Bump github.com/aws/aws-sdk-go from 1.25.31 to 1.25.48

[dependabot-preview]

Bumps github.com/aws/aws-sdk-go from 1.25.31 to 1.25.48.

Release notes

Sourced from github.com/aws/aws-sdk-go's releases.

Release v1.25.48 (2019-12-05)

Service Client Updates

• service/apigatewayv2: Updates service API and documentation
  • Amazon API Gateway now supports HTTP APIs (beta), enabling customers to quickly build high performance RESTful APIs that are up to 71% cheaper than REST APIs also available from API Gateway. HTTP APIs are optimized for building APIs that proxy to AWS Lambda functions or HTTP backends, making them ideal for serverless workloads. Using HTTP APIs, you can secure your APIs using OIDC and OAuth 2 out of box, quickly build web applications using a simple CORS experience, and get started immediately with automatic deployment and simple create workflows.
• service/kinesis-video-signaling: Adds new service
• service/kinesisvideo: Updates service API, documentation, and paginators
  • Introduces management of signaling channels for Kinesis Video Streams.

Release v1.25.47 (2019-12-04)

Service Client Updates

• service/application-autoscaling: Updates service API and documentation
• service/ebs: Adds new service
• service/lambda: Updates service API, documentation, and paginators
  • Added the ProvisionedConcurrency type and operations. Allocate provisioned concurrency to enable your function to scale up without fluctuations in latency. Use PutProvisionedConcurrencyConfig to configure provisioned concurrency on a version of a function, or on an alias.
• service/rds: Updates service API, documentation, and paginators
  • This release adds support for the Amazon RDS Proxy
• service/rekognition: Updates service API, documentation, waiters, and paginators
  • This SDK Release introduces APIs for Amazon Rekognition Custom Labels feature (CreateProjects, CreateProjectVersion,DescribeProjects, DescribeProjectVersions, StartProjectVersion, StopProjectVersion and DetectCustomLabels). Also new is AugmentedAI (Human In The Loop) Support for DetectModerationLabels in Amazon Rekognition.
• service/sagemaker: Updates service API, documentation, waiters, and paginators
  • You can now use SageMaker Autopilot for automatically training and tuning candidate models using a combination of various feature engineering, ML algorithms, and hyperparameters determined from the user's input data. SageMaker Automatic Model Tuning now supports tuning across multiple algorithms. With Amazon SageMaker Experiments users can create Experiments, ExperimentTrials, and ExperimentTrialComponents to track, organize, and evaluate their ML training jobs. With Amazon SageMaker Debugger, users can easily debug training jobs using a number of pre-built rules provided by Amazon SageMaker, or build custom rules. With Amazon SageMaker Processing, users can run on-demand, distributed, and fully managed jobs for data pre- or post- processing or model evaluation. With Amazon SageMaker Model Monitor, a user can create MonitoringSchedules to automatically monitor endpoints to detect data drift and other issues and get alerted on them. This release also includes the preview version of Amazon SageMaker Studio with Domains, UserProfiles, and Apps. This release also includes the preview version of Amazon Augmented AI to easily implement human review of machine learning predictions by creating FlowDefinitions, HumanTaskUis, and HumanLoops.
• service/states: Updates service API and documentation
  • This release of the AWS Step Functions SDK introduces support for Express Workflows.

Release v1.25.46 (2019-12-03)

Service Client Updates

• service/codeguru-reviewer: Adds new service
• service/codeguruprofiler: Adds new service
• service/compute-optimizer: Adds new service
• service/ec2: Updates service API and documentation
  • This release adds support for the following features: 1. An option to enable acceleration for Site-to-Site VPN connections, to improve connection performance by leveraging AWS Global Accelerator; 2. Inf1 instances featuring up to 16 AWS Inferentia chips, custom-built for ML inference applications to deliver low latency and high throughput performance. Use Inf1 instances to run high scale ML inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection at the lowest cost in the cloud. Inf1 instances will soon be available for use with Amazon SageMaker, Amazon EKS and Amazon ECS. To get started, see https://aws.amazon.com/ec2/instance-types/Inf1; 3. The ability to associate route tables with internet gateways and virtual private gateways, and define routes to insert network and security virtual appliances in the path of inbound and outbound traffic. For more information on Amazon VPC Ingress Routing, see https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Route_Tables.html#gateway-route-table; 4. AWS Local Zones that place compute, storage, database, and other select services closer to you for applications that require very low latency to your end-users. AWS Local Zones also allow you to seamlessly connect to the full range of services in the AWS Region through the same APIs and tool sets; 5. Launching and viewing EC2 instances and EBS volumes running locally in Outposts. This release also introduces a new local gateway (LGW) with Outposts to enable connectivity between Outposts and local on-premises networks as well as the internet; 6. Peering Transit Gateways between regions simplifying creation of secure and private global networks on AWS; 7. Transit Gateway Multicast, enabling multicast routing within and between VPCs using Transit Gateway as a multicast router.
• service/ecs: Updates service API, documentation, and paginators
  • This release supports ECS Capacity Providers, Fargate Spot, and ECS Cluster Auto Scaling. These features enable new ways for ECS to manage compute capacity used by tasks.
• service/eks: Updates service API, documentation, and paginators
• service/es: Updates service API and documentation
  • UltraWarm storage provides a cost-effective way to store large amounts of read-only data on Amazon Elasticsearch Service. Rather than attached storage, UltraWarm nodes use Amazon S3 and a sophisticated caching solution to improve performance. For indices that you are not actively writing to and query less frequently, UltraWarm storage offers significantly lower costs per GiB. In Elasticsearch, these warm indices behave just like any other index. You can query them using the same APIs or use them to create dashboards in Kibana.
• service/frauddetector: Adds new service
• service/kendra: Adds new service
  • It is a preview launch of Amazon Kendra. Amazon Kendra is a managed, highly accurate and easy to use enterprise search service that is powered by machine learning.
• service/networkmanager: Adds new service
• service/outposts: Adds new service
• service/s3: Updates service documentation and examples
  • Amazon S3 Access Points is a new S3 feature that simplifies managing data access at scale for shared data sets on Amazon S3. Access Points provide a customizable way to access the objects in a bucket, with a unique hostname and access policy that enforces the specific permissions and network controls for any request made through the access point. This represents a new way of provisioning access to shared data sets.
• service/s3control: Updates service documentation

... (truncated)

Changelog

Sourced from github.com/aws/aws-sdk-go's changelog.

Release v1.25.48 (2019-12-05)

Service Client Updates

• service/apigatewayv2: Updates service API and documentation
  • Amazon API Gateway now supports HTTP APIs (beta), enabling customers to quickly build high performance RESTful APIs that are up to 71% cheaper than REST APIs also available from API Gateway. HTTP APIs are optimized for building APIs that proxy to AWS Lambda functions or HTTP backends, making them ideal for serverless workloads. Using HTTP APIs, you can secure your APIs using OIDC and OAuth 2 out of box, quickly build web applications using a simple CORS experience, and get started immediately with automatic deployment and simple create workflows.
• service/kinesis-video-signaling: Adds new service
• service/kinesisvideo: Updates service API, documentation, and paginators
  • Introduces management of signaling channels for Kinesis Video Streams.

Release v1.25.47 (2019-12-04)

Service Client Updates

• service/application-autoscaling: Updates service API and documentation
• service/ebs: Adds new service
• service/lambda: Updates service API, documentation, and paginators
  • • Added the ProvisionedConcurrency type and operations. Allocate provisioned concurrency to enable your function to scale up without fluctuations in latency. Use PutProvisionedConcurrencyConfig to configure provisioned concurrency on a version of a function, or on an alias.
• service/rds: Updates service API, documentation, and paginators
  • This release adds support for the Amazon RDS Proxy
• service/rekognition: Updates service API, documentation, waiters, and paginators
  • This SDK Release introduces APIs for Amazon Rekognition Custom Labels feature (CreateProjects, CreateProjectVersion,DescribeProjects, DescribeProjectVersions, StartProjectVersion, StopProjectVersion and DetectCustomLabels). Also new is AugmentedAI (Human In The Loop) Support for DetectModerationLabels in Amazon Rekognition.
• service/sagemaker: Updates service API, documentation, waiters, and paginators
  • You can now use SageMaker Autopilot for automatically training and tuning candidate models using a combination of various feature engineering, ML algorithms, and hyperparameters determined from the user's input data. SageMaker Automatic Model Tuning now supports tuning across multiple algorithms. With Amazon SageMaker Experiments users can create Experiments, ExperimentTrials, and ExperimentTrialComponents to track, organize, and evaluate their ML training jobs. With Amazon SageMaker Debugger, users can easily debug training jobs using a number of pre-built rules provided by Amazon SageMaker, or build custom rules. With Amazon SageMaker Processing, users can run on-demand, distributed, and fully managed jobs for data pre- or post- processing or model evaluation. With Amazon SageMaker Model Monitor, a user can create MonitoringSchedules to automatically monitor endpoints to detect data drift and other issues and get alerted on them. This release also includes the preview version of Amazon SageMaker Studio with Domains, UserProfiles, and Apps. This release also includes the preview version of Amazon Augmented AI to easily implement human review of machine learning predictions by creating FlowDefinitions, HumanTaskUis, and HumanLoops.
• service/states: Updates service API and documentation
  • This release of the AWS Step Functions SDK introduces support for Express Workflows.

Release v1.25.46 (2019-12-03)

Service Client Updates

• service/codeguru-reviewer: Adds new service
• service/codeguruprofiler: Adds new service
• service/compute-optimizer: Adds new service
• service/ec2: Updates service API and documentation
  • This release adds support for the following features: 1. An option to enable acceleration for Site-to-Site VPN connections, to improve connection performance by leveraging AWS Global Accelerator; 2. Inf1 instances featuring up to 16 AWS Inferentia chips, custom-built for ML inference applications to deliver low latency and high throughput performance. Use Inf1 instances to run high scale ML inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection at the lowest cost in the cloud. Inf1 instances will soon be available for use with Amazon SageMaker, Amazon EKS and Amazon ECS. To get started, see https://aws.amazon.com/ec2/instance-types/Inf1; 3. The ability to associate route tables with internet gateways and virtual private gateways, and define routes to insert network and security virtual appliances in the path of inbound and outbound traffic. For more information on Amazon VPC Ingress Routing, see https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Route_Tables.html#gateway-route-table; 4. AWS Local Zones that place compute, storage, database, and other select services closer to you for applications that require very low latency to your end-users. AWS Local Zones also allow you to seamlessly connect to the full range of services in the AWS Region through the same APIs and tool sets; 5. Launching and viewing EC2 instances and EBS volumes running locally in Outposts. This release also introduces a new local gateway (LGW) with Outposts to enable connectivity between Outposts and local on-premises networks as well as the internet; 6. Peering Transit Gateways between regions simplifying creation of secure and private global networks on AWS; 7. Transit Gateway Multicast, enabling multicast routing within and between VPCs using Transit Gateway as a multicast router.
• service/ecs: Updates service API, documentation, and paginators
  • This release supports ECS Capacity Providers, Fargate Spot, and ECS Cluster Auto Scaling. These features enable new ways for ECS to manage compute capacity used by tasks.
• service/eks: Updates service API, documentation, and paginators
• service/es: Updates service API and documentation
  • UltraWarm storage provides a cost-effective way to store large amounts of read-only data on Amazon Elasticsearch Service. Rather than attached storage, UltraWarm nodes use Amazon S3 and a sophisticated caching solution to improve performance. For indices that you are not actively writing to and query less frequently, UltraWarm storage offers significantly lower costs per GiB. In Elasticsearch, these warm indices behave just like any other index. You can query them using the same APIs or use them to create dashboards in Kibana.
• service/frauddetector: Adds new service
• service/kendra: Adds new service
  • It is a preview launch of Amazon Kendra. Amazon Kendra is a managed, highly accurate and easy to use enterprise search service that is powered by machine learning.
• service/networkmanager: Adds new service
• service/outposts: Adds new service
• service/s3: Updates service documentation and examples
  • Amazon S3 Access Points is a new S3 feature that simplifies managing data access at scale for shared data sets on Amazon S3. Access Points provide a customizable way to access the objects in a bucket, with a unique hostname and access policy that enforces the specific permissions and network controls for any request made through the access point. This represents a new way of provisioning access to shared data sets.
• service/s3control: Updates service documentation
  • Amazon S3 Access Points is a new S3 feature that simplifies managing data access at scale for shared data sets on Amazon S3. Access Points provide a customizable way to access the objects in a bucket, with a unique hostname and access policy that enforces the specific permissions and network controls for any request made through the access point. This represents a new way of provisioning access to shared data sets.

... (truncated)

Commits

• f421e23 Release v1.25.48 (2019-12-05)
• 30425a4 Release v1.25.47 (2019-12-04) (#2987)
• a5379c3 Release v1.25.46 (2019-12-03) (#2986)
• 9c9db07 service/s3: Add support for Access Point resources (#2985)
• 1f18ce7 Release v1.25.45 (2019-12-02) (#2983)
• 3724cb0 Release v1.25.44 (2019-12-02) (#2982)
• eed38bc Release v1.25.43 (2019-11-26) (#2979)
• 98babde Release v1.25.42 (2019-11-25) (#2977)
• 9f236cf service/wafv2: new service release (#2976)
• 646ede7 aws/credentials/processcreds: Increase the default max buffer size (#2957)
• Additional commits viewable in compare view

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