of cloud-based workflows to transport, transcode, package, and deliver video. Visit the AWS Region Table for a full list of AWS Regions where MediaConnect is available. To learn more about MediaConnect, please visit here.  

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances, powered by the latest NVIDIA H200 Tensor Core GPUs. Available via EC2 Capacity Blocks, these instances deliver the highest performance in Amazon EC2 for deep learning and generative AI inference. You can use Amazon EC2 P5e instances for training and deploying increasingly complex large language models (LLMs) and diffusion models powering the most demanding generative AI applications. You can also use P5e instances to deploy demanding HPC applications at scale in pharmaceutical discovery, seismic analysis, weather forecasting, and financial modeling. P5e instances feature 8 H200 GPUs which have 1.7x GPU memory size and 1.5x GPU memory bandwidth than H100 GPUs featured in P5 instances. They provide market-leading scale-out capabilities for distributed training and tightly coupled HPC workloads with up to 3,200 Gbps of networking using second-generation Elastic Fabric Adapter (EFA) technology. To address customer needs for large scale at low latency, P5e instances are deployed in Amazon EC2 UltraClusters. P5e instances are now available in the US East (Ohio) AWS Region in the p5e.48xlarge sizes through EC2 Capacity Blocks for ML. To learn more about P5e instances, see Amazon EC2 P5e Instances.  

Starting today, Amazon Kinesis Data Streams supports adding a VPC endpoint using AWS PrivateLink that connects through the regional endpoint that has been validated under the Federal Information Processing Standard (FIPS) 140-3 program. With this new launch, you can easily use AWS PrivateLink with Kinesis Data Streams for those regulated workloads that require a secure connection using a FIPS 140-3 validated cryptographic module. FIPS compliant endpoints helps companies contracting with the US federal governments meet the FIPS security requirement to encrypt sensitive data in supported Regions. To create an interface VPC endpoint that connects to a Kinesis Data Streams FIPS endpoint, see using Amazon Kinesis Data Streams with Interface VPC Endpoints. This new capability is available in all AWS Regions in the United States, including the AWS GovCloud (US) Regions. To learn more about AWS PrivateLink, see accessing AWS services through AWS PrivateLink. To learn more about FIPS 140-3 at AWS, visit FIPS 140-3 Compliance.  

Amazon RDS Custom for SQL Server is a managed database service that allows you to bring your own licensed SQL Server media and have access to the underlying operating system and database environment. This service is now available in the AWS Regions of US West (N. California), Asia Pacific (Osaka), and Europe (Paris). By using Amazon RDS Custom for SQL Server, you can benefit from the agility of a managed database service, including features such as managed high availability (MAZ), automated backups and point-in-time recovery, and cross-region snapshot copying. You can choose to deploy RDS Custom for SQL Server using your own licensed SQL Server media or with License Included (LI) instances. It can help you save time on the undifferentiated heavy lifting of database management and focus on more business-impacting tasks. To learn more about Amazon RDS Custom, please see the Amazon RDS Custom for SQL Server User Guide and Amazon RDS Custom Pricing for pricing details and availability in the additional regions.

CloudWatch Application Signals, which helps troubleshoot issues quickly by providing out-of-the-box dashboards that correlate telemetry across metrics, traces, and logs for your applications and their dependencies, now supports Service Level Objectives (SLOs) calculated based on the request count i.e. the fraction of good or bad requests of the total operations performed by any service. This allows for more precise tracking of how many requests met the defined SLO criteria (e.g., latency under 200ms) out of the total requests received by a service. SLOs represent a commitment to maintain a certain level of service quality, typically expressed as percentage attainment of performance goals within a given time interval. The existing offering of period-based SLOs measures performance during each time period in an interval and aggregates proportion of good periods that meet a specified performance threshold. The release of request-based SLOs allows you to track error tolerance in terms of requests. This enables fine-grained monitoring based on customer traffic, particularly useful for applications prone to high fluctuations in request volume, where performance shortfall during period of low traffic can lead to a quick erosion of the error budget. Request-based SLOs are available in all regions where Application Signals is generally available - 28 commercial AWS Regions except CA West (Calgary) Region. For pricing, see Amazon CloudWatch pricing. See SLO documentation to learn more, or refer to the user guide and AWS One Observability Workshop to get started with Application Signals.  

Amazon RDS for PostgreSQL 17 Release Candidate 1 (RC1) is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 17 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 17 RC1 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database. PostgreSQL 17 includes updates to vacuuming that reduces memory usage, improves time to finish vacuuming, and shows progress of vacuuming indexes. With PostgreSQL 17, you no longer need to drop logical replication slots when performing a major version upgrade. PostgreSQL 17 continues to build on the SQL/JSON standard, adding support for `JSON_TABLE` features that can convert JSON to a standard PostgreSQL table. The `MERGE` command now supports the `RETURNING` clause, letting you further work with modified rows. PostgreSQL 17 also includes general improvements to query performance and adds more flexibility to partition management with the ability to SPLIT/MERGE partitions. Please refer to the PostgreSQL community announcement for more details. 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. You can use the PostgreSQL dump and load functionality to import or export your databases from the Preview Environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.

Amazon Elastic Container Service (Amazon ECS) now supports AWS Graviton-based compute with AWS Fargate Spot. This capability helps you run fault-tolerant Arm-based applications with up to 70% discount compared to Fargate prices. AWS Graviton processors are custom-built by AWS to deliver the best price-performance for cloud workloads. Amazon ECS with AWS Fargate enables customers to deploy and build workloads at scale in a serverless manner. Customers can get better price-performance by running Arm-based workloads. Starting today, customers can further optimize for costs by running fault-tolerant Arm-based workloads on AWS Fargate Spot. To get started, just configure your task definition just like you do today with cpu-architecture = ARM64 and choose FARGATE_SPOT as the capacity provider to run your Amazon ECS service or a standalone task. Amazon ECS will leverage spare AWS Graviton-based compute capacity available in the AWS cloud for running your service or task. You can now get the simplicity of serverless compute with familiar cost optimization levers of Spot capacity with Graviton-based compute. This capability is now available for Amazon ECS tasks running on AWS Fargate platform version 1.4.0 or higher in all commercial and the AWS GovCloud (US) Regions. To learn more about usage and pricing of AWS Fargate Spot, see Fargate Capacity Providers documentation and the AWS Fargate pricing page.  

You can now use Amazon Timestream for InfluxDB in the Canada (Central), Europe (London) and Europe (Paris) AWS regions. Timestream for InfluxDB makes it easy for application developers and DevOps teams to run fully managed InfluxDB databases on AWS for real-time time-series applications using open-source APIs. To migrate to Timestream for InfluxDB from a self-managed InfluxDB instance, you can use our sample migration script by following this guide. Timestream for InfluxDB offers the full feature set available in the InfluxDB 2.7 release of the open-source version, and adds deployment options with Multi-AZ high availability and enhanced durability. For high availability, Timestream for InfluxDB allows you to automatically create a primary database instance and synchronously replicate the data to an instance in a different Availability Zone. When it detects a failure, Amazon Timestream automatically fails over to a standby instance without manual intervention. With the latest release, customers can use Amazon Timestream for InfluxDB in the following regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Canada (Central), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Paris), Europe (Frankfurt), Europe (Ireland), Europe (London), and Europe (Stockholm). To get started with Amazon Timestream, visit our product page.  

Amazon Relational Database Service (Amazon RDS) for MariaDB now supports MariaDB minor versions 10.11.9, 10.6.19, and 10.5.26. We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of MariaDB, and to benefit from the bug fixes, performance improvements, and new functionality added by the MariaDB community. You can leverage automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance windows. You can also leverage Amazon RDS Managed Blue/Green deployments for safer, simpler, and faster updates to your MariaDB instances. Learn more about upgrading your database instances, including automatic minor version upgrades and Blue/Green Deployments, in the Amazon RDS User Guide. Amazon RDS for MariaDB makes it straightforward to set up, operate, and scale MariaDB deployments in the cloud. Learn more about pricing details and regional availability at Amazon RDS for MariaDB. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.  

Amazon S3 Access Grants now support ListCallerAccessGrants, a new API that allows AWS Identity and Access Management (IAM) principals and AWS IAM Identity Center end users to list all S3 buckets, prefixes, and objects they can access, as defined by their S3 Access Grants. Customers can use ListCallerAccessGrants to build applications that identify and then take action on data that is accessible to specific end users. For example, the Storage Browser for Amazon S3, an open source UI component that customers can add to their applications to provide end users with a simple interface for data stored in S3, uses ListCallerAccessGrants to present end users with the data that they have access to in S3, based on their S3 Access Grants. S3 Access Grants map identities in AWS IAM or Identity Providers (IdPs) to your datasets in S3. When customers call the ListCallerAccessGrants action, S3 identifies the IAM principal or IAM Identity Center user and their associated groups. The API then returns the S3 Access Grants for the end user and their groups based on group membership in AWS IAM or an IdP. The ListCallerAccessGrants API is available in all AWS Regions where AWS IAM Identity Center is available. For pricing details, visit Amazon S3 pricing. To learn more about S3 Access Grants, visit the S3 User Guide.

Amazon Web Services announces the ability to setup Amazon WorkSpaces Pools powered by Microsoft Windows 10 and 11 operating systems using Bring Your Own License (BYOL). Now, customers can bring their Windows 10 or 11 licenses (provided they meet Microsoft's licensing requirements) to support their eligible Microsoft 365 Apps for enterprise, providing a consistent desktop experience to their users when they switch between on-premise and virtual desktops. WorkSpaces Pools simplifies management across a customer’s WorkSpaces environment by providing a single console and set of clients to manage the various desktop hardware configurations, storage, and applications for their users. With BYOL, the operating system is hosted on hardware that is dedicated to admin’s AWS account and they can automatically scale a pool of virtual desktops based on real-time usage metrics or predefined schedules. WorkSpaces Pools offers pay-as-you-go hourly pricing that excludes any operating system fees, providing significant savings. To take advantage of this option, the organization must meet the Microsoft licensing requirements, and must commit to running a minimum number of WorkSpaces in a given AWS Region each month. BYOL for Windows 10 and 11 is supported for Standard, Performance, Power and PowerPro bundles. For the best experience with video conferencing we recommend using Power or PowerPro bundles only. For Region availability details, see AWS Regions and Availability Zones for WorkSpaces Pools. To learn more about this option and the eligibility requirements, please see the Amazon WorkSpaces BYOL documentation and FAQs on BYOL.

You can now automatically mark a performance evaluation question as not applicable based on conversational insights (e.g., detected call reason, etc.), and you can now use additional contact metrics (e.g., longest hold duration, number of holds, agent interaction duration including holds) to automatically fill answers to questions in the evaluation form. With this launch, you can automatically complete only the applicable evaluation questions, under specific conditions. For example, you could check if an agent explained new account benefits and pricing, only for those customers who called to open an account. Additionally, you can automatically evaluate whether the agent was able to resolve the customer’s issue efficiently (e.g., resolved the customer’s issue within 10 minutes) and did not put the customer repeatedly on hold. This feature is available in all regions where Contact Lens performance evaluations is already available. To learn more, please visit our documentation and our webpage. For information about Contact Lens pricing, please visit our pricing page.

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R7i instances are available in Europe (Milan). Amazon EC2 R7i instances are powered by custom 4th Generation Intel Xeon Scalable processors (code-named Sapphire Rapids), available only on AWS, offer up to 15% better performance over comparable x86-based Intel processors utilized by other cloud providers. R7i instances deliver up to 15% better price-performance versus R6i instances. These instances are SAP certified and are a great choice for memory-intensive workloads, such as SAP, SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases like SAP HANA, and real time big data analytics like Hadoop and Spark. They offer larger instance sizes, up to 48xlarge, and two bare metal sizes (metal-24xl, metal-48xl) for high-transaction and latency-sensitive workloads. These bare-metal sizes support built-in Intel accelerators: Data Streaming Accelerator, In-Memory Analytics Accelerator, and QuickAssist Technology, allowing customers to facilitate efficient offload and acceleration of data operations and optimize performance for workloads. R7i instances support the new Intel Advanced Matrix Extensions (AMX) that accelerate matrix multiplication operations for applications such as CPU-based ML. In addition, customers can now attach up to 128 EBS volumes to an R7i instance (vs 28 EBS volume attachments on R6i). This allows processing of larger amounts of data, scale workloads, and improve performance over R6i instances. To learn more, visit Amazon EC2 R7i Instances.

Amazon S3 is announcing the alpha release of Storage Browser for S3, an open source component that you can add to your web applications to provide your end users with a simple interface for data stored in S3. With Storage Browser for S3, you can provide authorized end users access to easily browse, download, and upload data in S3 directly from your own applications. Storage Browser for S3 is available in the AWS Amplify JavaScript and React client libraries. Storage Browser for S3 only displays the data that your end users are authorized to access and automatically optimizes requests to deliver high throughput data transfer. You can control access to your data based on your end user’s identity using AWS security and identity services or your own managed services. You can also customize Storage Browser for S3 to match your existing application’s design and branding. We are making the alpha release of Storage Browser for S3 available to collect early feedback and incorporate community input into the design and implementation. To get started with Storage Browser for S3, visit the GitHub page.  

Amazon RDS for Oracle now supports Oracle Enterprise Manager (OEM) and Oracle Label Security (OLS) option with Oracle Multitenant configuration. OEM enables monitoring and managing the Oracle infrastructure from a single console. OLS provides fine-grained control of access to individual tables or rows, and helps you enforce regulatory compliance with a policy-based administration. To enable OEM on your Amazon RDS for Oracle database instance with multitenant configuration, you can add an option to a new or existing option group. You can use OEM option for OEM Database Express for a lightweight tool for managing a single database instance, or OEM_AGENT for OEM Cloud Control for an enterprise-level tool for managing a large environment. To learn more about enabling OEM, please refer to Amazon RDS for Oracle documentation. To enable the OLS option on your Amazon RDS for Oracle database instance with multitenant configuration, you can add the option to a new or existing option group. In order to use the OLS option, you need to have a valid Oracle Label Security option license, in addition to an Oracle Enterprise Edition license with “Software Update License & Support”. To learn more about enabling OLS, please refer to Amazon RDS for Oracle documentation. 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 AWS Gateway Load Balancer (GWLB) is launching a new capability that allows you to align the TCP idle timeout value of GWLB with clients and target appliances. Using this capability you can now perform uninterrupted stateful inspection and fine tuning of the applications that use long-lived flows, such as financial applications, databases and ERP systems, when using GWLB. Prior to this launch, TCP idle timeout was a fixed value of 350 seconds, which could interrupt long-lived traffic flows of some applications. With this launch, you now have the flexibility to configure GWLB’s TCP idle timeout to be a value between 60 seconds and 6000 seconds, with the default remaining at 350 seconds for backward compatibility. This configuration can help reduce interruptions in traffic flows by maintaining target stickiness for the optimal duration based on the needs of your application. You can configure the TCP idle timeout value using the 'tcp.idle_timeout.seconds' listener attribute on your existing and newly created GWLBs. This capability is available in all AWS commercial and the AWS GovCloud (US) Regions. For more information on how to use this feature, see this AWS blog post and product documentation.  

Amazon Managed Service for Apache Flink now supports Apache Flink minor version 1.20. This version is expected to be be the last 1.x minor version released by the Flink community before Flink 2.0. We recommend that you upgrade to Flink 1.20 to benefit from bug fixes, performance improvements, and new functionality added by the Flink community. You can use in-place version upgrades for Apache Flink to upgrade your existing application to this new version. Amazon Managed Service for Apache Flink makes it easier to transform and analyze streaming data in real time with Apache Flink. Apache Flink is an open source framework and engine for processing data streams. Amazon Managed Service for Apache Flink reduces the complexity of building and managing Apache Flink applications and integrates with Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis Data Streams, Amazon OpenSearch Service, Amazon DynamoDB streams, Amazon S3, custom integrations, and more using built-in connectors. Create or update an Amazon Managed Service for Apache Flink application in the Amazon Managed Service for Apache Flink console. You can learn more about Apache Flink 1.20 in Amazon Managed Service for Apache Flink in our documentation. To learn more about open source Apache Flink visit the official website. For Amazon Managed Service for Apache Flink region availability, refer to the AWS Region Table.  

Amazon RDS Custom for SQL Server now supports copying database snapshots, either created automatically or manually, across commercial AWS Regions. This enables you to seamlessly move database snapshots for your RDS Custom for SQL Server instances to different Regions, which can be used to build a robust disaster recovery solution for your mission-critical data. RDS Custom for SQL Server is a managed database service that allows you to bring your own licensed SQL Server software and customize the underlying operating system. With today's launch, you can manually copy RDS Custom for SQL Server database snapshots to other AWS Regions, enabling data retention, compliance, and disaster recovery for your applications deployed across multiple Regions. For more information see Copying an Amazon RDS Custom for SQL Server DB snapshot. When you create a snapshot of your RDS Custom for SQL Server instance, it captures the entire database instance, not just individual databases. To copy the snapshot in a different AWS region, select the snapshot, choose “Copy Snapshot” from the Snapshot actions menu, pick a destination region, and enter a name for the new snapshot. You can initiate the snapshot copy process from the AWS Management Console, the AWS Command Line Interface (CLI), or through the Amazon RDS Custom for SQL Server APIs. Amazon RDS Custom for SQL Server with cross-region snapshot copying is available in all commercial AWS Regions where RDS Custom for SQL Server is offered.  

Agents for Amazon Bedrock enable developers to create generative AI-based applications that can complete complex tasks for a wide range of use cases and deliver answers based on company knowledge sources. In order to complete complex tasks, with high accuracy, reasoning capabilities of the underlying foundational model (FM) play a critical role. Today, Amazon Bedrock customers in US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Singapore) can leverage Claude 3.5 Sonnet with their Bedrock Agents. Claude 3.5 Sonnet is Anthropic’s latest foundation model and ranks among the best in the world. Claude 3.5 Sonnet delivers improved speed, performance and agentic reasoning compared with Claude 3 Opus. Additionally, with this model, Bedrock Agents now supports the Anthropic recommended tool use for function calling which leads to an improved developer and end user experience. To learn more, read the Claude in Amazon Bedrock product page and documentation. To get started with Claude 3.5 Sonnet in Amazon Bedrock, visit the Amazon Bedrock console. To learn more about the list of models supported on Bedrock Agents, visit the documentation page.

Today, AWS announces the addition of DEBUG and INFO logging levels for AWS AppSync GraphQL APIs. These new logging levels provide more granular control over log verbosity and make it easier to troubleshoot your APIs while optimizing readability and costs. With DEBUG and INFO levels, alongside the existing ERROR and ALL levels, customers now have greater flexibility to capture relevant log information at the appropriate level of detail. This allows customers to more precisely pinpoint and resolve issues by sending just the right amount of information to their Amazon CloudWatch Logs. Customers can now log messages from their code with the “error”, “log”, and “debug” functions and configure the level at which logs will be sent to CloudWatch Logs on their API. The API logging level can be changed at any time without having to change any resolver or function code. For example, an API’s logging level can be set to DEBUG during development and troubleshooting but changed to INFO in production. The logging level can be set to ALL to see additional trace information. The new logging levels are available in all AWS Regions where AppSync is supported. To learn more about AppSync's new logging levels and how to implement them in your GraphQL APIs, see the AWS AppSync Developer Guide.  

You can now run petabyte-scale data analytics and machine learning on Amazon EMR Serverless directly from Amazon SageMaker Studio notebooks. EMR Serverless automatically provisions and scales the required resources, allowing you to focus on your data and models without having to configure, optimize, tune, or manage clusters. EMR Serverless automatically installs and configures open source frameworks and provides a performance-optimized runtime that is compatible with and faster than standard open source. With this release, you can now visually create and browse EMR Serverless applications directly from SageMaker Studio and connect to them in a few simple clicks. Once connected to an EMR Serverless application, you can use Spark SQL, Scala, Python to interactively query, explore and visualize data, and run Apache Spark jobs to process data directly from Studio Notebooks. Jobs run fast because they use EMR’s performance-optimized versions of Spark. For e.g. Spark on EMR 7.1 is 4.5x faster than it’s open source equivalent. EMR Serverless offers fine-grained automatic scaling, which provisions and quickly scales the compute and memory resources to match the requirements of your application and you pay for only what you use. These features are supported on SageMaker Distribution 1.10 and above, and are generally available in all AWS Regions where SageMaker Studio is available. To learn more, read the blog Use LangChain with PySpark for Processing documents at massive scale with Amazon SageMaker Studio and EMR Serverless, or the SageMaker Studio documentation here.  

Today, Amazon Simple Email Service (SES) launched enhancements to its onboarding experience to help customers easily discover and activate key SES features. The SES console now features an adaptive setup page that brings recommendations for optimal setup to the forefront. Additionally, the update introduces the option to enable the Virtual Deliverability Manager (VDM) within the initial onboarding wizard, offering maximum guidance from the beginning of the setup process. Previously, the SES onboarding process focused primarily on the initial steps of authenticating domains and attaining production access. With an on-demand advisor check, this workflow guides customers to begin sending authenticated email that meets mailbox provider requirements with DKIM-alignment, SPF-alignment, and a DMARC policy. Now, the enhanced onboarding experience empowers customers to optimize their email deliverability from the start. Customers can easily configure email monitoring, provision dedicated IP addresses, and adjust sending limits to meet their projected volume - all through the adaptive setup page that detects their current configuration and provides tailored recommendations. With these enhancements, SES customers can be confident they are setting up their email infrastructure for long-term success from the beginning of their SES journey. SES offers a guided onboarding experience in all AWS regions where SES is available. For more information, please visit the documentation for getting started with SES.  

Stable Image Ultra, Stable Diffusion 3 Large (SD3 Large), and Stable Image Core models from Stability AI are now generally available in Amazon Bedrock. These models will empower customers in various industries, including media, marketing, retail, and game development, to generate high-quality visuals with unprecedented speed and precision. All three of these models are capable of generating stunningly photo-realistic images with exceptional detail, color accuracy, and lifelike lighting. Each model caters to diverse use cases: Stable Image Ultra – produces the highest quality, photo-realistic outputs, making it perfect for professional print media and large-format applications. This model excels at rendering exceptional detail and realism. Stable Diffusion 3 Large – strikes an ideal balance between generation speed and output quality, making it ideal for creating high-volume, high-quality digital assets like websites, newsletters, and marketing materials. Stable Image Core – optimized for fast and affordable image generation, making it great for rapidly iterating on concepts during the ideation phase. These models enable customers to streamline creative processes, swiftly adapt to market trends, drive innovation through visual brainstorming, and gain a competitive advantage by boosting productivity, reducing costs, and improving visual communication across business functions. Stability AI’s Stable Image Ultra, SD3 Large, and Stable Image Core models are now available in Amazon Bedrock in the US West (Oregon) AWS region. To learn more read the AWS News Blog or visit the Stability AI in Amazon Bedrock product page, and documentation. To get started with SD3 in Amazon Bedrock, visit the Amazon Bedrock console.

We are excited to announce the launch of enhanced management options for Amazon Timestream for InfluxDB, allowing you to scale your instance sizes up or down as needed and update your deployment configuration between Single-AZ and Multi-AZ, giving you greater flexibility and control over your time-series data processing and analysis. Timestream for InfluxDB is extensively used in applications that require high-performance time-series data processing and analysis. You can quickly respond to changes in data ingestion rates, query volumes, or other workload fluctuations by scaling your instances sizes up and down, ensuring that your Timestream for InfluxDB instances always have the necessary resources to handle your workload and cost effectively. You can also change your availability configuration by moving between Single-AZ and Multi-AZ configurations depending on your needs and budget. This means you can focus on building and deploying your applications, rather than worrying about instance sizing and management. Amazon Timestream for InfluxDB is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Mumbai), Europe (Ireland), Europe (Frankfurt), and Europe (Stockholm). You can create a Amazon Timestream Instance from the Amazon Timestream console, AWS Command line Interface (CLI), or SDK, and AWS CloudFormation. To learn more about compute scaling for Amazon Timestream for InfluxDB, visit the product page, documentation, and pricing page.  

Starting today, memory-optimized Amazon Compute Cloud (Amazon EC2) X2idn instances are available in Middle East (Bahrain) region. These instances, powered by 3rd generation Intel Xeon Scalable Processors and built with AWS Nitro System, are designed for memory-intensive workloads. They deliver improvements in performance, price performance, and cost per GiB of memory compared to previous generation X1 instances. These instances are SAP-certified for running Business Suite on HANA, SAP S/4HANA, Data Mart Solutions on HANA, Business Warehouse on HANA, SAP BW/4HANA, and SAP NetWeaver workloads on any database. With this launch, X2idn are available in the following AWS Regions: US East (Ohio, N. Virginia), US West (Oregon, N. California), Africa (Cape Town), Asia Pacific (Hyderabad, Jakarta, Malaysia, Mumbai, Osaka, Seoul, Singapore, Sydney, Tokyo), China (Beijing, Nginxia), Middle East (Bahrain, Dubai), Europe (Frankfurt, Ireland, London, Milan, Paris, Stockholm, Spain, Zurich), Canada (Central), South America (São Paulo), and the AWS GovCloud (US-East, US-West) Regions. X2idn is available for purchase with Savings Plans, Reserved Instances, Convertible Reserved, On-Demand, and Spot instances, or as Dedicated instances or Dedicated hosts. To learn more, visit the EC2 X2i Instances Page, or connect with your AWS Support contacts.