Nov 2018

Today we are announcing the availability of License Included (LI) Amazon Machine Images (AMIs) for Windows Server, version 1809, providing customers with an easy and flexible way to get up and running with the latest versions of Windows Server Semi-Annual Channel release. This version of Windows Server includes improved support for containers and Kubernetes, and is best suited for use cases including building modern container and micro-services based Windows applications.

By running Windows Server, versions 1809 on Amazon EC2, customers can combine the scale, performance, and elasticity of AWS with all the new capabilities in this latest Semi-Annual Channel release of Windows Server.

Windows Server, version 1809 License Included (LI) AMIs are available in all public AWS regions, and can be launched directly from the Amazon EC2 console. Instances running Windows Server, version 1809 AMIs are billed under the standard Windows pricing. To learn more and get started with Windows Server on EC2, check out the User Guide for Windows Instances.

Application Load Balancers now support invoking Lambda functions to serve HTTP(S) requests. This enables users to access serverless applications from any HTTP client, including web browsers. With the Application Load Balancers' support for content-based routing rules, you can also route requests to different Lambda functions based on the request content. Prior to this launch, you could only use EC2 instances, containers, and on-premises servers, as targets for Application Load Balancers, and you needed other proxy solutions to invoke Lambda functions over HTTP(S). You can now use an Application Load Balancer as a common HTTP endpoint to simplify operations and monitoring for applications that use servers and serverless computing.

You can register Lambda functions as targets for an Application Load Balancer using the Elastic Load Balancing Console, AWS SDK, or AWS Command Line Interface (CLI). You can also configure an Application Load Balancer as a trigger for a Lambda functions from the AWS Lambda console in few clicks.

Usual AWS Lambda and Application Load Balancer charges apply. Please visit the Application Load Balancer pricing page for more information.

Support for Lambda invocation via Application Load Balancer is available for existing and new Application Load Balancers in US East (N. Virginia), US East (Ohio), US West (Northern California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada ( Central), EU (Frankfurt), EU (Ireland), EU (London), EU (Paris), South America (São Paulo), and GovCloud (US-West) AWS Regions.

To learn more, please refer to the demo, the blog, and the Application Load Balancer documentation.

AWS Step Functions is now integrated with eight additional AWS services, making workflows faster to build, simpler to secure, and easier to monitor.

You can now assemble and deploy new serverless architectures using nested applications supported by the AWS Serverless Application Model (SAM) using the AWS Serverless Application Repository. Nested applications are loosely-coupled components of a serverless architecture.

You can now use the AWS Well-Architected Tool to review your workloads against the latest AWS architectural best practices, and get guidance on how to improve your cloud architectures. The tool is based on the AWS Well-Architected Framework, which was developed to help you build secure, high-performing, resilient, and efficient application architectures.

You can now develop your AWS Lambda function code using Ruby. AWS Lambda is a compute service that runs your code in response to events, and automatically manages the compute resources for you. 

We are announcing Lambda Runtime API and Lambda Layers, two new AWS Lambda features that enable developers to build custom runtimes, and share and manage common code between functions. 

The AWS Toolkit for PyCharm is a new, open source plug-in that makes it easier to create, step-through debug, and deploy Python applications on AWS. Before today, developers who preferred PyCharm had to take multiple steps to configure their IDEs for developing and debugging applications. Now, you can get started faster and be more productive when developing on AWS.

The AWS Toolkit for PyCharm provides an integrated experience for developing serverless applications in Python. The toolkit helps you get started fast with built-in project templates that leverage the AWS Serverless Application Model (AWS SAM) to define and configure resources. The toolkit also includes an integrated experience for step-through debugging of serverless applications with the AWS SAM CLI and makes it easy to deploy your applications from the IDE. Check out our blog post to learn more about developing serverless applications in PyCharm.

The AWS Toolkits for Visual Studio Code (Developer Preview) and IntelliJ (Developer Preview) are still in active development and will include similar features when they become generally available.

We welcome feedback and pull requests for any of the toolkits on GitHub. The source code for The AWS Toolkits for PyCharm and IntelliJ is here and for Visual Studio Code is here.

The AWS Toolkit for PyCharm is available for download from the JetBrains Plug-in Repository.

The AWS Well-Architected Framework helps cloud architects build secure, high-performing, resilient, and efficient infrastructure for their applications. It provides a consistent approach for Amazon Web Services (AWS) customers and AWS Partner Network (APN) Partners to evaluate architectures and implement designs that will scale over time.

Today we announced Amazon Managed Streaming for Apache Kafka (Amazon MSK) in public preview. Amazon MSK is a fully managed, highly available, and secure service that makes it easy for developers and DevOps managers to run applications on Apache Kafka in the AWS Cloud without needing Apache Kafka infrastructure management expertise. Amazon MSK operates highly available Apache Kafka clusters, provides security features out of the box, is fully compatible with open-source versions of Apache Kafka allowing existing applications to migrate without code changes, and has built-in AWS integrations that accelerate application development.

AWS Marketplace, a curated digital catalog, announced that you can now find and procure hundreds of machine learning algorithms and model packages in AWS Marketplace and deploy them directly on Amazon SageMaker. You can choose from free and paid algorithms and models that span across a broad range of popular categories such as computer vision, natural language processing, speech recognition, text, data, voice, image, video analysis, predictive analysis, and more.

You can now hibernate your Amazon EC2 instances backed by Amazon EBS and resume them at a later time. Applications can pick up exactly where they left off instead of rebuilding the memory footprint all over again. For example, applications that rely on caches and other memory-centric components can take tens of minutes to preload or warm up. These factors impose a delay and can force you to over-provision in case you need incremental capacity very quickly. Using hibernate, you can maintain a fleet of pre-warmed instances with memory footprint that can get to a productive state faster. You can do this without modifying your existing applications. Hibernate is just like closing and opening your laptop lid, with your application starting up right where it left off.

Upon hibernation, your instance’s EBS root volume and any other attached EBS data volumes are persisted between sessions. Additionally, data from memory (RAM) is also saved to your EBS root volume. Upon resume, your EBS root device is restored from its prior state, including the RAM content. Previously attached data volumes are reattached and the instance retains its instance ID. While the instances are in hibernation, you pay only for the EBS volumes and Elastic IP addresses attached to it.

This feature is available for On-Demand and Reserved Instances running on freshly launched M3, M4, M5, C3, C4, C5, R3, R4, and R5 instances running Amazon Linux 1. The AMI snapshot used to launch the instance must be encrypted. This ensures protection of sensitive contents in memory (RAM) as they get copied to the root volume.

You can enable hibernation for your EBS-backed instances at launch. You can then hibernate and resume your EBS-backed EC2 instances through the AWS Management Console, or though the AWS SDK and CLI using the existing stop-instances and start-instances commands.

EC2 Instance Hibernation is now available in the US East (N. Virginia, Ohio), US West (N. California, Oregon), Canada (Central), South America (Sao Paulo), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), and EU (Frankfurt, London, Ireland, Paris) Regions.

To learn more about hibernation, visit this blog. For information about enabling hibernation for your EC2 instances, visit our FAQs or technical documentation.  

Amazon FSx for Windows File Server provides a fully managed native Microsoft Windows file system so you can easily move your Windows-based applications that require file storage to AWS.

Amazon FSx for Lustre is a fully managed file system that is optimized for compute-intensive workloads, such as high-performance computing and machine learning. You can leverage the scale and performance of FSx for Lustre to process your file-based data sets from Amazon S3 or other durable data stores.

AWS License Manager makes it easier to manage licenses in AWS and on-premises servers from software vendors such as Microsoft, SAP, Oracle, and IBM. AWS License Manager lets administrators create customized licensing rules that emulate the terms of their licensing agreements, and then enforces these rules when an instance of EC2 gets launched. Administrators can use these rules to limit licensing violations, such as using more licenses than an agreement stipulates or reassigning licenses to different servers on a short-term basis. The rules in AWS License Manager enable you to limit a licensing breach by physically stopping the instance from launching or by notifying administrators about the infringement. Administrators gain control and visibility of all their licenses with the AWS License Manager dashboard and reduce the risk of non-compliance, misreporting, and additional costs due to licensing overages.

AWS License Manager integrates with AWS services to simplify the management of licenses across multiple AWS accounts, IT catalogs, and on-premises, through a single AWS account. License administrators can add rules in AWS Service Catalog, which allows them to create and manage catalogs of IT services that are approved for use on all their AWS accounts. Through seamless integration with AWS Systems Manager and AWS Organizations, administrators can manage licenses across all the AWS accounts in an organization and on-premises environments. AWS Marketplace buyers can also use AWS License Manager to track bring your own license (BYOL) software obtained from the Marketplace and keep a consolidated view of all their licenses.

To learn more access the AWS License Manager page.

AWS App Mesh is a service mesh that allows you to easily monitor and control communications across microservices applications.

AWS Security Hub is a new service in Preview that gives you a comprehensive view of your high-priority security alerts and compliance status across AWS accounts.  With Security Hub, you now have a single place that aggregates, organizes, and prioritizes your security alerts, or findings, from multiple AWS services, such as Amazon GuardDuty, Amazon Inspector, and Amazon Macie, as well as from AWS Partner solutions.

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now support distributed training of TensorFlow deep learning models with near-linear scaling efficiency up to 256 GPUs. The AWS Deep Learning AMIs come pre-built with an enhanced version of TensorFlow that is integrated with an optimized version of the Horovod distributed training framework to provide this level of scalability. With this enhancement, you can now train the ResNet50 model with TensorFlow-Horovod in just under 15 minutes.

AWS Inferentia is a machine learning inference chip, custom designed by AWS to deliver high throughput, low latency inference performance at an extremely low cost. AWS Inferentia will support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as well as models that use the ONNX format.  

Amazon Elastic Inference allows you to attach just the right amount of GPU-powered acceleration to any Amazon EC2 and Amazon SageMaker instance to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon. 

Amazon Timestream is a purpose-built time series database service for collecting, storing, and processing time-series data such as server and network logs, sensor data, and industrial telemetry data for IoT and operational applications. Amazon Timestream processes trillions of events per day at one-tenth the cost of relational databases, with up to one thousand times faster query performance than a general-purpose database.

You can now integrate your AWS Fargate, Amazon Elastic Container Service for Kubernetes (EKS), and Amazon Elastic Container Service (ECS) applications with AWS Cloud Map to make it easy for your containerized services to discover and connect with each other. AWS Cloud Map is a cloud resource discovery service. With Cloud Map, you can define custom names for your application resources, and it maintains the updated location of these dynamically changing resources. This increases your application availability because your web service always discovers the most up-to-date locations of its resources.  

AWS Cloud Map is service discovery for all your cloud resources. With Cloud Map, you can define custom names for your application resources, and it maintains the updated location of these dynamically changing resources. This increases your application availability because your web service always discovers the most up-to-date locations of its resources. 

Amazon SageMaker now supports new capabilities for better orchestration, experimentation, and collaboration for machine learning (ML) workflows. AWS Step Functions is now integrated with Amazon SageMaker and AWS Glue, making it easier to build, deploy, monitor, and iterate ML workflows. Using AWS Step Functions, you can now automate ML workflows by connecting multiple Amazon SageMaker jobs in a few minutes, and with less code. We now have a new capability to help you organize, track, and evaluate your ML training experiments with Amazon SageMaker Search, that is available in beta starting today. Lastly, you can now associate GitHUb, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter notebooks. Visit the AWS Step Functions documentation for more details.

Amazon Sagemaker now supports deploying Inference Pipelines so you can pass raw input data and execute pre-processing, predictions, and post-processing on real-time and batch inference requests. SageMaker also supports two new machine learning frameworks: Scikit-learn and Spark ML. This makes it easy to build and deploy feature preprocessing pipelines with a suite of feature transformers available in the new SparkML and scikit-learn framework containers in Amazon SageMaker. These new capabilities also enable you to write SparkML and Scikit-learn code once and reuse it for training and inference which provides consistency in pre-processing steps and easier management of your machine learning processes.

Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications. Based on the same technology used at Amazon.com, Amazon Personalize allows developers to easily build sophisticated personalization capabilities into their applications, such as personalized product and content recommendations, tailored search results, and targeted marketing promotions.

Amazon DynamoDB on-demand is a flexible new capacity mode for DynamoDB capable of serving thousands of requests per second without capacity planning. DynamoDB on-demand offers simple pay-per-request pricing for read and write requests so that you only pay for what you use, making it easy to balance costs and performance.

Amazon RDS on VMware is a service, now in preview, that delivers Amazon RDS managed relational databases in VMware vSphere on-premises data centers. RDS on VMware automates database provisioning, operating system and database patching, backup, point-in-time restore, storage and compute scaling, instance health monitoring, and failover.

Amazon Forecast, a fully managed service that uses machine learning to highly accurate forecasts, is now available in preview. 

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions.

Developers, start your engines. We are excited to introduce the preview of AWS DeepRacer, the fastest way to get rolling with machine learning, literally. Get hands-on with a fully autonomous 1/18th scale race car driven by reinforcement learning, 3D racing simulator, and global racing league. You can pre-order your AWS DeepRacer now on amazon.com.

Amazon Textract is a service that automatically extracts text and data from scanned documents. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.

Amazon QLDB is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log ‎owned by a central trusted authority. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time.

Amazon Managed Blockchain is a fully managed service that makes it easy to create and manage scalable blockchain networks using the popular open source frameworks Hyperledger Fabric and Ethereum. Hyperledger Fabric is available today, Ethereum is coming soon.

Starting today, you can easily export Lightsail instances and volumes to EC2 with a simple, guided experience. With this feature, Lightsail offers an additional way to grow your applications and scale your cloud deployments by utilizing the full selection and configurability of EC2.

Starting today, you can now add tagging to your Lightsail resources, including virtual servers, managed databases, load balancers, block storage, snapshots, and DNS zones. Lightsail tags allow you to easily organize your projects, create cost allocation reports for billing, and enable access control for your resources.

Amazon SageMaker now enables developers and data scientists to quickly and easily develop reinforcement learning models at scale with Amazon SageMaker RL.

AWS IoT Greengrass now supports Amazon SageMaker Neo. Neo enables machine learning models to train once and run anywhere in the cloud and at the edge. Neo automatically optimizes TensorFlow, MXNet, PyTorch, ONNX, and XGBoost models for deployment on ARM, Intel, and Nvidia processors. Optimized models run up to twice as fast and consume less than a tenth of the memory footprint. Neo will also be available as open source code under the Apache Software License soon, enabling hardware vendors to customize it for their processors and devices. Using Neo with AWS IoT Greengrass, you can retrain these models in Amazon SageMaker, and update the optimized models quickly to improve intelligence on these edge devices. You can use a broad range of devices based on the Nvidia Jetson TX2, Arm v7 (Raspberry Pi), or Intel Atom platforms.  

Amazon SageMaker Neo lets customers train models once, and run them anywhere with up to 2X improvement in performance. Applications running on connected devices at the edge are particularly sensitive to performance of machine learning models. They require low latency decisions, and are often deployed across a broad number of different hardware platforms. Amazon SageMaker Neo compiles models for specific hardware platforms, optimizing their performance automatically, allowing them to run at up to twice the performance, without any loss in accuracy. As a result, developers no longer need to spend time hand tuning their trained models for each and every hardware platform (saving time and expense). SageMaker Neo supports hardware platforms from NVIDIA, Intel, Xilinx, Cadence, and Arm, and popular frameworks such as Tensorflow, Apache MXNet, and PyTorch.

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly.

You can now connect to an Amazon EFS file system from EC2 instances in a different AWS account or Amazon Virtual Private Cloud (VPC).

AWS Outposts bring native AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility for a truly consistent and seamless hybrid cloud.

You can now create up to 1,000 file systems in your AWS account. 

S3 Glacier Deep Archive is a new Amazon S3 storage class that provides secure, durable object storage for long-term data retention and digital preservation. S3 Glacier Deep Archive offers the lowest price of storage in AWS, and reliably stores any amount of data. It is the ideal storage class for customers who need to make archival, durable copies of data that rarely, if ever, need to be accessed.

The AWS Container Competency recognizes AWS Partner Network (APN) Technology Partners with a product or solution on AWS that offers support to run containers workloads. The APN Partner’s offering integrates with AWS services in ways that improve customers' ability to run container workloads on AWS.

AWS Marketplace, which lists over 4,500 software listings from over 1,400 software sellers, has announced Private Marketplace. Private Marketplace is a new feature that enables you to create a custom digital catalog of pre-approved products from AWS Marketplace. As an administrator, you can now select products that meet your procurement policies and make them available for your users. You can also further customize Private Marketplace with company branding, such as logo, messaging, and color scheme. All controls for Private Marketplace apply across your entire AWS Organizations, and you can define fine-grained controls using Identity and Access Management. 

AWS Marketplace, a curated digital catalog, has announced that you can now find and buy more than 180 curated and trusted container products in AWS Marketplace and through the Amazon Elastic Container Services (Amazon ECS) console. Container products are offered in popular categories such as high performance computing, security, and developer tools. Once you find the container products you need, you can quickly deploy them on services such as Amazon ECS, Amazon Elastic Container Services for Kubernetes (Amazon EKS), and AWS Fargate by using deployment templates such as task definitions, Helm charts, and AWS CloudFormation templates provided by the software seller. Products are offered with a variety of purchasing options, including free, bring-your-own-license, fixed monthly fees, and pay-as-you-go.

The AWS Developer Tools now offer improved continuous delivery support for the Amazon Container Services. Amazon Elastic Container Service (ECS) and AWS Fargate now support blue/green deployments via AWS CodeDeploy. Additionally, AWS CodePipeline now supports Amazon Elastic Container Registry (ECR) as a source provider. Previously, both of these actions required additional steps. 

Today we announced the general availability of AWS Elemental MediaConnect, a reliable, secure, and flexible transport service for live video. Using MediaConnect, broadcasters and content owners can cost-effectively send high-value live content into the cloud, securely transmit it to partners for distribution, and replicate it to multiple destinations around the globe. Build mission-critical live video transport workflows in a fraction of the time and cost of satellite or fiber, with broadcast-grade monitoring to maintain confidence that your video is delivered. MediaConnect combines reliable video transport, highly secure stream sharing, and real-time network traffic and video monitoring that allow you to focus on your content, not your transport infrastructure. To learn more, please visit https://aws.amazon.com/mediaconnect/.

AWS announces the General Availability of CloudWatch Logs Insights, a fully integrated, interactive, and pay-as-you-go log analytics service for CloudWatch. CloudWatch Logs Insights enables you to explore, analyze, and visualize your logs instantly, allowing you to troubleshoot operational problems with ease. With Logs Insights, you only pay for the queries you run. Logs Insights scales with your log volume and query complexity giving you answers in seconds. In addition, you can publish log-based metrics, create alarms, and correlate logs and metrics together in CloudWatch Dashboards for complete operational visibility. 

Amazon DynamoDB now offers native, server-side support for transactions, simplifying the developer experience of making coordinated, all-or-nothing changes to multiple items both within and across tables. With support for transactions, developers can extend the scale, performance, and enterprise benefits of DynamoDB to a broader set of mission-critical workloads.

Transactions provide atomicity, consistency, isolation, and durability (ACID) in DynamoDB, enabling you to maintain data correctness in your applications more easily. Using transactions, you can support sophisticated workflows and business logic that require adding, updating, or deleting multiple items as a single, all-or-nothing operation. For example, a video game developer can ensure players’ profiles are updated correctly when they exchange items in a game or make in-game purchases.

Support for transactions is available in all standard AWS Regions where DynamoDB is offered. Pricing for transactions is based on the sizes of the items in the transaction.

For more information, see Amazon DynamoDB transactions.

Amazon Aurora Global Database is a new feature in the MySQL-compatible edition of Amazon Aurora, designed for applications with a global footprint. It allows a single Aurora database to span multiple AWS regions, with fast replication to enable low-latency global reads and disaster recovery from region-wide outages.

Amazon Comprehend Medical is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. Using Amazon Comprehend Medical, you can quickly and accurately gather medical information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources, like doctors’ notes, clinical trial reports, and patient health records. Additionally, the service identifies the relationship among the extracted medication and test, treatment and procedure information. For example, the service identifies a particular dosage, strength, or frequency related to a specific medication from unstructured clinical notes.

AWS Ground Station is a fully managed service that lets you control satellite communications, downlink and process satellite data, and scale your satellite operations quickly, easily and cost-effectively without having to worry about building or managing your own ground station infrastructure.

Amazon Translate is a fully managed neural-network based machine translation service that delivers high-quality, real-time, and affordable language translation. Today, we are introducing Custom Terminology, a feature that lets you customize Amazon Translate’s output to use company and domain-specific vocabulary. By uploading and invoking Custom Terminology with translation requests, you can ensure that your brand names, character names, model names, and other unique content get translated exactly the way you define it.  

Today, we are announcing the preview availability of ML Insights for Amazon QuickSight. ML Insights allows customers to discover hidden trends and outliers, identify key business drivers, forecast future results, and summarize their data in easy-to-understand, natural language narratives, without spending countless hours on manual analysis and investigation. With ML-powered anomaly detection, customers can continuously analyze their data across billions of data points to uncover hidden insights that are often buried in aggregates, not visible in plain sight, and are not scalable with manual analysis. With ML-powered forecasting, non-technical business users can now predict their key business metrics with point and click simplicity. Finally, we're announcing auto-narratives, a new feature that automatically delivers insights in plain language, embedded contextually in the dashboard, allowing customers to create a shared understanding of data across the organization, without interpreting complex visualizations. You don't need any ML, excel modeling, or domain expertise to use these features. Read our blog post to learn more about these features.

Amazon QuickSight dashboards can now be embedded in applications. As an application developer, you can now create rich, interactive dashboards in QuickSight and embed them into your application portals and websites, without having to investigate charting libraries, add specialized analytics know-how to their teams, or set up, manage and optimize servers to scale to your growing userbase. 

Amazon Kinesis Data Analytics now supports Java-based stream processing applications, in addition to the previously supported SQL. Amazon Kinesis Data Analytics is the easiest way to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time. Now, you can use your own Java code in Amazon Kinesis Data Analytics to continuously transform and load your data lake, generate metrics to feed real-time gaming leaderboards, apply machine-learning models to data streams from connected devices, and more. 

AWS Key Management Service (KMS) has integrated with AWS CloudHSM so you now have the option to create your own KMS custom key store. Each custom key store is backed by an AWS CloudHSM cluster and enables you to generate, store, and use your KMS keys in hardware security modules (HSMs) that you control. The KMS custom key store helps satisfy compliance obligations that would otherwise require the use of on-premises HSMs and supports AWS services and encryption toolkits that are integrated with KMS.

The AWS Service Delivery Program helps customers identify and choose top APN Partners with a track record of delivering specific AWS services to customers. To receive an AWS Service Delivery designation, APN Partners must undergo a technical validation related to their service delivery expertise.

AWS RoboMaker is a service that makes it easy to develop, simulate, and deploy intelligent robotics applications at scale. RoboMaker extends the most widely used open-source robotics software framework, Robot Operating System (ROS), with connectivity to cloud services. This includes AWS machine learning services, monitoring services, and analytics services that enable a robot to stream data, navigate, communicate, comprehend, and learn. RoboMaker provides a robotics development environment for application development, a robotics simulation service to accelerate application testing, and a robotics fleet management service for remote application deployment, update, and management.

Amazon Web Services (AWS) announces the availability of C5n instances that can utilize up to 100 Gbps of network bandwidth. C5n instances offer significantly higher network performance across all instance sizes, ranging from 25 Gbps of peak bandwidth on smaller instance sizes to 100 Gbps of network bandwidth on the largest instance size. In addition, C5n instances also feature 33% higher memory footprint compared to C5 instances. C5n instances are ideal for applications that can take advantage of improved network throughput and packet rate performance.

Amazon EC2 A1 instances deliver significant cost savings and are ideally suited for scale-out and Arm-based workloads that are supported by the extensive Arm ecosystem. A1 instances are the first EC2 instances powered by AWS Graviton Processors that feature 64-bit Arm Neoverse cores and custom silicon designed by AWS.

Amazon S3 Object Lock is a new S3 feature that blocks object version deletion during a customer-defined retention period so that you can enforce retention policies as an added layer of data protection or for regulatory compliance. You can migrate workloads from existing write-once-read-many (WORM) systems into Amazon S3, and configure S3 Object Lock at the object- and bucket-levels to prevent object version deletions prior to pre-defined Retain Until Dates or Legal Hold Dates. S3 Object Lock protection is maintained regardless of which storage class the object resides in and throughout S3 Lifecycle transitions between storage classes.

AWS IoT Things Graph is a service that provides an easy way for developers to connect devices and web services to build IoT applications. In AWS IoT Things Graph, devices and web services are represented as reusable components called models that hide low level details and expose states, actions, and events of underlying devices and services as APIs. You can use the drag-and-drop interface to visually connect models and define interactions between them to build multi-step automation applications.

Amazon S3 now supports four new features to reduce your storage costs by making it even easier to build archival applications using the Amazon S3 Glacier storage class and by enabling one-click data replication to S3 Glacier in another AWS Region. S3 PUT to Glacier, S3 Cross-Region Replication to Glacier, S3 Restore Notifications, and S3 Restore Speed Upgrade are available using the S3 APIs, AWS Software Development Kits (SDKs), and AWS Management Console for simpler integration with your archival workloads and applications.

You can now qualify your device hardware by validating interoperability with AWS IoT Core, AWS IoT Greengrass, Amazon FreeRTOS, and Amazon Kinesis Video Streams. Open to all AWS Partner Network (APN) Partners to easily qualify their devices. Once devices are qualified, APN Partners can list their hardware in the AWS Partner Device Catalog.

Today we are announcing 2x improvement in peak performance of Provisioned IOPS SSD (io1) Volumes from 32,000 IOPS to 64,000 IOPS and from 500 MB/s to 1,000 MB/s of throughput per volume when attached to Nitro system EC2 instances.

AWS IoT Greengrass allows you to bring local compute, messaging, data caching, sync, and ML inference capabilities to edge devices. Starting today, you can use new features that extend the capabilities of AWS IoT Greengrass including connectors to third-party applications and AWS services, hardware root of trust private key storage, and isolation and permission settings that increase AWS IoT Greengrass Core configuration options.  

The AWS Amplify Console is a continuous deployment and hosting service for modern web applications with serverless backends. Modern web applications include single page app frameworks like React, Angular, and Vue, and static-site generators like Jekyll, Hugo, and Gatsby.

BLE support in Amazon FreeRTOS is now available in beta. You can securely connect Amazon FreeRTOS devices that use BLE to AWS IoT through Android and iOS devices.  

Today, we are excited to announce AWS Global Accelerator, a network layer service that you can deploy in front of your internet applications to improve the availability and performance for your globally-distributed user base. AWS Global Accelerator uses AWS’ vast, highly available and congestion-free global network to direct internet traffic from your users to your applications running in AWS Regions. With AWS Global Accelerator, your users are directed to your application based on geographic location, application health, and routing policies that you can configure. AWS Global Accelerator also allocates static anycast IP addresses that are globally unique for your application and do not change, thus removing the need to update clients as your application scales.

AWS Transit Gateway is a new service that enables customers to connect thousands of Amazon Virtual Private Clouds (VPCs) and their on-premises networks using a single gateway. As you grow the number of workloads in AWS across multiple accounts, you need to scale your networks, control your connectivity policies better and monitor your network effectively. Today, you can connect pairs of Amazon VPCs using peering. However, managing point-to-point connectivity across large number of VPCs, without centrally managing connectivity and routing policies, can be operationally costly and cumbersome. This solution can be hard to manage for hundreds of VPCs.

AWS IoT Events is a new, fully managed IoT service that makes it easy to detect and respond to events from IoT sensors and applications. AWS IoT Events recognizes events across multiple sensors to identify operational issues, such as equipment slowdowns, and triggers alerts such as notifying support teams of an issue. AWS IoT Events offers a managed complex event detection service on the AWS cloud.

AWS IoT Device Tester, a Windows/Linux/Mac test automation tool for connected devices, is now generally available. You can use AWS IoT Device Tester to easily determine if your devices running Amazon FreeRTOS or AWS IoT Greengrass can be authenticated by and interoperate with AWS IoT Services. Also, passing the AWS IoT Device Tester tests is required in order to be listed in the AWS Partner Device Catalog. There are two versions of AWS IoT Device Tester, AWS IoT Device Tester for Amazon FreeRTOS and AWS IoT Device Tester for AWS IoT Greengrass.

AWS Snowball Edge Compute Optimized will be shipping to customers the week of December 10th in the US East (Virginia), US West (Oregon), US West (Northern California), GovCloud (US-West), and EU (Ireland) AWS Regions.  

Dynamic Training is an open-source deep learning project that allows you to reduce model training cost and time by leveraging the cloud's elasticity and scale. The first reference implementation of Dynamic Training is based on Apache MXNet, and is open sourced under Dynamic Training with Apache MXNet.

AWS IoT SiteWise is a new managed service that makes it easy to collect and organize your data from industrial equipment at scale. You can easily monitor equipment across your industrial facilities to identify waste, such as breakdown of equipment and processes, production inefficiencies, and defects in products. 

We’re excited to announce the acceleration of machine learning (ML) at AWS with four customized learning paths, 30+ digital ML courses totaling 45+ hours, and our new AWS Certified Machine Learning – Specialty beta exam. Learners can now dive deep into the same ML curriculum used to train Amazon’s developers and data scientists.

Our new on-demand ML courses, hands-on labs, and documentation we originally developed for Amazon's internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value.

AWS Cloud users also can now validate their ML training and experience with our new AWS Certified Machine Learning – Specialty beta exam. Plus, the on-demand digital training courses within the new ML curriculum are available at no cost.

We offer four new learning paths designed to fit specific ML roles: Business Decision Maker, Developer, Data Scientist, or Data Platform Engineer. We also created a path designed to help prepare learners to take the beta exam for our new ML certification. These paths leverage our new training offerings to help personalize the learning experience.

Learn more about our new ML offerings, and how to register for classes here. You can also learn more about the AWS Certified Machine Learning – Specialty beta exam here. 

Today, Amazon Web Services (AWS) is announcing Firecracker, new virtualization and open source technology that enables service owners to operate secure multi-tenant container-based services by combining the speed, resource efficiency, and performance enabled by containers with the security and isolation offered by traditional VMs. Firecracker implements a virtual machine manager (VMM) based on Linux's Kernel-based Virtual Machine (KVM), and provides a RESTful API to create and manage microVMs with any combination of vCPU and memory to match application requirements. Firecracker is built with minimal device emulation that enables faster startup time, provides a reduced memory footprint for each microVM, and offers a trusted sandboxed environment for each container. 

AWS DataSync is an online data transfer service that makes it easy for you to automate movement of data between on-premises storage and Amazon S3 or Amazon Elastic File System (Amazon EFS). DataSync uses a purpose-built protocol to accelerate and secure data transfer over the Internet or AWS Direct Connect, at speeds up to 10 times faster than open-source tools.

Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables customers to run HPC applications requiring high levels of inter-instance communications, like computational fluid dynamics, weather modeling, and reservoir simulation, at scale on AWS. It uses a custom-built operating system bypass technique to enhance the performance of inter-instance communications, which is critical to scaling HPC applications. With EFA, HPC applications using popular HPC technologies like Message Passing Interface (MPI) can scale to thousands of CPU cores. EFA supports industry-standard libfabric APIs, so applications that use a supported MPI library can be migrated to AWS with little or no modification.

EFA is available as an optional EC2 networking feature that you can enable on C5n.9xl, C5n.18xl, and P3dn.24xl instances. Additional instance types will be supported in the coming months.

Sign up for a preview of EFA capabilities to enhance your HPC applications 

S3 Intelligent-Tiering is a new Amazon S3 storage class designed for customers who want to optimize storage costs automatically when data access patterns change, without performance impact or operational overhead. S3 Intelligent-Tiering is the first cloud object storage class that delivers automatic cost savings by moving data between two access tiers — frequent access and infrequent access — when access patterns change, and is ideal for data with unknown or changing access patterns.

Amazon EFS Infrequent Access (EFS IA) is a new storage class for Amazon EFS that is cost-optimized for files that are accessed less frequently.

S3 Batch Operations is a new feature that makes it simple for customers to manage billions of objects stored in Amazon S3, with a single API request or a few clicks in the S3 Management Console. Now, all AWS customers can make changes to object properties and metadata, and perform other storage management tasks – such as copying objects between buckets, replacing tag sets, modifying access controls, and restoring archived objects from Amazon S3 Glacier – for any number of S3 objects in minutes instead of months.

AWS Transfer for SFTP enables you to easily move your file transfer workloads that use the Secure Shell File Transfer Protocol (SFTP) to AWS without needing to modify your applications or manage any SFTP servers.

Today we are announcing updates to our face detection, analysis, and recognition features, providing customers with improvements in the ability to detect more faces from images, perform higher accuracy face matches, and obtain improved age, gender, and emotion attributes for faces in images. Amazon Rekognition customers can use each of these enhancements starting today, at no additional cost. No Machine Learning experience is required.

Amazon CloudFront announces six new Edge locations, across four continents. In the United States, the new locations are in Chicago, Newark, and Ashburn. Internationally, the new locations are in Munich, Tokyo, and Rio de Janerio. Just over one year ago, we announced our 100th Edge location in Tokyo. The addition of these six new locations today now brings CloudFront's total network to 150 Points of Presence worldwide, across 65 cities and 29 countries.

We also just celebrated CloudFront's ten-year anniverary a few days ago. To read more about CloudFront's tenth anniversary, read our blog which dives into the story of how CloudFront was created in response to an internal challenge from Jeff Bezos and Andy Jassy. Thank you for being a part of our evolutionary journey. Here's to the next ten years!

Starting today, you can enable Origin Failover for your Amazon CloudFront distributions to improve the availability of content delivered to your end users.

You can now use Amazon CloudFront for applications using the WebSocket protocol to provide improved performance and security to your end users.

You can now access data in your Amazon Aurora Serverless cluster directly from your AWS Management console, using the new Query Editor (Beta). It provides an easy way for admins and developers to run SQL queries without having to install and setup an external JDBC/ODBC client. Query results are instantly visible within the console.

Amazon Aurora Serverless is a deployment option that automatically starts, scales, and shuts down an Amazon Aurora database. It offers database capacity without the need to provision, scale, and manage any database servers. Aurora Serverless makes it easy and cost-effective to run applications with intermittent or cyclical usage patterns.

You can now group traces to match a filter expression. In addition, X-Ray will automatically create Amazon CloudWatch metrics for traces that match a particular group. This allows you to view service graphs and create CloudWatch alarms by the group, enhancing your ability to quickly understand and fix application issues.  

The new redesigned Amazon EC2 Spot Console simplifies the Spot capacity deployment experience, and also provides fleet recommendations based on your application requirements. Now you can quickly launch Spot Instances by following the Spot best practices of diversification, and flexibility across instance types and Availability Zones, in three simple steps.

First, you can select the best option that fits your application requirements from a list of pre-configured compute choices. The EC2 Spot console offers compute choices such as Flexible, Load Balancing, Big Data, and Defined Duration Workloads. Next, you can specify your minimum hardware requirements for your application as vCPUs and memory, or as instance types. Finally, you can specify the target compute capacity in vCPUs or instances. The EC2 Spot console will provide the fleet request recommendations based on your input. You can also review the request summary that shows the estimated cost of launching the target capacity per hour and the estimated savings over On-demand prices. You can also edit the fleet request recommendations and customize it based on your requirements.

To get started with the new EC2 Spot console, click “Request Spot Instances” in the EC2 Spot Console. The simplified EC2 Spot Console is now available in all public AWS regions. You can learn more about this feature by reading the documentation page.

Amazon Neptune now allows you to use HTTPS to encrypt data in transit between your graph database clients and the Neptune service endpoints. This enhancement uses the industry-standard Transport Layer Security (TLS) 1.2 protocol to encrypt all data sent to and from connected clients. 

Amazon SageMaker can now publish training metrics to Amazon CloudWatch in real-time. You can then use CloudWatch to query, monitor, and visualize your Amazon SageMaker training jobs.

Amazon SageMaker is now integrated with Apache Airflow for building and managing your machine learning workflows. With this integration, multiple SageMaker operators including model training, hyperparameter tuning, model deployment, and batch transform are now available with Airflow.

Amazon SageMaker has added several enhancements to the built-in TensorFlow and Chainer containers. These enhancements make it easier to run TensorFlow and Chainer scripts, while taking advantage of the capabilities Amazon SageMaker offers, including a library of high-performance algorithms, managed and distributed training with automatic model tuning, one-click deployment, and managed hosting.

You can now get aggregated views of the health and performance of all AWS resources through CloudWatch Automatic Dashboards. This enables you to quickly get started with monitoring, explore account and resource-based view of metrics and alarms, and easily drill-down to understand the root cause of performance issues.

Soon, you will be able to use AWS Snowball Edge Compute Optimized for edge processing use cases that require additional computing power in remote, disconnected or austere environments. The new configuration comes with 52 vCPUs and an optional GPU so you can perform real-time full-motion video analysis and advanced analytics before transferring the data back to Amazon S3.

Starting today, you can use AWS Batch with workloads running on AMD-based r5a and m5a instances.  

You can now launch R5 instance types when using Amazon RDS for Oracle. Amazon EC2 R5 instances are the next generation of the Amazon EC2 Memory Optimized instances.

You can now access your Amazon Aurora Serverless database using a new built-in Data API. This API enables you to easily access Aurora Serverless with web services-based applications including AWS Lambda and AWS AppSync. The Data API Beta is available for the MySQL-compatible edition of Amazon Aurora Serverless in the US East (N. Virginia) Region. There is no additional charge to use the Data API Beta. 

The AWS Management Console now makes it easier to find and access your favorite services and discover learning content that helps you get started with AWS.

You can now use Amazon MQ with applications that are subject to Service Organization Control (SOC) compliance. Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud. 

The AWS ALB ingress controller project is now generally available in version 1.0.0 and supported with Amazon Elastic Container Service for Kubernetes (EKS). This project allows you to use the Elastic Load Balancing Application Load Balancer (ALB) with your Kubernetes cluster managed by Amazon EKS.

AWS Systems Manager Distributor is a new feature that you can use to securely store and distribute software packages, such as software agents, in your accounts. Distributor integrates with existing Systems Manager features to simplify and scale the package distribution, installation, and update process.

AWS Auto Scaling automates capacity management for various AWS services, including Amazon EC2, by tracking capacity utilization. Now, you can use Predictive Scaling in AWS Auto Scaling to scale your Amazon EC2 capacity in advance of traffic changes. Auto Scaling enhanced with Predictive Scaling delivers faster, simpler, and more accurate capacity provisioning, resulting in lower cost and more responsive applications.

Predictive Scaling predicts future traffic based on daily and weekly trends, including regularly-occurring spikes, and provisions the right number of EC2 instances in advance of anticipated changes. Provisioning the capacity just in time for an impending load change makes Auto Scaling faster than ever before. Predictive Scaling’s machine learning algorithms detect changes in daily and weekly patterns, automatically adjusting their forecasts. This removes the need for manual adjustment of Auto Scaling parameters over time, making Auto Scaling simpler to configure and consume. Auto Scaling enhanced with Predictive Scaling delivers faster, simpler, and more accurate capacity provisioning to our customers.

Predictive Scaling can be configured through the AWS Auto Scaling console, AWS Auto Scaling APIs via SDK/CLI, and CloudFormation. To get started, navigate to AWS Auto Scaling page and create a scaling plan for Amazon EC2 resources that includes Predictive Scaling in 3 simple steps. Once enabled, customers can visualize their forecasted traffic and the generated scaling actions within a few seconds.

Predictive Scaling in AWS Auto Scaling is now available in US East (Ohio), US East (N. Virginia), US West (Oregon), EU (Ireland), and Asia Pacific (Singapore). Learn more about Predictive Scaling in this blog.  

AWS Batch now supports multi-node parallel jobs, which enables you to run single jobs that require multiple EC2 instances. Multi-node parallel jobs allow customers with tightly coupled, distributed computing workloads to take advantage of AWS Batch’s fully managed batch computing capabilities, avoiding the complexities of provisioning, managing, monitoring, and scaling their compute clusters, and reducing cost and operational overhead.

You can now defer maintenance of your Amazon Redshift cluster to keep your data warehouse running without interruption during critical business periods. You will now also receive advance notifications from Amazon Redshift prior to any upcoming maintenance on your cluster.

AWS AppSync released today three new features for storing and syncing app data: Pipeline Resolvers, Delta Sync, and support for Aurora Serverless. 

Pipeline Resolvers allows you to break up resolvers (the code that runs in response to a network request) into multiple steps, share code across resolvers, and orchestrate calls requiring multiple data sources. These new capabilities unlock new scenarios for data aggregation and authorization in a GraphQL API. You can mix and match actions from any supported data source such as Amazon DynamoDB, AWS Lambda, or HTTP endpoints.

Delta Sync optimizes the end user experience when the device switches from offline to online, providing automatic network reconnection and synchronization of only changed database items to the client cache. This new SDK feature can be used with standard GraphQL queries against a single data source, or combined with the new Pipeline Resolvers to optimize your backend into a journal of changed events. Delta Sync is available for JavaScript, iOS, and Android client SDKs.

An Aurora Serverless Data Source is now built in, enabling you to access Aurora Serverless using GraphQL. This functionality leverages the new Aurora Serverless Data API to efficiently manage connections and allows customers to leverage a relational database within AppSync without using a Lambda function as the intermediary.

You can read more about these new features in the AppSync Developer Guide.

You can now create alarms on metric math expressions such as +, -, /, *, and mathematical functions such as Sum, Average, Min, Max, and Standard Deviation. This enables you to create thresholds and set automated actions to fix operational issues. 

Amazon Translate is a fully managed neural-network based machine translation service that delivers high-quality, real-time, and affordable language translation. Today, we are announcing that Amazon Translate now supports the following eight new languages: Danish, Dutch, Finnish, Hebrew, Indonesian, Korean, Polish, and Swedish. These languages expand upon the existing 13 languages already available in Amazon Translate. With the new languages and quality improvements across many others, we are adding 280 new language pairs, for a total of 417. For a full list, see the Amazon Translate documentation.

Amazon Elasticsearch Service revamped cluster health monitoring to provide detailed cluster and node-level metrics that help you understand the health of your Elasticsearch domains. With 22 new metrics, including indexing rate, query latency, and HTTP response codes, it is easy to track query and indexing performance, request success rates, and JVM health. Additionally, with node-level metrics, you can quickly see the health of all of the Elasticsearch nodes in your domain and drill down on any potential issues. To complement the new metrics, we have integrated with CloudWatch Dashboards to enable one-click dashboards creation, enabling you to create fully customizable dashboards and easily set up CloudWatch Alarms on your domain and instance metrics. All of the new metrics are available in the updated Amazon Elasticsearch Service console. To learn more, please refer to our documentation. 

Today, Amazon AppStream 2.0 released a new Windows Client. The Windows Client lets you use dual monitors and USB peripherals such as 3D mice with your applications on AppStream 2.0. Dual monitors improve multi-tasking by providing additional screen space, and 3D mice make it easier to use design applications on AppStream 2.0. The Windows Client also supports keyboard shortcuts, such as Alt + Tab, clipboard shortcuts, and function keys.

Administrators can download the AppStream 2.0 Windows Client from https://clients.amazonappstream.com and install it remotely for all users in their organization. They can then configure the client to launch with their SAML 2.0 sign-in portal to allow their users to easily sign in and start using their applications on AppStream 2.0. They can also configure application streaming links to launch in the Windows Client instead of a browser. To learn more about how to deploy and use the Windows Client visit the documentation.

USB peripherals and keyboard shortcuts are supported on all streaming instances, and dual monitors on general purpose, compute optimized, memory optimized, and graphics pro streaming instances. The Windows Client is compatible with Windows 7, 8, 8.1 and 10. AppStream 2.0 offers pay-as-you-go pricing. Please see Amazon AppStream 2.0 Pricing for more information, and try our sample applications.

Amazon Elasticsearch Service now offers self-service updates, giving you the flexibility to control when to update your domains. You can now update your domain with a single click. Previously, new product features and enhancements were deployed on a rolling schedule. With self-service updates, you can adopt new features as they arrive and update your domains at the time of your choosing. To see when an update is available for your domain, you can simply log in to the Amazon Elasticsearch Service console or check with the REST API. 

The multi-account, multi-Region data aggregation capability in AWS Config now supports aggregating the configuration data of AWS resources. Aggregation of resource configuration data complements AWS Config rule compliance data aggregation, which launched earlier this year. With this launch, IT administrators can centrally monitor both configuration and compliance data from multiple accounts and Regions. This helps you reduce the time and overhead needed to gather an enterprise-wide view of your resource inventory and AWS Config rule compliance status. 

AWS Lambda now supports the Kinesis Data Streams (KDS) enhanced fan-out and HTTP/2 data retrieval features for Kinesis event sources. The HTTP/2 data retrieval API improves the data delivery speed between data producers and Lambda functions by more than 65%. Enhanced fan-out allows you to process the same KDS stream with multiple Lambda functions in parallel without performance degradation.  

Amazon SageMaker now supports two additional algorithms: Object2Vec and IP Insights.

AWS CodePipeline has reduced the transition time in-between pipeline actions, which means your pipelines will run faster, you'll get build and test results sooner, and you can iterate on features more rapidly. CodePipeline actions are tasks such as building code or deploying to a region. CodePipeline has also raised the default limit on actions per stage to 50 for all action types. Previously, there was a default limit of 20 total actions per stage including limits of 10 for both sequential and parallel actions. The new, higher limits enable you to create more complex pipelines without worrying about limits on the number of actions per stage.

You can now create, manage, and deploy AWS CloudTrail trails across an organization from a single account. Through integration with AWS Organizations, this organization trail enables you to define a uniform event logging strategy for your organization that is applied automatically to each member account in an organization. Users in member accounts are able to see these trails, but they can’t modify them. This helps you uniformly apply and enforce your event logging strategy across the AWS accounts in your organization.

Today we are announcing the general availability of EMR Notebooks, a managed environment, based on Jupyter Notebooks that allows data scientists, analysts, and developers to prepare and visualize data, collaborate with peers, build applications, and perform interactive analysis using EMR clusters.

EMR Notebooks is pre-configured for Spark. It supports Spark magic kernels allowing you to interactively run Spark jobs on EMR clusters written in languages such as PySpark, Spark SQL, Spark R, and Scala. The notebooks come packaged with open-source libraries found in Conda allowing you to import these libraries and use them to manipulate data and visualize computational results in rich graphical plots. Further, each notebook has integrated Spark monitoring capabilities that let you monitor the progress of your jobs and debug code directly from the notebook.

You can create multiple notebooks directly from the console. There is no software or instances to manage, and notebooks spin up instantly, you have a choice of either attaching the notebook to an existing cluster or provision a new cluster directly from the console. You can attach multiple notebooks to a single cluster, detach notebooks and re-attach them to new clusters.

EMR Notebooks saves your notebook files periodically to your Amazon S3 buckets. Saved notebooks can be retrieved from the EMR console or downloaded from your S3 bucket.

To learn more, please visit the EMR Notebooks page.

There is no additional cost for using EMR Notebooks. You only pay for the EMR cluster attached to the notebook. You can find out more about the pricing for your cluster by visiting Amazon EMR pricing.

EMR Notebooks is available in the US East (N.Virgina and Ohio), US West (N.California and Oregon), Canada (Central), EU(Frankfurt, Ireland, and London), and Asia Pacific (Mumbai, Seoul, Singapore, Sydney, and Tokyo) regions.  

Today, Amazon Connect introduced the loop block in contact flows. The loop block lets you repeat segments of a contact flow, which makes it easier for customers to enter difficult inputs like credit cards, account numbers, or social security numbers by giving them opportunities to retry. You can also use the loop block to play an announcement, such as to state that the company is closed due bad weather, for a specified number of times before hanging up. To get started, you can find the loop block in your contact flow editor, for more information see the user documentation.

You can now develop your AWS Lambda functions using Python 3.7 in addition to the already supported 2.7 and 3.6 versions. Python 3.7 is the newest major release of the Python language, and it contains many new features such as support for data classes, customization of access to module attributes, and typing enhancements. 

Amazon Transcribe now includes streaming transcription, a new feature that enables users to receive text transcripts from live audio streams in real time. 

Today, Amazon WorkMail announced that you can use outbound mail rules to trigger Lambda functions. With the power of Lambda, you can use these functions to perform analytics on communication within your organization, log audit trails of messages sent, or even gain insights into the sentiment or other communication patterns of your organization. You can also trigger additional, custom actions according to the email being sent. For example, you can use a Lambda function to notify email recipients about an urgent email through additional communication channels like Amazon Chime or text message.

To get started, first create a Lambda function using the AWS Lambda console. For example, a function that records metrics or triggers additional actions. Then, go to the Amazon WorkMail console to select the Lambda action in your organization's outbound mail flow rules settings. You can optionally specify which sender and receiver the rule should apply to. Now, when an email is sent from your Amazon WorkMail organization, Amazon WorkMail will pass the recipients and subject text to your Lambda function. To get you up and running even more quickly with Lambda for WorkMail, try starting with the WorkMail Hello World function from the Serverless Application Repository. This template function shows you how to publish metrics to CloudWatch whenever an email is sent. Alternatively, you can start a Lambda function from scratch to create custom functionality in a programming language of your choice. Finally, you can even publish your own Lambda functions through the Serverless Application Repository for other Amazon WorkMail customers to use.

Lambda for WorkMail is available today in all AWS Regions where Amazon WorkMail is offered. See Configuring AWS Lambda on WorkMail to try it now. To learn more about Amazon WorkMail, or to start your trial, please visit Amazon WorkMail.

AWS Service Catalog, used by enterprises, system integrators, and managed service providers to organize, govern, and provision cloud resources on AWS, is announcing an update to AWS Service Catalog Connector for ServiceNow. The connector allows you to use your existing AWS Service Catalog configuration, including curated products, portfolios, constraints, and tagging, and expose this to your ServiceNow administrators and users.

Amazon ElastiCache for Redis now supports auto-failover and backup/restore for T2 node types. This launch enables you to build development and test environments based on T2 nodes that have comparable setups as production general-purpose M and compute-optimized R node environments. 

NOTE: This Quick Start is no longer available. See the Quick Start home page for our latest Quick Start catalog. 

This Quick Start automatically deploys software from Informatica and Tableau Software, integrated with AWS services, to provide an end-to-end solution for self-service cloud analytics on the AWS Cloud. 

Amazon SageMaker Automatic Model Tuning now supports warm start of hyperparameter tuning jobs. With warm start, a new hyperparameter tuning job can be created using prior knowledge learned from one or more parent tuning jobs. This enables Automatic Model Tuning to complete in less time, which reduces your tuning costs.

AWS IoT Device Management now provides features for fleet indexing and jobs that provide additional mechanisms to manage your fleet of devices. Fleet indexing makes it easy to query against device metadata or state across your fleet, and jobs allow you to send remote actions to one or many devices at once, control the deployment of your jobs to your devices, and track the current and historical status of your jobs running on each device.

You can now use the sam build command to compile deployment packages for AWS Lambda functions written in Python using the AWS Serverless Application Model (AWS SAM) Command Line Interface (CLI).

Amazon DynamoDB Backup and Restore provides the capability to easily create on-demand and continuous backups of your DynamoDB tables and restore from these backups, if needed. You can back up tables from a few megabytes to hundreds of terabytes of data, with no impact on the performance or availability of your production applications. 

Amazon ElastiCache for Redis now allows you to scale your Redis Cluster environment up to 250 nodes and 250 shards. This increase lets you store over 16.6x more in-memory data and enables configurations with higher write throughput by lifting the previous 15 shard limit. This increase also enables a maximum in-memory data size of 170.6 TB. 

Starting today, you can use AWS Cost Explorer’s improved forecasting engine to access more accurate spend forecasts that are tailored to your needs, regardless of your environment’s complexity. AWS Cost Explorer’s improved forecasting engine segments your historical data based on distinct charge types (e.g., on-demand usage, reserved instance usage, and more) and uses a combination of machine learning and rules-based models to predict spend across all of those charge types individually. By using this multiple time series-based approach, Cost Explorer ensures a higher degree of accuracy, irrespective of the AWS services being used.

Route 53 Resolver makes hybrid cloud easier for enterprise customers by enabling seamless DNS query resolution across your entire hybrid cloud. Create DNS endpoints and conditional forwarding rules to allow resolution of DNS namespaces between your on-premises data center and Amazon Virtual Private Cloud (VPC).

Route 53 Resolver includes the Amazon DNS Server (AmazonProvidedDNS) which is available by default in all Amazon VPCs and responds to DNS queries from AWS resources for public records, Amazon VPC-specific DNS names, and Amazon Route 53 private hosted zones. Customers with workloads leveraging both Amazon VPCs and on-premises resources also need to resolve private DNS records hosted on-premises. Similarly, these on-premises resources may need to resolve names hosted on AWS. These customers can now have bi-directional query resolution regardless of where the names are hosted through the use of Route 53 Resolver rules and endpoints.

Route 53 Resolver rules allow customers to conditionally forward DNS requests from your VPC to an on-premises DNS resolver. Rules are applied directly to your Amazon VPC and can be shared across multiple accounts. These rules will allow you to forward names like “example.com” across AWS Direct Connect and AWS Managed VPN so that it can resolve DNS names that are served from your data center. Conversely, you can create a Route 53 Resolver endpoint that serves as a forwarding target for your on-premises DNS server. This way workloads in your data center can resolve DNS names from services such as Route 53 Private DNS, AWS Private Link, Amazon Elastic File System, AWS Active Directory Service, and more.

To learn more, visit the Route 53 product page for full details and pricing, or see our documentation.

AWS Systems Manager Automation now supports multi-account and multi-Region actions, enabling you to centrally manage your AWS resources. This feature reduces the time and overhead required for enterprise-wide configuration and compliance remediation.

AWS Config launches a new managed rule named cloudformation-stack-drift-detection-check that helps you evaluate whether your AWS CloudFormation stacks' actual configuration differs, or has drifted, from its expected configuration.

You can now develop your Elastic Beanstalk applications using Node.js 10. The latest Node.js 10 comes with increased stability and numerous improvements such as full support for N-API (an API for building native Addons), time-travel debugging functionality via a new Visual Studio Code Extension and support for Inspector protocol. For the complete list of Node.js 10 features, visit the official Node.js 10 release announcement. You can upgrade your existing Elastic Beanstalk Node.js environment using the Elastic Beanstalk console or through the AWS CLI and Elastic Beanstalk API. See Updating Your Elastic Beanstalk Environment's Platform Version for additional details.

AWS Server Migration Service (SMS) now allows you to save on-premises server volumes as an encrypted Amazon Machine Image (AMI). You can use this feature to encrypt all of the replicated data that is stored in your EBS volumes, enabling you to easily migrate workloads that have additional security and compliance requirements.

AWS Server Migration Service (SMS) now offers the ability to resume failed replication jobs, allowing you to take remedial action and resume the replication from the point it failed, as opposed to restarting the job. This adds resiliency to the migration process and allows you to complete large-scale server migrations in an efficient and timely manner.

You can now give your Amazon WorkSpaces users more control over their experience and reduce your helpdesk load with new end user self-service management capabilities. With this release, you can let your Workspaces users reboot, rebuild, change the bundle type, increase the volume size, and change the running mode of their Workspace directly in the Workspaces client without help from IT or help desk teams. This helps users quickly and easily manage their own Workspace to optimize their experience, and reduces the number of help desk requests for IT to handle.

AWS Secrets Manager makes it easier to follow the security best practice of using short-term secrets by rotating secrets safely on a schedule that you determine. For example, you can configure Secrets Manager to rotate a database credential daily, turning a typical, long-term secret in to a short-term secret that is rotated automatically. Today, Secrets Manager introduced a client-side caching library for Java and a client-side caching library of Java Database Connectivity (JDBC) drivers that make it easier to use these secrets in your applications.  

You can now assign tags to AWS IoT Core, AWS IoT Device Management, and AWS IoT Device Defender resources. This enables you to allocate costs and get detailed billing reports across your device fleets as well as define IAM permissions based on these tags.

You can now launch M5 instances types, the next generation of the Amazon EC2 General Purpose compute instances when using Amazon Relational Database Service (RDS) for PostgreSQL. Amazon EC2 M5 instances offer a balance of compute, memory, and networking resources for a broad range of database workloads.

M5 instances are powered by 2.5 GHz Intel Xeon Scalable processors and deliver improved price/performance compared to M4 instances. To meet the demand of intensive database workloads, M5 instances introduce a new larger sized instance, m5.24xlarge, which provides 96 vCPUs, 384 GiB of memory, and 25 Gbps of network bandwidth.

Next-generation Elastic Network Adapter (ENA) and NVM Express (NVMe) technology provide M5 instances with high throughput, low latency interfaces. M5 instances offer up to 25 Gbps of network bandwidth and up to 10 Gbps of dedicated bandwidth for database storage.

Amazon RDS database instances running PostgreSQL versions 9.6.9 (and higher) and 10.4 (and higher) are supported with M5 instance types.

You can easily scale up to these new instance classes by modifying your existing DB instance in the AWS Management Console. Please refer to the Amazon RDS User Guide for more details. Refer to Amazon RDS Pricing for pricing and regional availability.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Starting today, customers can use the Custom Entities API to easily build models to extract custom entities (policy numbers, part codes, serial numbers, etc.) that are tailored to your organization’s need. Customers are now able to analyze the vast amount of unstructured data with the accuracy and entity coverage they need for their unique use cases.

AWS Server Migration Service now supports hourly replication intervals, which enable customers to further minimize the downtime when migrating on-premises servers to Amazon EC2

You can now use Amazon Pinpoint to convert text scripts into life-like speech. Amazon Pinpoint can then deliver these voice messages to your customers over the phone. This feature is a great way to deliver transactional messages, such as one-time passwords, appointment reminders, and order confirmations, to customers. 

Phone calls offer several advantages over other channels: they're highly reliable, very extensible, and just about everybody has a phone number. The addition of voice messages to Pinpoint’s existing email, SMS, and push notifications channels provides you with even more flexibility in delivering the right message at the right place for your users.  

You can create your messages in dozens of languages and customize your messages by including personalized content. You can further customize messages by controlling volume, changing pronunciation, emphasizing key words, and more.

Amazon Pinpoint Voice is available in the US East (N. Virginia) and EU (Ireland) regions. For more information, see the Amazon Pinpoint Voice API reference and the Amazon Pinpoint User Guide.

The maximum payload size for asynchronous AWS Lambda functions is now 256KB. Previously, the limit was 128KB. Lambda supports multiple invocation modes today. Now, you can pass larger payloads when a function is invoked asynchronously, allowing Lambda to operate more seamlessly with services like SNS that already support larger payloads. Read our documentation to learn more about the asynchronous invocation type. 

This Quick Start builds an Internet of Things (IoT) Camera Connector environment and serverless architecture on the Amazon Web Services (AWS) Cloud in about 5 minutes.

Amazon Cloud Directory is now available in the AWS GovCloud (US-West) Region, an isolated region designed to address specific regulatory and compliance requirements of US Government agencies, as well as contractors, educational institutions, and other US customers that run sensitive workloads in the cloud.  

API publishers can now deploy the Amazon API Gateway Serverless Developer Portal directly from the AWS Serverless Application Repository, without having to download or install anything. The API Gateway Serverless Developer Portal enables API publishers to connect with API subscribers.

Amazon Aurora Serverless (MySQL compatible edition) is now available in an additional 9 AWS Regions. With the additions of Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), US West (N. California), Canada (Central), EU (Frankfurt), EU (London), and EU (Paris), you can now choose the Serverless configuration of Amazon Aurora in 14 geographic Regions.

The Serverless Application Repository now supports applications with the following additional resources: Application Auto Scaling, Amazon Athena, AWS AppSync, AWS Certificate Manager, Amazon CloudFront, AWS CodeBuild, AWS CodePipeline, AWS Glue, AWS IAM, Amazon SNS, Amazon SQS, AWS Systems Manager, and AWS StepFunctions. These new resources make it easier for teams and organizations to publish, store, and distribute a wider and more powerful set of serverless applications.

Amazon GuardDuty has added three new threat detections. Two of the detections help detect suspicious Tor Network-related activity, and the third helps identify cryptocurrency mining-related activity.

You can now retain Amazon RDS automated backups (system snapshots and transaction logs) when you delete a database instance. This allows you to restore a deleted database instance to a specified point in time within the backup retention period even after it has been deleted, protecting you against accidental deletion of data. 

Amazon EC2 Fleet now supports a new request type, Instant, that lets you provision capacity synchronously across instance types, Availability Zones (AZs) and purchase models. The CreateFleet API will return the instances launched in the API response with the “Instant” request type. EC2 Fleet now offers 3 request types: instant, request, and maintain, that can be used to indicate whether EC2 Fleet should return instance information synchronously, launch instances asynchronously until the fleet reaches your desired capacity, or attempt to maintain your desired capacity.

When you set your fleet type to “Instant” you can rely on a synchronous API response, providing instance details including instance id, instance type and subnet rather than describing a fleet ID. When using Instant type fleets EC2 Fleet will not attempt to replenish instances if interrupted. Following the create-fleet synchronous response EC2 Fleet will take no further action enabling you to control if and when instances are launched.

Amazon EC2 Fleet is a feature that simplifies the provisioning of large amounts of EC2 capacity, including provisioning across different EC2 instance types, Availability Zones and across On-Demand, Reserved and Spot purchasing models. With a single API call, you can provision capacity that delivers the best mix of instance types and purchasing models to achieve your desired performance and cost. To learn more about EC2 Fleet, read the FAQs.

AWS Systems Manager now integrates with Windows PowerShell DSC to manage Windows instance configuration through State Manager. With native PowerShell DSC support in AWS Systems Manager you can use existing custom workflows to manage and report compliance of Windows instances at scale using tags. This helps you meet your compliance requirements and eliminates the need to create and maintain new configuration documents.

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for you to add a speech-to-text capability to your applications. Amazon Transcribe now supports transcription of audio in the following new languages: British English, Australian English, and Canadian French. These languages expand upon the existing two languages already available in Amazon Transcribe: US English and US Spanish.

AWS Secrets Manager makes it easier to follow the security best practice of using short-term secrets by rotating secrets safely on a schedule that you determine. For example, you can configure Secrets Manager to rotate a database credential daily, turning a typical, long-term secret in to a short-term secret that is rotated automatically. Now, Secrets Manager makes it easier for you to manage and rotate secrets by introducing three enhancements to the Secrets Manager console. First, you can tag secrets from the console. Second, you can give the rotation Lambda function a custom name. Finally, you can use existing Lambda functions to rotate new secrets.

AWS has updated Video on Demand on AWS, a solution that automatically provisions the services necessary to build a scalable, distributed video-on-demand workflow on AWS. The solution now leverages AWS Elemental MediaConvert job templates to define the solution’s encoding options. You can customize the solution to work with any valid MediaConvert template.

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with an optimized build of TensorFlow 1.12 and support for MMS 1.0, a Model Server for Apache MXNet that provides a flexible and easy way to serve deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX). For Amazon EC2 C5 instances, Deep Learning AMIs deploy compute-optimized TensorFlow built with Intel Advanced Vector Extensions (AVX instruction sets) to speed up the performance of vector and floating point operations. The AMIs also come pre-configured to leverage Intel Math Kernel Library for Deep Neural Networks (MKL-DNN). Training a ResNet-50 benchmark with the synthetic ImageNet dataset using our optimized build of TensorFlow 1.12 on a c5.18xlarge instance type was 13x faster than training using the stock TensorFlow 1.12 binaries.

Amazon Comprehend for advanced text analytics now includes Custom Classification.

You can now tag your Amazon Elastic Container Service (Amazon ECS) and AWS Fargate resources such as services, task definitions, tasks, clusters, and container instances . This will enable you to better allocate cost, improve visibility into your workloads, easily search and identify your containerized applications, implement programmatic infrastructure management actions, and define fine-grained resource-level permissions.

Many ECS customers are managing thousands of resources such as task definitions, services, tasks, and clusters, spread across different teams, products, business units, and environments. Without tagging, it was difficult to manage the entire infrastructure and the underlying cost in a systematic manner. Now, you can tag your ECS resources at creation or at runtime using the ECS APIs and Console. Customers using the AWS Fargate launch-type can now easily allocate the cost of their ECS Tasks using tags. For customers using the EC2 launch-type, ECS now provides detailed ECS Tasks usage data in the Cost & Usage Report, which customers can use to allocate the cost of the underlying EC2 instances.

To take advantage of tagging, you will need to opt-in to the new Amazon Resource Names (ARN) and resource identifiers (ID) format for tasks, container instances, and services. These new ARNs and IDs are necessary to introduce the new tagging functionality and all its underlying benefits.

Additional information about the new ARN and ID formats can be found in the FAQ and documentation. You can also read our post to learn more about moving to new ARN and ID. If you have questions, please reach out to your account team or contact AWS Support on the community forums or through AWS Premium Support. To opt in, visit the ECS page of your AWS console. Resource tagging is currently not available in the GovCloud (US-East) region.  

Today we are announcing the general availability of the new AWS Cost Explorer console. With brand new summary dashboards, automated identification of spend and usage trends, and a simplified user experience, the new Cost Explorer console is designed to help customers of all sizes, and levels of expertise, perform cost management activities.

Today, we are announcing support for Domain Name System (DNS) resolution over Inter-Region Virtual Private Cloud (VPC) Peering. You can now resolve DNS hostnames to private IP addresses when queried from a peered VPC in another AWS Region. Using DNS names to access resources makes application development and management simpler and less error-prone. By using DNS resolution over Inter-Region VPC Peering, resources in peered VPCs in another AWS Region are always accessed over the Inter-Region VPC Peering connection.

Inter-Region VPC Peering allows VPC resources running in different AWS Regions, such as EC2 instances, RDS databases, and Lambda functions, to communicate with each other using private IP addresses, without requiring gateways, VPN connections, or separate network appliances. Built on the same scalable, redundant, and highly available technology that powers VPCs today, Inter-Region VPC Peering encrypts inter-region traffic with no single point of failure or bandwidth bottleneck. Traffic using Inter-Region VPC Peering always stays on the AWS global network and never traverses the public internet. This approach reduces the attack surface to threat vectors, such as common exploits and DDoS attacks.

You can enable DNS resolution for Inter-Region VPC Peering using the AWS Management Console or the AWS Command Line Interface (CLI). For more information, see DNS Resolution Support for Peering Connections.

For more information on VPC Peering, see our documentation.

Amazon Athena is a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Starting today, you can configure your AWS Cost & Usage Report to automatically integrate with Amazon Athena, making it easier and more cost effective than ever to directly access and query your cost and usage information.

You can now use new task definition conventions to easily inject sensitive information stored in AWS Systems Manager Parameter Store into containers managed by Amazon Elastic Container Service (Amazon ECS).

As customers build applications, they need to reference sensitive information such as database credentials, tokens, configuration variables or SSH keys. Previously, customers had to directly reference this sensitive information in the task definition or manage your own run-time secrets with custom solutions to decouple secrets from core application logic stored in container images.

Now, you have new task definition conventions for exposing sensitive information stored in AWS Systems Manager Parameter Store to container instances. You can still set, get, update and delete secrets via existing AWS Systems Manager Parameter Store APIs. However, task definitions can now be used to designate which of those secrets should be exposed to what containers.

To learn more about how to integrate ECS with Parameter Store, read our documentation.
 

Amazon Simple Notification Service (Amazon SNS) now provides server-side encryption (SSE) of topics for additional protection of sensitive data. This feature is integrated with AWS Key Management Service (AWS KMS), which allows you to centrally manage keys that protect Amazon SNS topics along with keys that protect your other AWS resources.

AWS Systems Manager, which provides information about your instances and the software installed on them, now supports a multi-account, multi-Region view. With this enhancement, you can simplify your workflow by centrally viewing, storing, and exporting inventory data across your accounts from a single console.

Amazon DynamoDB is a fully managed, nonrelational database that delivers reliable performance at any scale. Because of the flexible DynamoDB data model, enterprise-ready features, and industry-leading service level agreement, customers are increasingly moving to DynamoDB sensitive workloads such as financial and healthcare data, whose compliance regulations mandate data encryption.

NOTE: This Quick Start is no longer available. See https://aws.amazon.com/quickstart/ for our latest Quick Start catalog.

iOS users can now monitor and access their AWS resources through the new AWS Console mobile application.

The Console mobile application lets customers view and manage a select set of resources to support incident response while on-the-go. Login process leverages biometrics authentication (on supported devices), making access to AWS resources a simple and quick.

It’s now faster and easier to bring your Windows 7 and Windows 10 desktop operating systems to Amazon WorkSpaces and save money with bring your own license (BYOL) automation. With this release, you can bring your existing Windows desktop licenses to WorkSpaces with a simple few clicks and move hundreds or even thousands of Windows desktops to WorkSpaces faster. This helps you recognize significant cost savings because you get a discount of $4 per WorkSpace per month (a savings of up to 16%) when you bring your existing Windows licenses to WorkSpaces.

Now, AWS Identity and Access Management (IAM) makes it easier for you to manage your IAM resources by enabling you to add tags to your IAM users and roles (also known as IAM principals). These tags allow you to add customizable key-value pairs to resources, providing better resource management for AWS services that support tagging of AWS resources. 

With the introduction of Amazon S3 Block Public Access, securing your S3 data has never been easier. With a few clicks in the S3 management console, you can apply S3 Block Public Access to every bucket in your account – both existing and any new buckets created in the future – and make sure that there is no public access to any object. By default, new S3 bucket settings do not allow public access, but customers can modify these settings to grant public access using policies or object-level permissions. The Amazon S3 Block Public Access settings override S3 permissions that allow public access, making it easy for the account administrator to set up a centralized control to prevent variation in security configuration regardless of how an object is added or a bucket is created. These settings are auditable, providing a further layer of control, using AWS Trusted Advisor bucket permission checks, AWS CloudTrail logs, Amazon Macie and Amazon CloudWatch.

You can now quickly resize your Amazon Redshift cluster in minutes by adding nodes to get better performance and more storage for demanding workloads or by removing nodes to save cost.

We’re excited to announce that AWS Training and Certification has teamed up with Coursera to expand our training offerings with a new course, AWS Fundamentals: Going Cloud-Native. This foundational-level course –developed by AWS and available exclusively on Coursera – introduces learners to AWS’ core services and infrastructure.

Recommended for learners who are new to cloud computing, this course uses presentations, demos, and learning assessments to teach you how to deploy a cloud-native application using AWS. It may be used as a complementary training for learners who are preparing for the AWS Certified Cloud Practitioner exam.

Learners can enroll in this course for free, or gain unlimited access to course materials, graded materials, and a shareable course certificate for $49 USD.

Register today

AWS Direct Connect is announcing the launch of logical redundancy over a single virtual interface for newly-created 1Gbps or 10Gbps dedicated connections at the Equinix SV5, San Jose, CA location.

Prior to this launch, you could create one IPv4 and IPv6 Border Gateway Protocol (BGP) peering each per virtual interface. In the event that BGP peering went down, you would have experienced downtime. With the launch of logical redundancy support, you can establish two IPv4 and IPv6 BGP peerings on two different AWS devices over a single connection. Logical redundancy can reduce downtime when a BGP peering session goes down due to a device failure or maintenance activity.

AWS Direct Connect will enable AWS Direct Connect partners to provide hosted connections at the Equinix SV5, San Jose, CA location. When enabled, hosted connections will support logical redundancy.

To learn more about the logical redundancy support, please refer to the FAQ page for this feature.

Starting today, AWS IoT Analytics is available in the Europe (Frankfurt) AWS region. This is in addition to the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) AWS regions. AWS IoT Analytics gives you the ability to cleanse, process, enrich, store, and analyze IoT data at scale. On August 23, 2018, we launched feature enhancements including the ability to containerize your custom analysis code, automate its execution on a set schedule, and analyze only the incremental data when you need it.

This Quick Start deploys BIG-IP Virtual Edition (VE), an application delivery and security services platform from F5 Networks, on the Amazon Web Services (AWS) Cloud in about 30 minutes.  

Amazon EC2 Auto Scaling now lets you provision and automatically scale instances across purchase options, Availability Zones (AZ), and instance families in a single Auto Scaling group (ASG), to optimize scale, performance, and cost. Now you can include Spot Instances with On-Demand and RIs in a single ASG, to save up to 90% on compute.

Earlier this year, we announced EC2 Fleet, an API to provision capacity across purchase options, AZs and instance types. Now this EC2 Fleet functionality is available via EC2 Auto Scaling. Powered by EC2 Fleet, you can now create an ASG by defining which EC2 instance types work for you and how much of the desired capacity should be filled using On-Demand, RI and Spot purchase options. EC2 Auto Scaling continues to optimize and maintain the mix as and when the ASG scales out or scales back, simplifying capacity provisioning and cost optimization with automatic scaling across instances and purchase options. EC2 Auto Scaling also continues to provide lifecycle hooks, instance health checks and scheduled scaling to automate capacity management.

When setting up an ASG, now you can specify what percentage of your ASG capacity should be fulfilled by On-Demand instances or RIs, and what percentage with Spot Instances. You can also indicate instances types or instances with specific amount of RAM or vCPU in the ASG configurations. EC2 Auto Scaling then provisions the lowest price combination of instances to meet the desired capacity based on these preferences.

This capability is now available in all public Regions. To learn more about provisioning and automatically scale instances across purchase options, Availability Zones (AZ), and instance families with EC2 Auto Scaling, visit this blog.  

You can now add personalized ads to multi-period DASH live streams using AWS Elemental MediaTailor. Support for multi-period DASH can be combined with multi-period DASH endpoint support in AWS Elemental MediaPackage to enable personalized dynamic ad insertion for a broader range of playback devices. To learn more, please visit the MediaTailor documentation pages.

Amazon ElastiCache for Memcached now supports Memcached version 1.5. This open source Memcached version offers significant improvements including better connection management, improved item size management for items above 1MB, and memory overhead improvements by reducing per-item memory requirements. 

Amazon Corretto is a no-cost, multiplatform, production-ready distribution of the Open Java Development Kit (OpenJDK).

The AWS Storage Gateway virtual tape library, or Tape Gateway, now supports IBM Spectrum Protect (Tivoli Storage Manager) version 7.1.9 running on Microsoft Windows, and Bacula Enterprise Edition version 10. Additionally, Tape Gateway is compatible with new versions of common backup applications, including Veritas NetBackup 8, Veritas Backup Exec 20, Microsoft System Center Data Protection Manager (DPM) 2016, and Quest NetVault Backup 11 and 12.

Starting today, the Amplify framework adds support for Authentication workflows to its iOS and Android SDKs with a simple, declarative programming model. This includes automatic credentials management and refresh routines when leveraging either Amazon Cognito User Pools or Identity Pools, allowing you to seamlessly interact with services such as AWS AppSync, Amazon S3, Amazon Pinpoint and more. The SDK now includes a built in state management and notification system for developers to hook into and perform custom UI flows or screen transitions. It also is aware of the network state, allowing developers to build offline apps and protect against failures when making requests to AWS services.

Along with this release, all iOS and Android documentation has been rewritten and centralized along with the Amplify JavaScript documentation at https://aws-amplify.github.io/. Mobile and web developers now have a central location for JavaScript, iOS, and Android documentation when building apps on AWS.

AWS CloudFormation now allows you to detect if configuration changes were made to your stack resources outside of CloudFormation via the AWS Management Console, CLI, and SDKs. Drift is the difference between the expected configuration values of stack resources defined in CloudFormation templates and the actual configuration values of these resources in the corresponding CloudFormation stacks. This allows you to better manage your CloudFormation stacks and ensure consistency in your resource configurations. For more information on Drift detection, visit the AWS Blog.

Drift detection is available in the following regions: US East (Ohio), US EAST (N. Virginia), US West (N. California), US West (Oregon), Canada (Central), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), EU (Frankfurt), EU (Ireland), EU (London), EU (Paris) and South America (São Paulo).

To learn more about Drift detection and the resource types that support drift detection, visit our documentation page.
 

You can now launch Amazon RDS for Oracle instances running on Oracle 12.2. To create a new Oracle 12.2 database instance with just a few clicks in the AWS Management Console, use the "Launch DB Instance" wizard and select the DB engine version "12.2.0.1.ru-2018-10.rur-2018-10.r1".

NOTE: This Quick Start is no longer available. See https://aws.amazon.com/quickstart for our latest Quick Start catalog.

Starting today, you can access detailed information regarding your budgets’ performance from the recently-introduced budget detail pages within AWS Budgets. Using the new budgets history functionality, you can easily analyze your budget’s performance over the past 12 months by comparing your budgeted amount to your actuals (i.e., your budget variance). This allows you to identify which areas of your business have under- or over-performed based on their budgets, making it easy to adjust your forecasts to improve the accuracy of your budgets or perform further analysis using AWS Cost Explorer. For example, you can track the actual monthly spend for your ‘Game Development Team’ account relative to their monthly budget over the last 12 months and take action based on your findings. Please note that budgets history information can also be accessed programmatically via the AWS Budgets API.

Amazon RDS for Oracle now supports the Oracle October Patch Set Updates (PSU). Oracle PSU contains critical security updates and other important updates. To learn more about the Oracle PSUs supported on Amazon RDS, visit the Amazon RDS patch update documentation.

We’re excited to announce the availability of Lumberyard Beta 1.16. With over 250 improvements, fixes, and features, this release includes:

AWS CodePipeline now makes it easier to perform actions such as deployments, builds, or tests in multiple regions from a single pipeline. Previously, you needed to set-up a pipeline in a region in order for CodePipeline to perform actions in that region. Now, you can add cross-region actions such as additional region deployments to help improve your application's latency and availability.

Amazon RDS for PostgreSQL now supports custom Domain Name Services (DNS) servers which are used for name resolution during outbound network access. This is in addition to the existing support for the Amazon Route 53 domain service in your VPC.

You can configure a custom DNS Server in the DHCP Options Set for the Amazon VPC that contains your RDS instance. For more information, see DHCP Options Sets in the Amazon VPC documentation, and DHCP Options Set in the AWS Directory Service documentation.

You can enable the configured custom DNS server with a new database parameter for your RDS for PostgreSQL databases to communicate with other services within your VPC. This works by using either private or custom DNS names, keeping outbound network traffic within the boundaries of your VPC.

Your RDS for PostgreSQL database instance needs to be in a VPC to be able to use this feature. To move your non-VPC RDS databases to a VPC, follow the instructions in the documentation.

For using custom DNS server for outbound network access for RDS for PostgreSQL, please visit the documentation page.

You can now create custom endpoints for Amazon Aurora databases. This allows you to distribute and load balance workloads across different sets of database instances in your Aurora cluster.

Amazon SageMaker now supports AWS PrivateLink for notebook instances. Earlier this year, we announced support for AWS PrivateLink for API operations and runtime with Amazon SageMaker. With this enhancement, you can use AWS PrivateLink to secure your connection to notebook instances as well.

Your real-time applications can now benefit from Amazon ElastiCache for Redis support of Redis 5.0. Now you can take advantage of new Redis 5.0 features such as Redis Streams, enhanced sorted sets with pop operations, improved HyperLogLog algorithm, and better memory management.

AWS Secrets Manager makes it easier to securely automate the provisioning of your AWS resources by integrating with AWS CloudFormation. You can use this integration to provision secrets, such as database credentials, required to create and access your AWS resources.

AWS Batch now supports EC2 Launch Templates, allowing you to build customized templates for your compute resources, and enabling Batch to scale instances with those requirements. You can specify your EC2 Launch Template to add storage volumes, specify network interfaces, or configure permissions, among other capabilities. EC2 Launch Templates reduce the number of steps required to configure Batch environments by capturing launch parameters within one resource.

AWS is announcing immediate availability of the new AWS GovCloud (US-East) Region. The GovCloud (US-East) Region joins GovCloud (US-West) as the second AWS GovCloud Region in the US, and the 19th AWS Region worldwide. The AWS GovCloud (US-East) Region consists of three Availability Zones and with this launch, the AWS Global Infrastructure now offers a total of 57 Availability Zones worldwide, serving customers in over 190 countries.

Amazon Polly is a service that turns text into lifelike speech. Today, we are excited to announce additional Italian and Spanish voice options.

You can now use Amazon Data Lifecycle Manager (DLM) lifecycle policies as a resource in your AWS CloudFormation templates, stacks, and stacksets.

AWS Batch now supports enhanced Identity and Access Management (IAM)-based fine-grained access controls. IAM-based controls enable administrators to match Portable Operating System Interface (POSIX) controls with their IAM users in AWS Batch. In addition to POSIX support, administrators can write IAM policies that control access to Job Definitions and Job Queues when submitting Jobs to AWS Batch.

AWS Certificate Manager is now a HIPAA Eligible Service and has been added to the AWS Business Associate Addendum (BAA).

We are excited to announce that Amazon Redshift is now available in the AWS GovCloud (US-East) Region.

AWS ParallelCluster is a fully supported and maintained open source cluster management tool that makes it easy for scientists, researchers, and IT administrators to deploy and manage High Performance Computing (HPC) clusters in the AWS cloud. HPC clusters are collections of tightly coupled compute, storage, and networking resources that enable customers to run large scale scientific and engineering workloads. Built as an enhancement to and replacement for the popular open source CfnCluster project, AWS ParallelCluster enables customers to quickly build an HPC cluster on AWS. It automatically sets up the required compute resources and shared file systems and offers a variety of batch scheduler options, including AWS Batch, Sun Grid Engine (SGE), Torque, and Slurm. 

AWS ParallelCluster reduces the operational overhead of cluster management and simplifies running HPC workloads on AWS. AWS ParallelCluster facilitates both quick-start proof of concepts (POCs) and production deployments. AWS ParallelCluster is available at no additional charge, and you pay only for the AWS resources needed to run your applications. AWS ParallelCluster is released via the Python Package Index (PyPI). AWS ParallelCluster’s source code is hosted under the Amazon Web Services repository on GitHub at  https://github.com/aws/aws-parallelcluster

Learn how to launch an HPC cluster using AWS ParallelCluster here

Amazon S3 announces Cross Region Replication for AWS GovCloud (US) Regions. Amazon S3’s Cross Region Replication (CRR) enables automatic and asynchronous replication of new objects between the AWS GovCloud (US) Regions for reduced latency, compliance, security, disaster recovery, and a number of other use cases.

Amazon SageMaker Batch Transform now supports Amazon Virtual Private Cloud (VPC). With VPC support, your model containers and the AWS resources they access, such as the Amazon Simple Storage Service (S3) buckets can be made private within your network and not connected to the internet. This enhancement also enables you to monitor all network traffic in and out of your model containers using VPC Flow Logs.

CloudFormation has added support for the following new resources:

AWS::SecretsManager::ResourcePolicy
Use the AWS::SecretsManager::ResourcePolicy resource to define a resource-based policy and attach it to a secret that's stored in Secrets Manager.

AWS::SecretsManager::RotationSchedule
Use the AWS::SecretsManager::RotationSchedule resource to configure rotation for a secret.

AWS::SecretsManager::Secret
Use the AWS::SecretsManager::Secret resource to create a secret and stores it in Secrets Manager.

AWS::SecretsManager::SecretTargetAttachment
Use the AWS::SecretsManager::SecretTargetAttachmentresource to complete the final link between a Secrets Manager secret and its associated database.

AWS::DLM::LifecyclePolicy
Use the AWS::DLM::LifecyclePolicy resource to create a lifecycle policy for Amazon Data Lifecycle Manager.

Secrets Manager Dynamic reference:
Use the secretsmanager dynamic reference to retrieve entire secrets or secret values that are stored in AWS Secrets Manager for use in your templates.

CloudFormation has updated support of following resource types:

AWS::ApiGateway::Deployment
In the StageDescription property type, use the TracingEnabled property to specify whether active tracing with X-ray is enabled for this stage.

AWS::ApiGateway::Stage
Use the TracingEnabled property to specify whether active tracing with X-ray is enabled for this stage.

AWS::CloudWatch::Alarm
Use the DatapointsToAlarm property to specify the number of datapoints that must be breaching to trigger the alarm. This is used only if you are setting an "M out of N" alarm. In that case, this value is the M.

AWS::EC2::SecurityGroupIngress
Use the SourcePrefixListId property to specify the AWS service prefix of an Amazon VPC endpoint.

AWS::IAM::Role
Use the PermissionsBoundary property to specify the policy that is used to set the permissions boundary for the role.

AWS::IAM::User
Use the PermissionsBoundary property to specify the policy that is used to set the permissions boundary for the user.

AWS::IoT::TopicRule
In the TopicRulePayload property type, use the ErrorActions property to specify the action to take when an error occurs.
In the Action property type:
l Use the IoTAnalytics property to send message data to an AWS IoT Analytics channel.
l Use the StepFunctionsAction property to start execution of a Step Functions state machine.

AWS::KMS::Key
Use the PendingWindowInDays property to specify the waiting period, specified in number of days, after which AWS KMS deletes the customer master key (CMK).

AWS::RDS::DBInstance
Use the EnableCloudwatchLogExports property to specify the list of log types that need to be enabled for exporting to CloudWatch Logs.
Use the EnableIAMDatabaseAuthentication property to enable mapping of AWS Identity and Access Management (IAM) accounts to database accounts.
Use the EnablePerformanceInsights property to enable Performance Insights for the DB instance.
Use the PerformanceInsightsKMSKeyId property to specify the AWS KMS key identifier for encryption of Performance Insights data. The AWS KMS key ID is the Amazon Resource Name (ARN), AWS KMS key identifier, or the AWS KMS key alias for the AWS KMS encryption key.
Use the PerformanceInsightsRetentionPeriod property to specify the amount of time, in days, to retain Performance Insights data.
Use the ProcessorFeatures property to specify the number of CPU cores and the number of threads per core for the DB instance class of the DB instance.
Use the PromotionTier property to specify the order in which an Aurora Replica is promoted to the primary instance after a failure of the existing primary instance.

AWS::RDS::DBCluster
Use the EnableCloudwatchLogExports property to specify the list of log types that need to be enabled for exporting to CloudWatch Logs.
Use the EnableIAMDatabaseAuthentication property to enable mapping of AWS Identity and Access Management (IAM) accounts to database accounts.
Use the BackTrackWindow property to set the target backtrack window, in seconds. To disable backtracking, specify 0. If specified, this property must be set to a number from 0 to 259,200 (72 hours).

AWS::Route53::RecordSet
Use the MultiValueAnswer property to route traffic approximately randomly to multiple resources, such as web servers. Create one multivalue answer record for each resource and specify true for MultiValueAnswer.

AWS::S3::Bucket
Use the RegionalDomainName attribute with the Fn::GetAtt function to return the regional domain name of the specified bucket.

AWS::WorkSpaces::Workspace
Use the Tags property to specify the tags (key-value pairs) that you want to attach to the WorkSpace.
Use the WorkspaceProperties property to specify information about a WorkSpace.

For a complete list of resources please click here

To learn more about AWS CloudFormation, please visit our documentation page.
 

Amazon SageMaker is now available in 5 additional AWS regions in the Americas, Europe, and Asia Pacific. With this announcement, SageMaker is available in the US West (N. California), Canada Central (Montreal), Europe (London), Asia Pacific (Mumbai), and Asia Pacific (Singapore) AWS regions.

Amazon Aurora with PostgreSQL compatibility now supports AWS Identity and Access Management (IAM) to manage database access. Database administrators can associate database users with IAM users and roles. This way, you can manage user access to all AWS resources from a single location, avoiding issues caused by permissions being out of sync on different AWS resources.

Resource tags allow you to add metadata and apply access policies to your Amazon Elastic Block Store (Amazon EBS) resources. Starting today, you can allow resource tags set on your EBS volumes to be automatically copied to any EBS snapshots that are created using Amazon Data Lifecycle Manager (DLM). This allows you to easily set snapshot metadata, such as access policies, to match the parent volume. You may enable this functionality on new or existing lifecycle policies. You may also choose to disable it at a future date.

AWS IoT Core now supports Basic Ingest, a new feature that allows AWS IoT Core customers to securely send large amounts of data to over 10 AWS services such as Kinesis and S3 via AWS IoT Rule Actions, without incurring additional messaging charges.  

AWS Key Management Service (KMS) now offers a standalone console that simplifies the process of managing encryption keys. The new console separates AWS managed keys from customer managed keys within your account and provides enhanced search and filtering capabilities to more easily find the keys you want to manage, even if you have hundreds or thousands of keys. 

Now you can use AWS Single Sign-On (AWS SSO) to manage SSO access to more pre-integrated business applications such as Evernote, Datadog, and JFrog Artifactory. This expands the list of already pre-integrated business applications, such as Salesforce, Google Suite, and Microsoft Office 365. For full list of business applications pre-integrated with AWS SSO, see Cloud Applications.

The new AWS CloudFormation console is now generally available. The new console makes it easier for you to use AWS CloudFormation and simplifies the management of CloudFormation Stacks and StackSets. You can opt-in to the new experience by visiting the CloudFormation console. Send us your comments and suggestions using the feedback link in the new console.

The CloudFormation console is available in US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), EU (Ireland), EU (Frankfurt), EU (Paris), EU (London), South America (Sao Paulo), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Osaka), Asia Pacific (Seoul) and Asia Pacific (Mumbai) regions. To learn more about CloudFormation, visit our product page.
 

Amazon Elasticsearch Service Reserved Instances (RIs) offer significant discounts compared to standard On-Demand Instances. Starting today, you can use AWS Budgets to set custom utilization and coverage budgets based on your Amazon Elasticsearch RIs that alert you when you fall below the threshold you define.

Amazon Inspector now offers agentless network assessments with the ‘Network Reachability’ rules package that identifies ports and services on your Amazon EC2 instances that are accessible from outside your VPC. With just a few clicks in the Inspector console, you can analyze the network configuration of your AWS account to identify the resources accessible from the internet or private networks like VPN, Direct Connect, or a peered VPC.  

Amazon Glacier is now available in the AWS South America (Sao Paulo) Region. Customers use Amazon Glacier to archive data for long term retention, for compliance, and to back up content that is infrequently accessed. Amazon Glacier can be used directly through the AWS Management Console; the AWS software development kits (SDKs) for Java, .NET, PHP, or Python; the Amazon Glacier API directly; or by using S3 Lifecycle policies to move existing objects stored in Amazon S3 to Amazon Glacier for archival storage. With this expansion, Amazon Glacier is now available in all AWS Regions.

Amazon SageMaker now supports incremental learning for its built-in visual recognition algorithms – Image Classification and Object Detection. With incremental learning, you can initialize your model with knowledge learned from prior training. This enables the model to preserve the knowledge gained previously and extend it by training the model on new data. It becomes useful when your training data comes in batches over time. With this capability, you do not need to retrain your model from scratch every time you get new data, saving you time and compute resources.

Amazon SageMaker now supports Apache MXNet 1.3 and TensorFlow 1.11 in its built-in containers for MXNet and TensorFlow respectively. This makes it easier to run MXNet and TensorFlow scripts, while taking advantage of the capabilities Amazon SageMaker offers, including a library of high-performance algorithms, managed and distributed training with automatic model tuning, one-click deployment, and managed hosting.

Amazon RDS for SQL Server now offers Always On Availability Groups for the Multi-AZ configuration in all AWS Regions. This new high availability option meets the requirements of enterprise-grade production workloads on SQL Server. 

Amazon API Gateway now provides a tiered pricing model for API requests. This enables you to decrease your costs based on the number of API requests you make per region across your AWS accounts. API Gateway is a service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.

Amazon QuickSight now allows you to create Top N and Bottom N filters to see the highest or lowest performers on a given metric. This feature allows dashboard authors to create charts, tables, and other visualizations that will dynamically show only the top or bottom N members of a dimension regardless of how many members there are or how frequently they may change. The Top N and Bottom N options can be used with text, date, and numeric filters. See here to learn more.

Amazon Simple Queue Service (SQS) First-in, First-out (FIFO) queues are now available in Asia Pacific (Tokyo) and Asia Pacific (Sydney) regions, in addition to existing availability in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) regions.

You can now clone existing hyperparameter tuning jobs to create new jobs through the Amazon SageMaker console. It is common to run multiple hyperparameter tuning jobs with the same parameters such as datasets, hyperparameter ranges, and compute resources. Setting up many jobs with the same details can be tedious and time-consuming. Cloned hyperparameter tuning jobs use the same configuration of the parent tuning job. This makes it easier and faster to create new hyperparameter tuning jobs.

Amazon SageMaker now supports AWS Key Management Service (KMS) to encrypt data used for Batch Transform. Using AWS KMS, customers have the ability to control the encryption keys to protect the storage volumes used in their Batch Transform jobs. 

AWS has updated Centralized Logging, a solution that automatically provisions the services necessary to collect, analyze, and display logs on AWS across multiple accounts and AWS Regions. The solution now leverages the scalability and security features of Amazon Cognito User Pools for Kibana dashboard user authentication, supports Amazon Elasticsearch Service (Amazon ES) version 6.3, and includes the option to encrypt Amazon ES data at rest.

Beginning today, if you're using geoproximity routing in the Amazon Route 53 Traffic Flow console, you can see how your end users will be routed to each of your application's endpoints on an interactive map.

Xilinx Zynq-7000 Programmable system-on-chip (SoC) is now qualified for Amazon FreeRTOS. You can take advantage of Amazon FreeRTOS features and benefits using the Avnet MicroZed Industrial IoT Bundle, a development board powered by Xilinx.

AWS Fargate is now available in US West (Northern California) and Asia Pacific (Seoul) Regions.

AWS Fargate is a compute engine for Amazon ECS that lets you run containers in production without deploying or managing servers. Fargate lets you focus on designing and building your applications instead of managing the infrastructure that runs them.

For a full list of AWS Regions where Fargate is available, please visit our Region table.

 

Amazon FreeRTOS is now available in the Asia Pacific (Mumbai), Asia Pacific (Seoul), and Asia Pacific (Singapore) regions.

Amazon FreeRTOS is an IoT operating system for microcontrollers that extends the FreeRTOS kernel with software libraries for security, connectivity, and updateability to make small, low-powered edge devices easy to program, deploy, secure, connect, and manage.

This Quick Start deploys a Dynatrace Managed cluster on the Amazon Web Services (AWS) Cloud. 

Starting today, you can create Amazon RDS for MariaDB, Amazon RDS for MySQL and Amazon RDS for PostgreSQL database instances with up to 32 TiB of storage. Existing database instances using SSD-backed storage can also be scaled up to 32 TiB storage without any downtime. The new storage limit is an increase from 16 TiB and is supported for Provisioned IOPS and General Purpose SSD storage types.

The 32 TiB storage size supports larger databases and it also allows you to consolidate database shards into a single database instance, which will simplify your application code and reduce database administration work. To learn more, please visit the Storage for Amazon RDS documentation page.

Amazon RDS makes it easy to set up, operate, and scale a relational database in the cloud. See Amazon RDS for MariaDB, Amazon RDS for MySQL and Amazon RDS for PostgreSQL for regional availability.

Amazon Elastic Container Service for Kubernetes (EKS) is now available in the AWS US East (Ohio) region.

AWS Elemental MediaPackage is a video origination and just-in-time packaging service that allows video distributors to securely and reliably deliver streaming content at scale. From a single video input, AWS Elemental MediaPackage creates video streams formatted to play on connected TVs, mobile phones, computers, tablets, and game consoles. It makes it easy to implement popular video features commonly found on DVRs, such as start-over, pause, and rewind. The service can also protect your content using Digital Rights Management (DRM) technologies.

Following the recent announcement of updates to the PostgreSQL database, we have updated Amazon RDS for PostgreSQL to support PostgreSQL minor versions 10.5, 9.6.10, 9.5.14, 9.4.19, and 9.3.24 in the AWS GovCloud (US) region. This release fixes PostgreSQL security vulnerabilities included in current and previous minor releases by the PostgreSQL community and contains additional bug fixes and improvements.

With this update, we have also added support for extensions pglogical version 2.2.0 and pg_similarity version 1.0 in PostgreSQL 10.5 and 9.6.10; pageinspect version 1.6 extension is supported with PostgreSQL 10.5; Map Box Vector Tiles in PostGIS with libprotobuf-c is supported with PostgreSQL 10.5; wal2json has been updated to 01c5c1ec in PostgreSQL 10.5 and 9.6.10; and pg_hint_plan has been updated to 1.3.1 and 1.2.3.

Amazon RDS for PostgreSQL makes it easy to set up, operate, and scale PostgreSQL deployments in the cloud. Learn more about upgrading your database instances from the Amazon RDS User Guide. See Amazon RDS for PostgreSQL Pricing for regional availability.

Amazon Kinesis Data Analytics is now available in the AWS US East (Ohio) Region. Amazon Kinesis Data Analytics is the easiest way to process streaming data in real time with standard SQL without having to learn new programming languages or processing frameworks. Amazon Kinesis Data Analytics enables you to query streaming data or build entire streaming applications using SQL, so that you can gain actionable insights and respond to your business and customer needs promptly.

Amazon Neptune is now available in the Europe (Frankfurt) region.

You can now use Apache Flink 1.6.0, Apache Zeppelin 0.8.0, and S3 Select with Apache Hive and Presto on Amazon EMR release 5.18.0. Flink 1.6.0 adds several new features and updates, including native support for state TTL that allows you to control access to Flink states and support for HTTP/REST based job submissions that allows better integration with container environments on the cluster. It also features several SQL and Table API improvements that simplify the executions of streaming and batch queries and adds SQL support for Avro data format. Zeppelin 0.8.0 features support for running Spark interpreter in Apache Hadoop YARN cluster mode, support for Ipython interpreter, and ability to use Apache HDFS as backend storage for saving and reading Zeppelin notebook files.

With EMR release 5.18.0, you can now use S3 Select with Hive and Presto. S3 Select allows applications to retrieve only a subset of data from an object stored in Amazon S3. This improves performance as it reduces the amount of data that needs to be transferred to and processed by the EMR cluster when running Hive and Presto queries. Please visit S3 Select with Hive and S3 Select with Presto pages to learn more about these features.

Additionally, with this release, you can also use the upgraded versions of Apache Spark 2.3.2, Apache HBase 1.4.7, and Presto 0.210.

You can create an Amazon EMR cluster with the release 5.18.0 by choosing the release label “emr-5.18.0” from the AWS Management Console, AWS CLI, or SDK. You can select Flink, HBase, Presto, and Zeppelin to install these applications when you launch your EMR cluster. Please visit the Amazon EMR documentation for more information about EMR release 5.18.0, Flink 1.6.0, HBase 1.4.7, Spark 2.3.2, Presto 0.210, and Zeppelin 0.8.0.

Amazon EMR release 5.18.0 is now available in all supported regions for Amazon EMR.

You can stay up to date on EMR releases by subscribing to the RSS feed for EMR release notes. Use the RSS icon at the top of the EMR Release Guide to link the feed URL directly to your favorite feed reader.

You can now launch Amazon EMR clusters with the next generation of graphics powered G3 instances, memory-optimized Z1d instances, and storage-optimized H1 instances from the Amazon EC2 family. G3 and Z1d instances are available with the EMR release 5.18.0 and later while H1 instances are available with the EMR release 5.17.0 and later.

The graphics powered G3 instances feature the latest generation of GPUs and are ideal for graphics-intensive big-data applications that require a powerful combination of CPU, memory, and GPU capacity.

The Z1d instances feature high single thread performance CPUs and high memory and are ideal for memory intensive application that doesn’t require many CPUs.

The storage-optimized H1 instances offer dense local HDD storage with high network bandwidth and are ideal for data-intensive applications that require low-cost storage, high disk throughput, and fast sequential disk I/O access to big datasets.

These instances are available in various sizes, to learn more about these instances, please visit the Amazon EC2 instance page. For Amazon EMR pricing for these instances, visit the Amazon EMR pricing page.

Amazon EMR supports these instances in the following regions:

G3 instances are supported in the US East (N.Virginia and Ohio), US West (San Francisco and Oregon), Europe(Ireland and Frankfurt), and Asia Pacific (Sydney, Singapore, and Tokyo) regions.

Z1d instances are supported in the US East (N.Virginia), US West(San Francisco and Oregon), Europe(Ireland), and Asia Pacific(Singapore and Tokyo) regions.

H1 instances are supported in the US East (N.Virginia and Ohio), US West (Oregon), and Europe(Ireland) regions.

Amazon EMR now supports a public EMR artifacts repository to help developers build applications based on the EMR distribution for Apache Hadoop and Apache Hive using Apache Maven for dependency management. The EMR artifacts repository hosts the same optimized versions of libraries and dependencies that are available with specific Amazon EMR release versions, ensuring that artifacts used in building applications against the EMR stack are compatible with the runtime libraries on the EMR cluster. Artifacts in the repository correspond to EMR release 5.18.0 and later. Please visit the Amazon EMR documentation for more information about the EMR artifacts repository.

Details: Amazon CloudFront announces six new Edge locations, adding to our global presence in major cities around the world. The new edge locations are in Hyderabad (2), New Delhi, London (2), and Hillsboro. Both Hyderabad, India and Hillsboro, Oregon are brand new locations. With this launch, CloudFront increases its average request processing capacity in India and the United Kingdom by up to 55%.
Adding these edge locations enhances delivery, performance, and scale for our customers. A full list of CloudFront’s global locations is available on the CloudFront Features webpage.

The Machine Learning for Telecommunicaton solution provides a framework for an end-to-end machine learning (ML) process including ad-hoc data exploration, data processing and feature engineering, and model training and evaluation. It also includes a synthetic telecom IP Data Record (IPDR) dataset to demonstrate how to use ML algorithms to test and train models for predictive analysis in telecommunication. Customers can use the included notebooks as a starting point to develop their own custom ML models, and customize the included Jupyter notebooks for their own use case.

Starting today, payer accounts can access all of their linked accounts’ Reserved Instance (RI) purchase recommendations from a centralized view, making it easy to identify cost savings opportunities on a per-account basis. Previously, payer accounts were only able to access recommendations based on the aggregate usage across all of their accounts. This launch expands the RI purchase recommendation capabilities to allow payer accounts to generate and view recommendations based on the usage incurred by each account. This allows you to easily identify the cost savings opportunities for each of your accounts from a central location, eliminating the need to access recommendations separately for each of your accounts.

Amazon RDS for Oracle is announcing support for Extended Data Types when using custom parameter groups with the MAX_STRING_SIZE parameter set to EXTENDED. Extended Data Types let users increase the maximum size of VARCHAR2, NVARCHAR2 and RAW columns to 32767 bytes.

Today, Amazon Pinpoint added support for transactional emails. This capability exists alongside the existing campaign-based email sending features of Amazon Pinpoint. Transactional emails are a great tool for sending ad-hoc messages, such as order confirmations and password reset emails.

Amazon Web Services (AWS) announces the availability of new EC2 instances featuring 2.5 GHz AMD EPYC 7000 series processors that are variants of Amazon EC2’s general purpose (M5) and memory optimized (R5) instance families. The AMD-based instances provide additional options for customers who are looking to achieve a 10% cost savings on their Amazon EC2 compute environment for a variety of workloads. M5a instances are ideal for business-critical applications, web and application servers, back-end servers for enterprise applications, gaming servers, caching fleets, and app development environments. R5a instances are ideal for high performance databases, distributed web scale in-memory caches, mid-size in-memory databases, real time big data analytics, and other enterprise applications.

Amazon EC2 Spot Console now provides savings information for Spot Instances launched in your account, enabling you to quickly understand the cost savings achieved over On-Demand prices. Using the Spot console, now you can view the usage and savings information for Spot Instances at the fleet level, or for all the running Spot Instances. You can view the savings made in the last hour or the last three days, and you can view the average cost per vCPU hour and per memory (GiB) hour. You can also view the detailed usage and savings information for the individual instance types used per single Spot Fleet or for all the running Spot instances.

To start viewing the Spot savings information for Spot fleets, choose “Savings” for a selected Spot Fleet request under EC2 Spot Console. By default, usage and savings information is displayed for the last 3 days. To start viewing the Spot savings across all the running instances, choose “Savings Summary” under EC2 Spot Console.

EC2 Spot savings information is accessible only using EC2 Spot Console. EC2 Spot savings is now available in all public AWS regions.

You can learn more about this feature by reading the documentation page.

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with Chainer 5.0, which includes support for Python 3.6 and iDeep 2.0. As with other frameworks, Deep Learning AMIs offer optimized builds of Chainer 5.0 that are fine-tuned and fully-configured for high performance deep learning on Amazon EC2 CPU and GPU instances.

The built-in algorithms that come with Amazon SageMaker now support Pipe Mode for datasets in CSV format. This accelerates the speed at which data can be streamed from Amazon Simple Storage Service (S3) into SageMaker by up to 40%, while training machine learning (ML) models. With this new enhancement, the performance benefits of Pipe Mode are extended to training datasets in CSV format in addition to the protobuf recordIO format that we released earlier this year.

You can now launch M5 instance types when using Amazon RDS for Oracle. Amazon EC2 M5 instances are the next generation of the Amazon EC2 General Purpose compute instances. M5 instances offers a balance of compute, memory, and networking resources for a broad range of database workloads.

Amazon Inspector security findings now include the Amazon Machine Image (AMI) ID, instance tags, auto scaling group, hostname, IP addresses, DNS names, and subnet ID of the Amazon EC2 instance that has the vulnerability or insecure configuration. You can view these fields by clicking the ‘Show Details’ button while reviewing a finding in the management console. These fields are also available when you describe findings through the AWS API and CLI.

Amazon Inspector automatically assesses applications for vulnerabilities or deviations from best practices. After performing an assessment, Amazon Inspector produces a detailed list of security findings prioritized by level of severity. These additional fields help you filter, group, and prioritize your security findings based on the image, network location, tags, or other attributes of vulnerable EC2 instances.

Amazon Inspector is available in the following eleven regions: US East (Northern Virginia), US East (Ohio), US West (Northern California), US West (Oregon), EU (Frankfurt), EU (Ireland), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), and AWS GovCloud (US).

To learn more about Amazon Inspector or to start your free trial, please visit Amazon Inspector.

Amazon Rekognition is a deep learning-based image and video analysis service that can identify objects, people, text, scenes, and activities, as well as detect unsafe content. Today we are announcing a major update to object and scene detection, also known as label detection. Label detection identifies objects and scenes in images. Until now, Amazon Rekognition could identify the presence of an object in an image, but couldn't find where the object is within the image. Amazon Rekognition can now specify the location of common objects such as dogs, people and cars in an image by returning object bounding boxes, and comes with significantly improved accuracy for all existing object and scene labels across a variety of use cases. In addition, customers can use the bounding box information to infer how many of each object ("3 dogs") occur in the image, and the relationship between objects ("dog on a couch"). These new enhancements all come at no additional cost.

You can now add outbound mail flow rules for your Amazon WorkMail organization. Outbound mail flow rules allow you to perform simple control actions like blocking sending of a message or routing messages to a custom appliance via the SMTP protocol. This adds extensibility to the WorkMail product by allowing you to control sending or delegate handling of outbound e-mail to 3rd party or custom appliances. For example, you can now route to appliances for data leak protection, email encryption, IP reputation management, and archiving. Furthermore, you can specify conditions for each outbound mail flow rule based on the domain and email addresses of the message sender and recipients. The rule will then only run when the sender and recipients match the rule's condition. This way you can route traffic differently depending on who is sending and who they are sending to. This control is especially useful if you are routing to appliances with a per-user cost model, so you can route to only the users who need it. Finally, this feature complements Amazon WorkMail’s interoperability with Microsoft Exchange by enabling customers who are transitioning from Exchange to WorkMail to route outbound email through their existing on-premises Exchange setup.

Like inbound mail flow rules, setting up outbound mail flow rules is easy with just a few quick steps in the Amazon WorkMail console. To get started, go to your Amazon WorkMail organization and click on Organization Settings. You can specify rules with the drop, bounce, or the SMTP routing actions, together with which sender and recipient email addresses or domains the rule should apply to.

Outbound mail flow rules are available today for Amazon WorkMail in all AWS Regions where Amazon WorkMail is offered. To learn more about Amazon WorkMail, or to start your trial, please visit Amazon WorkMail.

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Amazon S3 Management Console is now available in five new languages – Spanish, Italian, German, Traditional Chinese, and Portuguese – increasing the number of languages supported by the S3 Management Console to ten. The expansion of languages makes it even easier to configure, manage, and administer your S3 storage buckets and objects in your native language. The S3 Management Console is also available in English, French, Japanese, Korean, and Simplified Chinese.

Amazon GuardDuty has further optimized the analysis of AWS CloudTrail logs resulting in reduced GuardDuty cost for many customers. Cost reductions will vary by customer based on their volume of AWS CloudTrail logs. Customers with high-volumes of global CloudTrail events will see the greatest net positive impact. We introduced the first round of CloudTrail log analysis improvement in June 2018. This next optimization is a continuation of our efforts to reduce GuardDuty costs for customers, while at the same time continuously adding security value by introducing new detections and improving existing detections in the service.

Amazon Relational Database Service (RDS) now sends all service events to Amazon CloudWatch Events, allowing you to track and respond to changes in your Amazon RDS resources.

Amazon Elastic File System (Amazon EFS) now supports 512 locks per file.