You can now create Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters running on AWS Graviton3-based M7g instances or upgrade your existing x-86 based based M5 or T3 instances and replace them with AWS Graviton3-based M7G instances with a single click of a button in the Europe (London) AWS Region. AWS Graviton3 processor based M7G instances on Amazon MSK provisioned clusters allows you to achieve up to 24% compute cost savings and up to 29% higher write and read throughput over comparable MSK clusters running on M5 instances. Additionally, these instances lower energy consumption by up to 60% than comparable instances, making your Kafka clusters more environmentally sustainable. Please refer to our blog for more information on the price/ performance improvements of M7g instances and the Amazon MSK pricing page for information on pricing. To get started, you can update your existing clusters to M7G brokers using the AWS Management Console, and read our developer guide for more information.  

Corretto 23 is now available for download. Amazon Corretto is a no-cost, multi-platform, production-ready distribution of OpenJDK. Corretto 23 is an OpenJDK 23 Feature Release, which will be supported through April, 2025. OpenJDK 23 introduces enhanced performance with updates to the Z Garbage Collector and Vector API, along with improved JIT compilation for faster code execution. Additionally, it brings expanded pattern matching and switch expression capabilities for more concise and expressive coding. For more information about the features in OpenJDK 23, a detailed description can be found on its Project page. Amazon Corretto is distributed by Amazon under an open source license. Click on the Corretto home page to download Corretto 23. You can also get the updates on your Linux system by configuring a Corretto Apt or Yum repo.  

New minor versions of Microsoft SQL Server are now available on Amazon RDS for SQL Server, providing performance enhancements and security fixes. Amazon RDS for SQL Server now supports these latest minor versions of SQL Server 2016, 2017, 2019 and 2022 across the Express, Web, Standard, and Enterprise editions. We encourage you to upgrade your Amazon RDS for SQL Server database instances at your convenience. You can upgrade with just a few clicks in the Amazon RDS Management Console or by using the AWS CLI. Learn more about upgrading your database instances from the Amazon RDS User Guide. The new minor versions include: SQL Server 2016 - 13.00.6445.1 SQL Server 2017 - 14.00.3475.1 SQL Server 2019 - 15.00.4390.2 SQL Server 2022 - 16.00.4140.3 These minor versions are available in all AWS commercial regions where Amazon RDS for SQL Server databases are available, including the AWS GovCloud (US) Regions. Amazon RDS for SQL Server makes it simple to set up, operate, and scale SQL Server deployments in the cloud. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.

AWS CodeBuild now supports managed GitLab self-hosted runners. Customers can configure their CodeBuild projects to receive GitLab CI/CD job events and run them on CodeBuild ephemeral hosts. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages ready for deployment. This feature allows GitLab jobs to integrate natively with AWS, providing security and convenience through features such as IAM, AWS Secrets Manager, AWS CloudTrail, and Amazon VPC. Customers can access all compute platforms that CodeBuild offers, including Lambda, GPU-enhanced and Arm-based instances. CodeBuild's integration with GitLab runners is available in all regions where CodeBuild is offered. For more information about the AWS Regions where CodeBuild is available, see the AWS Regions page. Get started by setting up webhooks in a CodeBuild project, and updating your GitLab CI YAML to use self-managed runners hosted on CodeBuild machines. To learn more about runners powered by CodeBuild for GitLab or GitHub, see CodeBuild’s documentation for self-hosted runners in AWS CodeBuild.

Amazon S3 Express One Zone now supports server-side encryption with AWS Key Management Service (SSE-KMS) using customer managed keys. By default, S3 Express One Zone encrypts all objects with server-side encryption using S3 managed keys (SSE-S3). With S3 Express One Zone support for customer managed keys, you have more options to encrypt and manage the security of your data. S3 Bucket Keys are always enabled when you use SSE-KMS with S3 Express One Zone, at no additional cost. With customer managed keys, you can set key policies that govern which IAM roles can decrypt your data and see a full accounting in AWS CloudTrail of the specific keys used to encrypt and decrypt your data. In addition, with S3 Bucket Keys, KMS generates a bucket-level key instead of an individual KMS key for each KMS encrypted object. S3 Express One Zone uses this bucket key to secure unique data keys that are used to encrypt objects in a bucket, avoiding the need for additional KMS requests to complete encryption operations. This results in reduction of request traffic to KMS, allowing you to access encrypted objects in S3 Express One Zone at a fraction of the cost while maintaining the same single-digit millisecond data access. S3 Express One Zone support for SSE-KMS using customer managed keys is available in all AWS Regions where the storage class is available. Get started with KMS for S3 Express One Zone by using the S3 console, AWS CLI, or AWS SDKs to specify the customer managed key for your S3 directory bucket. To learn more, visit the S3 User Guide and AWS News Blog.

Today, AWS announces the general availability of the AWS SDK for Swift, the official AWS solution for accessing Amazon Web Services from applications built with the Swift programming language. Customers can now use AWS SDK for Swift for production workloads. AWS SDK for Swift provides a modern, user-friendly, and native Swift interface for accessing Amazon Web Services from Apple platforms, AWS Lambda, and Linux-based Swift on Server applications. Swift is the modern language of choice for developing natively on Apple devices, and is also widely used on Linux for server and utility purposes. AWS SDK for Swift fully embraces modern Swift language features and provides access to AWS services with an interface that will feel natural to the Swift developer community. The SDK supports features like Swift structured concurrency, binary data and event streaming, waiters and paginators, automatic retries, and flexible HTTP client options. The AWS SDK for Swift provides everything a developer needs to fully integrate their application with their AWS services. To get started, see the following list of resources: AWS SDK for Swift Developer Guide AWS SDK for Swift Blog post  Source Code - AWS SDK for Swift GitHub repo

Today, AWS announces the release of Neuron 2.20, introducing Neuron Kernel Interface (NKI) (beta), a programming interface for AWS Trainium and Inferentia, enabling developers to build optimized compute kernels for new functionalities, optimizations, and science innovations. Additionally, this release introduces NxD Training (beta), a PyTorch-based library enabling efficient distributed training, with a user-friendly interface compatible with NeMo. This release also introduces support for the JAX framework (beta). AWS Neuron is the SDK for AWS Inferentia and Trainium based instances purpose-built for generative AI. Neuron integrates with popular ML frameworks like PyTorch. It includes a compiler, runtime, tools, and libraries to support high performance training and inference of AI models on Trn1 and Inf2 instances. This release adds support features and performance improvements for model training and inference. For training, this release adds Llama 3.1 8B and 70B models support up to 32K sequence length, along with torch.autocast() for native PyTorch mixed precision support and PEFT LoRA techniques. For inference, Neuron 2.20 adds support for Llama 3.1 (405b, 70b, 8b) and Diffusion-Transformers (DiT) models like Pixart-alpha and Pixart-sigma. Additionally, this release adds inference support with top-p sampling on device and 128K context length with Flash Decoding. This release also adds support for Rocky 9.0 operating system and RMSNorm and RMSNormDx operators in the Neuron Compiler. For more information, see Neuron Release Notes.

AWS Transfer Family announces higher performance limits for SFTP connectors, with maximum supported file size increased to 150 GB and maximum throughput increased to 100 files per second. SFTP connectors provide fully managed and low-code capability to copy files between remote SFTP servers and Amazon S3. With today’s enhancements, customers can use connectors to easily scale workflows such as media files processing and database migrations that involve transferring large volume of files or larger file sizes over SFTP. Additionally, customers can achieve higher performance using SFTP connectors with remote SFTP servers that support concurrent sessions Increased limits for SFTP connectors are available in all AWS Regions where the service is available. To learn more about SFTP connectors, visit the documentation. To get started with Transfer Family’s SFTP offerings, take the self-paced SFTP workshop.  

AWS Elastic Disaster Recovery (AWS DRS) now supports recovery to AWS Local Zones. This feature allows you to set your recovery target to be within an AWS Local Zone. AWS DRS minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery. With this release, customers can launch recovered instances in a geography closer to their end users and data sources, which means lower latency and maintenance of data residency. This new capability adds support for all AWS Local Zones that are available in AWS Regions where AWS DRS is available. See the AWS Regional Services List for the latest availability information. To learn more about AWS DRS, visit our product page or documentation. To get started, sign in to the AWS Elastic Disaster Recovery Console.  

AWS Chatbot now enables customers to interact with Amazon Bedrock agents from Slack and Microsoft Teams chat channels. Prior to today, customers had to implement their own ad-hoc ways to integrate their Bedrock agents with their Microsoft Teams and Slack. They needed to develop custom chat applications and integrate it with Bedrock agents. Now with this launch, they can invoke their Bedrock agents from chat channels by connecting the Bedrock agent alias with an AWS Chatbot channel configuration. Once connected, channel members can start using the agent by tagging the agent such as “@aws ask connectorname ...”. AWS Chatbot support for Amazon Bedrock agents in chat applications is available at no additional cost in all AWS Regions where AWS Chatbot is offered. To learn more, visit the Connect Amazon Bedrock agents documentation page.

Amazon Keyspaces (for Apache Cassandra) is a scalable, serverless, highly available, and fully managed Apache Cassandra-compatible database service that offers 99.999% availability. Today, Amazon Keyspaces added the capability to add columns to existing multi-Region tables. With this launch, you can modify the schema of your existing multi-Region tables to add new columns. You only have to modify the schema of a multi-Region table in one of its replica regions and Keyspaces will replicate the new schema to the other regions where the table exists. Adding columns to multi-Region tables gives you the flexibility to update your data model seamlessly as your business and application needs evolve over time. Support for adding columns to multi-Region tables is available in all commercial AWS Regions where AWS offers Amazon Keyspaces. If you’re new to Amazon Keyspaces, the Getting Started guide shows you how to provision a Keyspace and explore the query and scaling capabilities of Amazon Keyspaces.  

AWS Amplify now supports asynchronous AWS Lambda invocations from your AWS AppSync API. This new feature allows you to invoke Lambda functions asynchronously, enabling your API to handle more complex, long-running processes without blocking the response to the client. With asynchronous Lambda invocations, developers can initiate operations like generative AI model inferences, batch processing jobs, or message queuing without blocking the GraphQL API response. This improves application responsiveness and scalability, especially for scenarios where immediate responses are not required or where long-running tasks need to be offloaded. The asynchronous Lambda invocation feature for AppSync is available in all AWS regions supported by AWS AppSync. In AWS Amplify, customers can opt in by defining an async function handler in their data schema. To learn more, refer to the Amplify documentation.  

Today, AWS launched volume discount pricing for AWS Elemental MediaConvert, providing customers with on-demand pricing which scales down as usage of the service goes up. Volume discount pricing takes effect during the September, 2024 billing cycle. With on-demand volume discount pricing, you can save up to 75% off list prices based on your monthly usage. During each monthly billing cycle, your usage will automatically flow through increasingly discounted rates which correspond to usage thresholds of MediaConvert. Basic Tier and Pro Tier usage accrue separately to meet volume discount thresholds each month per region, and each billing cycle restarts the accumulation of usage towards these thresholds. Volume discount pricing is applied automatically to list prices for MediaConvert on your monthly bill - there is no action required on your part and no change to the AWS Management Console, AWS CLI, or AWS SDKs. For more information about the AWS Regions where MediaConvert is available, see the AWS Region table. To learn more about volume discount pricing, including the thresholds and examples of billing scenarios, please refer to the pricing page and the user guide documentation.

AWS Private Certificate Authority (AWS Private CA) launches General Availability of the Connector for SCEP, which lets you use a managed and secure cloud certificate authority (CA) to enroll mobile devices securely and at scale. Simple Certificate Enrollment Protocol (SCEP) is a protocol widely adopted by mobile device management (MDM) solutions to obtain digital identity certificates from a CA and enroll corporate-issued and bring-your-own-device (BYOD) mobile devices. The Connector for SCEP helps you to reduce your PKI operational costs by using AWS Private CA with SCEP-compatible MDM solutions including, but not limited to, Microsoft Intune and Jamf Pro. The Connector for SCEP is one of three connectors that allow you to use AWS Private CA with Kubernetes, Active Directory, and, now, mobile devices. By using connectors, you can replace your existing CAs with AWS Private CA in environments that have an established native certificate distribution solution. This means that instead of using multiple CA solutions, you can utilize a single private CA solution for your enterprise. The Connector for SCEP is available in AWS Regions where AWS Private CA is available excluding GovCloud and China regions. This feature is offered at no additional charge. You only pay for the AWS Private CAs and the certificates issued from them. To get started, see the Getting started guide or go to the Connector for SCEP console.

Zero-ETL integrations help 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. Amazon Redshift zero-ETL integrations now support Redshift clusters and Serverless workgroups configured with enhanced VPC routing (EVR). When you use Amazon Redshift enhanced VPC routing, Amazon Redshift forces all COPY and UNLOAD traffic between your warehouse and data repositories through your virtual private cloud (VPC) based on the Amazon VPC service. To learn more and get started with zero-ETL integration, visit the getting started guides for Amazon Redshift. To learn more about enhanced VPC routing on Amazon Redshift, see documentation.

Amazon Redshift announces the general availability of Amazon Q generative SQL in Amazon Redshift Query Editor, an out-of-the-box web-based SQL editor for Amazon Redshift. Amazon Q generative SQL simplifies query authoring and increases productivity by allowing you to express queries in natural language and receive SQL code recommendations. Furthermore, it enables you to get insights faster without extensive knowledge of your organization’s complex database schema and metadata. Amazon Q generative SQL uses generative AI to analyze user intent, query patterns, and schema metadata to identify common SQL query patterns directly within Amazon Redshift, accelerating the query authoring process for users and reducing the time required to derive actionable data insights. It provides a conversational interface where users can submit queries in natural language within the scope of their current data permissions. Generative SQL leverages query history for better accuracy and you can further improve accuracy through custom context, such as table descriptions, column descriptions, foreign key and primary key definitions, and sample queries. Custom context enhances the AI model's understanding of your specific data model, business logic, and query patterns, allowing it to generate more relevant and accurate SQL recommendations. Amazon Q generative SQL in Amazon Redshift is available in the following regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Paris), and Europe (Ireland) regions. To learn more about pricing, visit the Amazon Q Developer pricing page. See the documentation to get started.

AWS announces Amazon Aurora PostgreSQL Optimized Reads in the AWS GovCloud (US) Regions, a new price-performance capability available on new r6gd and r6id instances that delivers up to 8x improved query latency and up to 30% cost savings compared to instances without it, for applications with large datasets that exceed the memory capacity of a database instance. Optimized Reads uses the local NVMe-based SSD block-level storage available on r6gd and r6id instances to store ephemeral data, reducing the data accesses to/from network-based storage offering improved read latency and throughput. These instances host temporary tables on the local storage (instead of network-based storage), delivering improved query performance for complex queries and faster index rebuild operations. Optimized Reads instances using I/O-Optimized use the local storage to extend their caching capacity. Database pages that are evicted from the in-memory buffer cache are cached onto local storage to speed subsequent retrievals of that data. This delivers up to 8x improved query latency, and enables Amazon Aurora PostgreSQL Optimized Reads with pgvector to increase queries per second for vector search by up to 9x in workloads that exceed available instance memory, accelerating the performance of Machine Learning and Generative AI applications. Customers can get started with Optimized Reads through the AWS Management Console, CLI, and SDK by modifying their Aurora database clusters or creating a new one using r6gd or r6id instances. Optimized Reads is available for Aurora Postgres 14.9 and 15.4. For more information visit our pricing page, and documentation.  

Amazon Connect now provides AWS CloudFormation for configuring agent hierarchy structures. You can use CloudFormation templates to programmatically deploy Amazon Connect hierarchy levels in a secure, efficient, and repeatable way, reducing the risk of human error from manual configuration. CloudFormation allows you to track changes over time, apply updates in a controlled and automated manner, and includes version controls so you can easy roll back changes if needed. CloudFormation support for agent hierarchies is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.

AWS WAF introduces a new version of Bot Control Managed Rule Group with enhanced features to help customers better manage and protect their web applications from bot activity. This includes: Token reuse detection: Identifies the reuse of WAF tokens across autonomous system numbers (ASNs) and geographic locations, in addition to existing IP-based detection. Customizable sensitivity levels – High, Medium, and Low – enable you to take mitigation actions based on your use cases. For example, customers with mobile users who frequently change IPs may allow higher token reuse, while those with more static applications can enforce stricter controls at lower thresholds. Expanded Bot Categories: 19 new bots, including several verified AI bots, added to various categories. Cloud Service Provider Labels: New labels that allow customers to selectively allow or block traffic from specific cloud service providers. Automated Browser Extension Labels: Labels to detect the presence of browser extensions that assist in web automation, such as Selenium IDE, complementing the existing automated browser signals. Improved CloudWatch Visibility: WAF label is now emitted for each matched rule, enhancing visibility in CloudWatch logs. This change is also included in the new 1.1 versions of the AWS WAF Fraud Control rule groups. AWS WAF Bot Control Managed Rule group is available in all AWS Regions, except the AWS GovCloud (US) Regions and the China Regions. To use the latest versions, you will have to manually change the version number of Bot Control Managed Rule group. To learn more, please review the documentation.

Today, Amazon QuickSight is announcing the general availability of Direct Query feature for its Google BigQuery data source. This launch is an extension of the feature here wherein QuickSight launched BigQuery connector support through SPICE (Super-fast, Parallel, In-memory Calculation Engine). Direct Query mode allows customers to perform near real-time querying of their BigQuery data. The feature doesn’t have a limitation on the number of rows or the data size that could be imported for a dataset. Direct queries also enable customers to extract data directly from BigQuery instead of having to cache it in SPICE. Google BigQuery Direct Query functionality in QuickSight is now available in all supported Amazon QuickSight regions here.

Amazon Connect now supports AWS CloudFormation for agent status, in addition to other resources used to configure a contact center like routing profiles, queues, Amazon S3 buckets, and AWS Lambda. You can use CloudFormation templates to programmatically deploy Amazon Connect agent statuses in a secure, efficient, and repeatable way, allowing you to ensure consistent configurations. CloudFormation allows you to track changes over time, apply updates in a controlled and automated manner, and includes version controls so you can easy roll back changes if needed. CloudFormation support for agent status is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect, the AWS cloud-based contact center, please visit the Amazon Connect website.

Amazon Neptune Database is now available in the AWS Asia Pacific (Jakarta) Region on engine versions 1.3.3.0 and later. You can now create Neptune clusters using R5, R5d, R6g, R6i, X2iedn, T3, and T4 instance types in the AWS Asia Pacific (Jakarta) Region. Amazon Neptune Database is a fast, reliable, and fully managed graph database as a service that makes it easy to build and run applications work with highly connected datasets. You can build applications using Apache TinkerPop Gremlin or openCypher on the Property Graph model, or using the SPARQL query language on W3C Resource Description Framework (RDF). Neptune also offers enterprise features such as high availability, automated backups, and network isolation to help customers quickly deploy applications to production. To get started, you can create a new Neptune cluster using the AWS Management Console, AWS CLI, or a quickstart AWS CloudFormation template. For more information on pricing and region availability, refer to the Neptune pricing page and AWS Region Table.

Amazon Neptune Database is now available in the AWS Europe (Spain) Region on engine versions 1.3.3.0 and later. You can now create Neptune clusters using Serverless, R5, R5d, R6g, R6i, X2iedn, T3, and T4 instance types in the AWS Europe (Spain) Region. Amazon Neptune Database is a fast, reliable, and fully managed graph database as a service that makes it easy to build and run applications work with highly connected datasets. You can build applications using Apache TinkerPop Gremlin or openCypher on the Property Graph model, or using the SPARQL query language on W3C Resource Description Framework (RDF). Neptune also offers enterprise features such as high availability, automated backups, and network isolation to help customers quickly deploy applications to production. To get started, you can create a new Neptune cluster using the AWS Management Console, AWS CLI, or a quickstart AWS CloudFormation template. For more information on pricing and region availability, refer to the Neptune pricing page and AWS Region Table.  

Amazon Bedrock Knowledge Bases announces support for cross-region inference, an optional feature that enables developers to seamlessly manage traffic bursts by utilizing compute across different AWS Regions. By using cross-region inference, Amazon Bedrock Knowledge Base customers using RetrieveAndGenerate API will be able to get higher throughput limits and enhanced resilience during periods of peak demand. By opting in, developers no longer have to spend time and effort predicting demand fluctuations. Instead, cross-region inference dynamically routes traffic across multiple regions, ensuring optimal availability for each request and smoother performance during high-usage periods. To use cross-region inference, customers need to specify an inference profile as the “modelARN“ in the request of RetrieveAndGenerate API. There's no additional routing cost for using cross-region inference and you will be charged based on the region you made the request in (source region). Please find the list of supported models and pre-defined regions here. To learn more about the feature and how to get started, refer to the Amazon Bedrock documentation or this blog.

Amazon Redshift Serverless now allows you to set a higher data warehouse base capacity configuration up to 1024 Redshift Processing Units (RPUs). Amazon Redshift Serverless measures data warehouse capacity in RPUs, and you pay only for the duration of workloads run in RPU-hours on a per-second basis. Previously, the highest base capacity was 512 RPUs. With the new higher base capacity of 1024 RPUs, you now have even more flexibility to support workloads of large complexity, processing terabytes or petabytes in size to accelerate data loading and querying based on your price performance requirements. You now have a base capacity range from 8 to 1024 RPUs. The large base capacity of Amazon Redshift Serverless can improve performance for your workloads serving use cases such as complex and long queries, large numbers of columns, queries with joins and aggregations requiring high memory, data lake queries scanning large amounts of data, and ingesting large datasets into the data warehouse. The support for 1024 RPUs higher base capacity configuration is now available for Amazon Redshift Serverless through the Amazon Redshift Console, API and CLI, and is available in the AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon). To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.  

New minor versions of Microsoft SQL Server are now available on Amazon RDS for SQL Server, providing performance enhancements and security fixes. Amazon RDS for SQL Server now supports these latest minor versions of SQL Server 2019 and 2022 across the Express, Web, Standard, and Enterprise editions. We encourage you to upgrade your Amazon RDS for SQL Server database instances at your convenience. You can upgrade with just a few clicks in the Amazon RDS Management Console or by using the AWS CLI. Learn more about upgrading your database instances from the Amazon RDS User Guide. The new minor versions include: SQL Server 2019 CU28 - 15.0.4385.2 SQL Server 2022 CU14 - 16.0.4135.4 These minor versions are available in all AWS commercial regions where Amazon RDS for SQL Server databases are available, including the AWS GovCloud (US) Regions. Amazon RDS for SQL Server makes it simple to set up, operate, and scale SQL Server deployments in the cloud. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.

Amazon Cognito has expanded multi-factor authentication (MFA) functionality to include email as an additional factor. You now have a choice of delivering one-time passwords (OTP) using email, in addition to the preexisting support for text messages (SMS) and time-based one-time passwords (TOTP). You can enable email MFA either as part of the sign in process or as a challenge for adaptive authentication. With Amazon Cognito, it's easier to integrate authentication, authorization, and user management into your web and mobile apps. Amazon Cognito provides authentication for applications with millions of users and supports sign-in with social identity providers such as Apple, Facebook, Google, and Amazon, and enterprise identity providers via standards such as SAML 2.0 and OpenID Connect. This new feature is now available as part of Cognito advanced security features in all AWS Regions, except AWS GovCloud (US) Regions. To get started, see the following resources: Adding MFA to a user pool Amazon Cognito advanced security features pricing

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.  

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.  

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.

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.  

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.

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.  

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.

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.  

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.

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.

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.