AWS This week Announcements October(8-14)
AWS This week Announcements October(8-14)
- NICE DCV Now Supports Credential Providers in Windows, Audio-out in Linux, and Multiple GPU Encoding
- Amazon Rekognition improves the accuracy of image moderation
- Consulting Partners Can Now Resell Software Solutions Available in AWS Marketplace
- AWS IoT Device Management Now Provides In Progress Timeouts and Step Timeouts for Jobs
- Amazon GuardDuty Provides Customization of Notification Frequency to Amazon CloudWatch Events
- Alexa for Business Now Lets Users Book Conference Rooms Using Alexa
- Deploy Spotinst Elastigroup for Amazon ECS on AWS with New Quick Start
- AWS Elastic Beanstalk Console Supports Network Load Balancer
- Amazon RDS for MySQL now supports global transaction identifiers (GTIDs)
- Amazon Transcribe Supports Deletion of Completed Transcription Jobs
- AWS Cost Explorer’s Reserved Instance Reports now Support Amazon Elasticsearch Reservations
- Amazon Comprehend Extends Natural Language Processing for Additional Languages and Region
- AWS PrivateLink now supports access over Inter-Region VPC Peering
- AWS Lambda enables functions that can run up to 15 minutes
- Amazon SageMaker Now Supports an Improved Pipe Mode Implementation
- Network Load Balancer now supports Inter-Region VPC Peering
- Amazon Aurora Databases Support up to Five Cross-Region Read Replicas
- AWS Systems Manager Launches Custom Approvals for Patching
- AWS Direct Connect now Supports Jumbo Frames for Amazon Virtual Private Cloud Traffic
- Resource Groups Tagging API Supports Additional AWS Services
- AWS Lambda Console Now Enables You to Manage and Monitor Serverless Applications
- Amazon RDS for Oracle Now Supports Database Storage Size up to 32TiB
- Introducing a New Size for Amazon EC2 G3 Graphics Accelerated Instances
- Amazon Athena adds support for Creating Tables using the results of a Select query (CTAS)
- Amazon EKS Enables Support for Kubernetes Dynamic Admission Controllers
- Amazon S3 Select is now available in the AWS GovCloud (US) Region
NICE DCV Now Supports Credential Providers in Windows, Audio-out in Linux, and Multiple GPU Encoding
NICE DCV is a remote visualization protocol that enables users to securely access remote desktop or application sessions, including 3D graphics applications hosted on servers with high-performance GPUs. NICE DCV offers end users a wide range of client devices, including an HTML5 client for web browser access, a native Windows client, and now a native Linux client. Native clients now support USB redirection for 3D mice and USB storage devices, and they support up to 4 monitors at 4k resolution. In addition, NICE DCV helps customers with remote Linux desktops reduce session costs by supporting multiple Linux sessions from a single high performance server. NICE DCV is free to use on Amazon EC2 instances.
The DCV 2017.2 release introduces the following features:
The DCV 2017.2 release introduces the following features:
- Audio-out in Linux
- Credential Providers support in Windows
- Hardware encoding using multiple GPUs with NVENC.
Amazon Rekognition improves the accuracy of image moderation
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. Amazon Rekognition now comes with an improved image moderation model that reduces false positive rates by 40% on average without any reduction in detection rates for truly unsafe content. Lower false positive rates imply lower volumes of flagged images to be reviewed further, leading to higher efficiency of human moderators and more cost savings.
Consulting Partners Can Now Resell Software Solutions Available in AWS Marketplace AWS Marketplace, which lists over 4,200 software listings from 1,400 Independent Software Vendors (ISVs), has announced that customers can now purchase software solutions directly from their preferred Consulting Partner in AWS Marketplace. This new feature helps you benefit from Consulting Partners who have knowledge of your business, localized support, and expertise and expands the ways you can purchase in AWS Marketplace. You can also negotiate with Consulting Partners who are authorized by an ISV on offer details such as pricing, end-user licensing terms, and add professional services before an offer is created. When offer details are finalized, Consulting Partners work with an AWS Marketplace representative to create and extend an offer with Seller Private Offers.
AWS IoT Device Management Now Provides In Progress Timeouts and Step Timeouts for Jobs
AWS IoT Device Management now provides two new jobs timeout configurations, in progress timeouts and step timeouts, which provide additional mechanisms to control and track executions for jobs running on devices. Jobs let you 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.
In progress timeouts allow customers to configure the time period that a job execution must reach a terminal state while it is in progress. This can help you easily identify situations where you may have a stuck job execution, such as if a device goes offline or if a firmware update is stuck in a loop. In progress timeouts are easily configured as an optional parameter during the creation of a job.
You can also configure a step timeout duration, which can be used to set a time period in which a particular execution step within a device's job must be completed. An example is to set a step timeout for which a device's download or validation of a firmware binary should be completed during an over-the-air (OTA) update. The step timeout duration can be updated by a device as it executes and completes each step within a job.
AWS IoT Device Management now provides two new jobs timeout configurations, in progress timeouts and step timeouts, which provide additional mechanisms to control and track executions for jobs running on devices. Jobs let you 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.
In progress timeouts allow customers to configure the time period that a job execution must reach a terminal state while it is in progress. This can help you easily identify situations where you may have a stuck job execution, such as if a device goes offline or if a firmware update is stuck in a loop. In progress timeouts are easily configured as an optional parameter during the creation of a job.
You can also configure a step timeout duration, which can be used to set a time period in which a particular execution step within a device's job must be completed. An example is to set a step timeout for which a device's download or validation of a firmware binary should be completed during an over-the-air (OTA) update. The step timeout duration can be updated by a device as it executes and completes each step within a job.
Amazon GuardDuty Provides Customization of Notification Frequency to Amazon CloudWatch EventsAmazon GuardDuty customers can now customize the notification frequency to Amazon CloudWatch Events for subsequent occurrences of an existing finding. Prior to this feature, recurring GuardDuty findings generated a CloudWatch Event every 6 hours. Now, customers can customize this to recur in 15 minute, 1 hour or the default 6 hour intervals. Unique/first time findings still generate a CloudWatch Event close to real time.
Alexa for Business Now Lets Users Book Conference Rooms Using Alexa
Starting today, Alexa for Business lets users check availability and reserve conference rooms using Alexa. Finding and booking a conference room for a last minute meeting is frequently a stressful, time-consuming task for many people. With Alexa for Business, users can check the current or future availability of the conference room they are in by asking, “Alexa, is this room free?”, or “Alexa, is this room free at 4?”. Or, they can reserve the room by saying “Alexa, book this room for half an hour”, or “Alexa, reserve this room at 2”. Conference rooms that are open might actually be reserved for a meeting, so users can identify who owns the current reservation by asking, “Alexa, who booked this room?”, and then find out if the meeting is actually happening or not.
Starting today, Alexa for Business lets users check availability and reserve conference rooms using Alexa. Finding and booking a conference room for a last minute meeting is frequently a stressful, time-consuming task for many people. With Alexa for Business, users can check the current or future availability of the conference room they are in by asking, “Alexa, is this room free?”, or “Alexa, is this room free at 4?”. Or, they can reserve the room by saying “Alexa, book this room for half an hour”, or “Alexa, reserve this room at 2”. Conference rooms that are open might actually be reserved for a meeting, so users can identify who owns the current reservation by asking, “Alexa, who booked this room?”, and then find out if the meeting is actually happening or not.
Deploy Spotinst Elastigroup for Amazon ECS on AWS with New Quick Start
Spotinst Elastigroup enables the use of production-grade Spot Instances by leveraging a prediction algorithm to predict the Spot Instance interruption 15 minutes ahead of time. Elastigroup then immediately spins up a new node in a different Spot capacity pool. As soon as that new node is healthy, Elastigroup starts to drain the instance that is marked for interruption. Elastigroup then terminates the instance after draining is completed.
This Quick Start is for organizations that want to use Spotinst Elastigroup’s capabilities to ensure availability and to efficiently scale Amazon ECS clusters that are running as Spot Instances at a discount of roughly 80% compared to On-Demand Instances, with additional savings from automatic task-based scaling.
With this Quick Start, Spotinst Elastigroup deployment takes about 7 minutes. The deployment is automated by AWS CloudFormation templates.
Spotinst Elastigroup enables the use of production-grade Spot Instances by leveraging a prediction algorithm to predict the Spot Instance interruption 15 minutes ahead of time. Elastigroup then immediately spins up a new node in a different Spot capacity pool. As soon as that new node is healthy, Elastigroup starts to drain the instance that is marked for interruption. Elastigroup then terminates the instance after draining is completed.
This Quick Start is for organizations that want to use Spotinst Elastigroup’s capabilities to ensure availability and to efficiently scale Amazon ECS clusters that are running as Spot Instances at a discount of roughly 80% compared to On-Demand Instances, with additional savings from automatic task-based scaling.
With this Quick Start, Spotinst Elastigroup deployment takes about 7 minutes. The deployment is automated by AWS CloudFormation templates.
To get started:
- View the architecture and details
- View the deployment guide for step-by-step instructions
- Download the AWS CloudFormation templates that automate the deployment
AWS Elastic Beanstalk Console Supports Network Load Balancer
AWS Elastic Beanstalk now supports creating Network Load Balancers through the AWS Elastic Beanstalk console.
Previously, you could only create a Network Load Balancer using the AWS Elastic Beanstalk CLI (Command Line Interface). Now in the Elastic Beanstalk console, you can now choose Network Load Balancer in addition to Application Load Balancer and Classic Load Balancer, when you configure your Elastic Beanstalk environment for high availability. A Network Load Balancer is ideal for load balancing of TCP traffic and is capable of handling millions of requests per second while maintaining ultra-low latencies. Network Load Balancers are also optimized to handle sudden and volatile traffic patterns. For more information about the features supported by each load balancer type, see Comparison of Elastic Load Balancing Products.
In addition, the AWS Elastic Beanstalk console now defaults to using Application Load Balancer by default instead of the previous generation Classic Load Balancer when creating an Elastic Beanstalk high availability environment.
AWS Elastic Beanstalk now supports creating Network Load Balancers through the AWS Elastic Beanstalk console.
Previously, you could only create a Network Load Balancer using the AWS Elastic Beanstalk CLI (Command Line Interface). Now in the Elastic Beanstalk console, you can now choose Network Load Balancer in addition to Application Load Balancer and Classic Load Balancer, when you configure your Elastic Beanstalk environment for high availability. A Network Load Balancer is ideal for load balancing of TCP traffic and is capable of handling millions of requests per second while maintaining ultra-low latencies. Network Load Balancers are also optimized to handle sudden and volatile traffic patterns. For more information about the features supported by each load balancer type, see Comparison of Elastic Load Balancing Products.
In addition, the AWS Elastic Beanstalk console now defaults to using Application Load Balancer by default instead of the previous generation Classic Load Balancer when creating an Elastic Beanstalk high availability environment.
Amazon RDS for MySQL now supports global transaction identifiers (GTIDs)
Amazon RDS for MySQL now supports global transaction identifiers (GTIDs), which uniquely identify each transaction on the server and within a replication setup.
Traditional MySQL replication is based on relative coordinates, with each replica keeping track of its position with respect to its current master's binary log files. This file-offset pair is used to determine points for starting, stopping, or resuming the flow of data between master and replica.
GTID is based on absolute coordinates, with each transaction having a unique identifier and each MySQL server keeping track of which transactions it has already executed. The absolute coordinates of a GTID permit "auto-positioning," the ability for a replica to be pointed at a master instance without needing to specify a binlog filename or position in the CHANGE MASTER statement. Customers can take advantage of GTIDs’ auto-positioning for simpler and less error-prone failover to replicas, hierarchical replication, point-in-time backup recovery, and crash-safe multi-threaded replication.
Amazon RDS for MySQL now supports global transaction identifiers (GTIDs), which uniquely identify each transaction on the server and within a replication setup.
Traditional MySQL replication is based on relative coordinates, with each replica keeping track of its position with respect to its current master's binary log files. This file-offset pair is used to determine points for starting, stopping, or resuming the flow of data between master and replica.
GTID is based on absolute coordinates, with each transaction having a unique identifier and each MySQL server keeping track of which transactions it has already executed. The absolute coordinates of a GTID permit "auto-positioning," the ability for a replica to be pointed at a master instance without needing to specify a binlog filename or position in the CHANGE MASTER statement. Customers can take advantage of GTIDs’ auto-positioning for simpler and less error-prone failover to replicas, hierarchical replication, point-in-time backup recovery, and crash-safe multi-threaded replication.
Amazon Transcribe Supports Deletion of Completed Transcription Jobs
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. Starting today, you have the ability to conveniently delete completed transcription jobs. This gives you end-to-end control over your transcription jobs and for how long your output transcripts are stored.
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. Starting today, you have the ability to conveniently delete completed transcription jobs. This gives you end-to-end control over your transcription jobs and for how long your output transcripts are stored.
AWS Cost Explorer’s Reserved Instance Reports now Support Amazon Elasticsearch Reservations
AWS Cost Explorer’s Reserved Instance (RI) Utilization and Coverage reports provide you with the ability to visualize your utilization and coverage trends both at a high level (e.g., utilization across all Amazon RDS reservations) or for highly-specific requests (e.g., utilization of all regional Amazon EC2 reservations owned by a particular linked account).
Starting today, you can view utilization and coverage information for your Amazon Elasticsearch reservations via AWS Cost Explorer. Using these reports, you can access information regarding your RI-related savings, RI hours purchased, and more over a user-defined time frame. From there, you can further refine the underlying dataset using the available filtering dimensions, which include Instance Type, Region, and Platform. Once you have refined the view of your data to meet your needs, you can save your progress as a custom report and refer back to it later.
AWS Cost Explorer’s Reserved Instance (RI) Utilization and Coverage reports provide you with the ability to visualize your utilization and coverage trends both at a high level (e.g., utilization across all Amazon RDS reservations) or for highly-specific requests (e.g., utilization of all regional Amazon EC2 reservations owned by a particular linked account).
Starting today, you can view utilization and coverage information for your Amazon Elasticsearch reservations via AWS Cost Explorer. Using these reports, you can access information regarding your RI-related savings, RI hours purchased, and more over a user-defined time frame. From there, you can further refine the underlying dataset using the available filtering dimensions, which include Instance Type, Region, and Platform. Once you have refined the view of your data to meet your needs, you can save your progress as a custom report and refer back to it later.
Amazon Comprehend Extends Natural Language Processing for Additional Languages and Region
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Today, we are pleased to announce that Amazon Comprehend supports four additional languages: French, German, Italian, and Portuguese. AWS customers working in these languages can now directly analyze feedback and articles and organize information using Comprehend. The service is also available in the AWS Europe (Frankfurt) Region starting today.
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Today, we are pleased to announce that Amazon Comprehend supports four additional languages: French, German, Italian, and Portuguese. AWS customers working in these languages can now directly analyze feedback and articles and organize information using Comprehend. The service is also available in the AWS Europe (Frankfurt) Region starting today.
AWS PrivateLink now supports access over Inter-Region VPC Peering
Applications in an AWS VPC can now securely access AWS PrivateLink endpoints across AWS Regions using Inter-Region VPC Peering. AWS PrivateLink allows you to privately access services hosted on AWS in a highly available and scalable manner, without using public IPs, and without requiring the traffic to traverse the Internet. This release makes it possible for customers to privately connect to a service even if the service endpoint resides in a different AWS Region. Traffic using Inter-Region VPC Peering stays on the global AWS backbone and never traverses the public Internet.
Applications in an AWS VPC can now securely access AWS PrivateLink endpoints across AWS Regions using Inter-Region VPC Peering. AWS PrivateLink allows you to privately access services hosted on AWS in a highly available and scalable manner, without using public IPs, and without requiring the traffic to traverse the Internet. This release makes it possible for customers to privately connect to a service even if the service endpoint resides in a different AWS Region. Traffic using Inter-Region VPC Peering stays on the global AWS backbone and never traverses the public Internet.
AWS Lambda enables functions that can run up to 15 minutes
You can now configure your AWS Lambda functions to run up to 15 minutes per execution. Previously, the maximum execution time (timeout) for a Lambda function was 5 minutes. Now, it is easier than ever to perform big data analysis, bulk data transformation, batch event processing, and statistical computations using longer running functions.
You can now configure your AWS Lambda functions to run up to 15 minutes per execution. Previously, the maximum execution time (timeout) for a Lambda function was 5 minutes. Now, it is easier than ever to perform big data analysis, bulk data transformation, batch event processing, and statistical computations using longer running functions.
Amazon SageMaker Now Supports an Improved Pipe Mode Implementation
Amazon SageMaker now includes an improved Pipe Mode implementation that accelerates the speed at which data can be streamed from Amazon Simple Storage Service (S3) into SageMaker, while training machine learning (ML) models. The latest implementation of Pipe Mode provides up to 9 times better data streaming throughput compared to File Mode.
Amazon SageMaker supports two methods of transferring training data: File Mode and Pipe Mode. With File Mode, the training data is downloaded first to an encrypted EBS volume attached to the training instance before training the model. With Pipe Mode, the data is streamed directly to the training algorithm while it is running. This results in faster training jobs and lesser disk space, reducing overall costs to train ML models on SageMaker.
Amazon SageMaker now includes an improved Pipe Mode implementation that accelerates the speed at which data can be streamed from Amazon Simple Storage Service (S3) into SageMaker, while training machine learning (ML) models. The latest implementation of Pipe Mode provides up to 9 times better data streaming throughput compared to File Mode.
Amazon SageMaker supports two methods of transferring training data: File Mode and Pipe Mode. With File Mode, the training data is downloaded first to an encrypted EBS volume attached to the training instance before training the model. With Pipe Mode, the data is streamed directly to the training algorithm while it is running. This results in faster training jobs and lesser disk space, reducing overall costs to train ML models on SageMaker.
Network Load Balancer now supports Inter-Region VPC Peering
Network Load Balancers now support connections from clients to IP-based targets in peered VPCs across different AWS Regions. Previously, access to Network Load Balancers from an inter-region peered VPC was not possible. With this launch, you can now have clients access Network Load Balancers over an inter-region peered VPC. Network Load Balancers can also load balance to IP-based targets that are deployed in an inter-region peered VPC. This support on Network Load Balancers is available in all AWS Regions.
Network Load Balancers now support connections from clients to IP-based targets in peered VPCs across different AWS Regions. Previously, access to Network Load Balancers from an inter-region peered VPC was not possible. With this launch, you can now have clients access Network Load Balancers over an inter-region peered VPC. Network Load Balancers can also load balance to IP-based targets that are deployed in an inter-region peered VPC. This support on Network Load Balancers is available in all AWS Regions.
Amazon Aurora Databases Support up to Five Cross-Region Read Replicas
Cross-region read replicas allow you to improve your disaster recovery posture, scale read operations in regions closer to your application users, and easily migrate from one region to another. Previously, an Aurora database could be deployed in one region and have a read replica in one additional region. With this update, you can replicate your Aurora database to a wider set of geographies.
Cross-region read replicas allow you to improve your disaster recovery posture, scale read operations in regions closer to your application users, and easily migrate from one region to another. Previously, an Aurora database could be deployed in one region and have a read replica in one additional region. With this update, you can replicate your Aurora database to a wider set of geographies.
AWS Systems Manager Launches Custom Approvals for Patching
AWS Systems Manager now provides more control on the patching workflow by adding the ability to define exactly what patches are approved for deployment and for how long those approved patches should be used for patching operations.
You can specify an approved list of patches for deployment, and you control how long it is in use. This also enables you to apply an approval process for a list of patches and use it for all patching operations for a desired time for consistency of patch deployment.
AWS Systems Manager now provides more control on the patching workflow by adding the ability to define exactly what patches are approved for deployment and for how long those approved patches should be used for patching operations.
You can specify an approved list of patches for deployment, and you control how long it is in use. This also enables you to apply an approval process for a list of patches and use it for all patching operations for a desired time for consistency of patch deployment.
AWS Direct Connect now Supports Jumbo Frames for Amazon Virtual Private Cloud Traffic
Customers can now use Jumbo Frames for traffic between their Virtual Private Cloud (VPC) and on-premises networks over AWS Direct Connect.
The maximum transmission unit (MTU) of a network connection is the size, in bytes, of the largest permissible packet that can be passed over the connection. The larger the MTU of a connection, the more data can be passed in a single packet. Until now, traffic over AWS Direct Connect was limited to 1,500 MTU.
With this release, customers can use Jumbo Frames for their AWS Direct Connect traffic. Jumbo Frames allow more than 1,500 bytes (up to 9,001 bytes) of data by increasing the payload size per packet, and thus lowering the packet overhead. As a result, you need fewer packets to send the same amount of data, which improves the end-to-end network performance. In addition, this release enables new use cases, such as supporting network overlay protocols, for on-premises connectivity over AWS Direct Connect
Customers can now use Jumbo Frames for traffic between their Virtual Private Cloud (VPC) and on-premises networks over AWS Direct Connect.
The maximum transmission unit (MTU) of a network connection is the size, in bytes, of the largest permissible packet that can be passed over the connection. The larger the MTU of a connection, the more data can be passed in a single packet. Until now, traffic over AWS Direct Connect was limited to 1,500 MTU.
With this release, customers can use Jumbo Frames for their AWS Direct Connect traffic. Jumbo Frames allow more than 1,500 bytes (up to 9,001 bytes) of data by increasing the payload size per packet, and thus lowering the packet overhead. As a result, you need fewer packets to send the same amount of data, which improves the end-to-end network performance. In addition, this release enables new use cases, such as supporting network overlay protocols, for on-premises connectivity over AWS Direct Connect
Resource Groups Tagging API Supports Additional AWS Services
You can now use the Resource Groups Tagging API to centrally manage tags and search resources for 6 additional AWS Services: Amazon Kinesis Data Firehose, AWS Secrets Manager, AWS Certificate Manager Private CA, AWS IoT Analytics, Amazon Aurora, and AWS Service Catalog.
You can now use the Resource Groups Tagging API to centrally manage tags and search resources for 6 additional AWS Services: Amazon Kinesis Data Firehose, AWS Secrets Manager, AWS Certificate Manager Private CA, AWS IoT Analytics, Amazon Aurora, and AWS Service Catalog.
AWS Lambda Console Now Enables You to Manage and Monitor Serverless Applications
You can now view, manage, and monitor your serverless applications directly from the AWS Lambda console using the new Applicationsmenu. This allows you to perform application level actions such as viewing all resources that together make up your application, and monitoring performance, errors, and traffic metrics for the application.
You can now view, manage, and monitor your serverless applications directly from the AWS Lambda console using the new Applicationsmenu. This allows you to perform application level actions such as viewing all resources that together make up your application, and monitoring performance, errors, and traffic metrics for the application.
Amazon RDS for Oracle Now Supports Database Storage Size up to 32TiB
you can create Amazon RDS for Oracle database instances with up to 32TiB of storage. Existing database instances using SSD-backed storage can also be scaled up to 32TiB storage without any downtime
The new storage limit is an increase from 16TiB and is supported for Provisioned IOPS and General Purpose SSD storage types.
The 32TiB storage size supports larger transactional databases, it also allows you to consolidate database shards into a single database instance, which will simplify your application code and reduce database administration work.
you can create Amazon RDS for Oracle database instances with up to 32TiB of storage. Existing database instances using SSD-backed storage can also be scaled up to 32TiB storage without any downtime
The new storage limit is an increase from 16TiB and is supported for Provisioned IOPS and General Purpose SSD storage types.
The 32TiB storage size supports larger transactional databases, it also allows you to consolidate database shards into a single database instance, which will simplify your application code and reduce database administration work.
Introducing a New Size for Amazon EC2 G3 Graphics Accelerated Instances
you can launch a smaller G3 instance - g3s.xlarge. Like other G3 instances, this new size is powered by NVIDIA Tesla M60 GPUs but is designed to be cost-effective for workloads that don’t need the high vCPU and RAM that current larger G3 instance sizes provide. The g3s.xlarge size has 4 vCPUs and 30.5 GiB of memory and is 50% lower in price compared to g3.4xlarge for Windows and 34% lower in price compared to g3.4xlarge for Linux.
you can launch a smaller G3 instance - g3s.xlarge. Like other G3 instances, this new size is powered by NVIDIA Tesla M60 GPUs but is designed to be cost-effective for workloads that don’t need the high vCPU and RAM that current larger G3 instance sizes provide. The g3s.xlarge size has 4 vCPUs and 30.5 GiB of memory and is 50% lower in price compared to g3.4xlarge for Windows and 34% lower in price compared to g3.4xlarge for Linux.
Amazon Athena adds support for Creating Tables using the results of a Select query (CTAS)
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Today, we are releasing support for creating tables using the results of a Select query or support for Create Table As Select (CTAS) statement. Analysts can use CTAS statements to create new tables from existing tables on a subset of data, or a subset of columns, with options to convert the data into columnar formats, such as Apache Parquet and Apache ORC, and partition it. Athena automatically adds the resultant table and partitions to the Glue Data Catalog, making them immediately available for subsequent queries. By default, CTAS statements in Athena write data in Parquet format. Other supported formats include Apache ORC, AVRO, JSON, and Text, with options to use Gzip or Snappy as compression formats.
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Today, we are releasing support for creating tables using the results of a Select query or support for Create Table As Select (CTAS) statement. Analysts can use CTAS statements to create new tables from existing tables on a subset of data, or a subset of columns, with options to convert the data into columnar formats, such as Apache Parquet and Apache ORC, and partition it. Athena automatically adds the resultant table and partitions to the Glue Data Catalog, making them immediately available for subsequent queries. By default, CTAS statements in Athena write data in Parquet format. Other supported formats include Apache ORC, AVRO, JSON, and Text, with options to use Gzip or Snappy as compression formats.
Amazon EKS Enables Support for Kubernetes Dynamic Admission Controllers
Amazon Elastic Container Service for Kubernetes (EKS) now supports dynamic admission controllers, allowing customers to deploy custom webhooks that enable additional open source tools for controlling network traffic and monitoring Kubernetes clusters on AWS.
Admissions controllers are a Kubernetes construct that allow you to run a piece of code after an API request has been authenticated and authorized, but before an object's state within the Kubernetes cluster is stored. Dynamic admission controllers allow you to accept, reject, or alter admission requests.
Amazon Elastic Container Service for Kubernetes (EKS) now supports dynamic admission controllers, allowing customers to deploy custom webhooks that enable additional open source tools for controlling network traffic and monitoring Kubernetes clusters on AWS.
Admissions controllers are a Kubernetes construct that allow you to run a piece of code after an API request has been authenticated and authorized, but before an object's state within the Kubernetes cluster is stored. Dynamic admission controllers allow you to accept, reject, or alter admission requests.
Amazon S3 Select is now available in the AWS GovCloud (US) Region
Amazon S3 Select is now available in the AWS GovCloud (US) 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.
S3 Select is a new Amazon S3 capability designed to pull out only the data you need from an object, which can dramatically improve the performance and reduce the cost of applications that need to access data in S3.
Most applications have to retrieve the entire object and then filter out only the required data for further analysis. S3 Select enables applications to offload the heavy lifting of filtering and accessing data inside objects to Amazon S3. By reducing the volume of data that has to be loaded and processed by your applications, S3 Select can improve the performance of most applications that frequently access data from S3 by up to 400%.
Select works on objects stored in CSV and JSON formats, Apache Parquet format, JSON Arrays, and BZIP2 compression for CSV and JSON objects. CloudWatch Metrics for S3 Select let you monitor S3 Select usage for your applications.
Amazon S3 Select is now available in the AWS GovCloud (US) 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.
S3 Select is a new Amazon S3 capability designed to pull out only the data you need from an object, which can dramatically improve the performance and reduce the cost of applications that need to access data in S3.
Most applications have to retrieve the entire object and then filter out only the required data for further analysis. S3 Select enables applications to offload the heavy lifting of filtering and accessing data inside objects to Amazon S3. By reducing the volume of data that has to be loaded and processed by your applications, S3 Select can improve the performance of most applications that frequently access data from S3 by up to 400%.
Select works on objects stored in CSV and JSON formats, Apache Parquet format, JSON Arrays, and BZIP2 compression for CSV and JSON objects. CloudWatch Metrics for S3 Select let you monitor S3 Select usage for your applications.
AD Connector, part of AWS Directory Service, is now available in the US East (Ohio), US West (N. California), Asia Pacific (Mumbai), Asia Pacific (Seoul), and Canada (Central) Region
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