Mock sample for your project: Elastic Load Balancing API

This is the ACM Private CA API Reference. It provides descriptions, syntax, and usage examples for each of the actions and data types involved in creating and managing private certificate authorities (CA) for your organization. The documentation for each action shows the Query API request parameters and the XML response. Alternatively, you can use one of the AWS SDKs to access an API that's tailored to the programming language or platform that you're using. For more information, see AWS SDKs. Each ACM Private CA API operation has a quota that determines the number of times the operation can be called per second. ACM Private CA throttles API requests at different rates depending on the operation. Throttling means that ACM Private CA rejects an otherwise valid request because the request exceeds the operation's quota for the number of requests per second. When a request is throttled, ACM Private CA returns a ThrottlingException error. ACM Private CA does not guarantee a minimum request rate for APIs. To see an up-to-date list of your ACM Private CA quotas, or to request a quota increase, log into your AWS account and visit the Service Quotas console.

This AWS CodeStar Notifications API Reference provides descriptions and usage examples of the operations and data types for the AWS CodeStar Notifications API. You can use the AWS CodeStar Notifications API to work with the following objects: Notification rules, by calling the following: CreateNotificationRule, which creates a notification rule for a resource in your account. DeleteNotificationRule, which deletes a notification rule. DescribeNotificationRule, which provides information about a notification rule. ListNotificationRules, which lists the notification rules associated with your account. UpdateNotificationRule, which changes the name, events, or targets associated with a notification rule. Subscribe, which subscribes a target to a notification rule. Unsubscribe, which removes a target from a notification rule. Targets, by calling the following: DeleteTarget, which removes a notification rule target (SNS topic) from a notification rule. ListTargets, which lists the targets associated with a notification rule. Events, by calling the following: ListEventTypes, which lists the event types you can include in a notification rule. Tags, by calling the following: ListTagsForResource, which lists the tags already associated with a notification rule in your account. TagResource, which associates a tag you provide with a notification rule in your account. UntagResource, which removes a tag from a notification rule in your account. For information about how to use AWS CodeStar Notifications, see link in the CodeStarNotifications User Guide.

Welcome to the IoT SiteWise API Reference. IoT SiteWise is an Amazon Web Services service that connects Industrial Internet of Things (IIoT) devices to the power of the Amazon Web Services Cloud. For more information, see the IoT SiteWise User Guide. For information about IoT SiteWise quotas, see Quotas in the IoT SiteWise User Guide.

Amazon Elastic Container Service Amazon Elastic Container Service (Amazon ECS) is a highly scalable, fast, container management service that makes it easy to run, stop, and manage Docker containers on a cluster. You can host your cluster on a serverless infrastructure that is managed by Amazon ECS by launching your services or tasks on Fargate. For more control, you can host your tasks on a cluster of Amazon Elastic Compute Cloud (Amazon EC2) instances that you manage. Amazon ECS makes it easy to launch and stop container-based applications with simple API calls, allows you to get the state of your cluster from a centralized service, and gives you access to many familiar Amazon EC2 features. You can use Amazon ECS to schedule the placement of containers across your cluster based on your resource needs, isolation policies, and availability requirements. Amazon ECS eliminates the need for you to operate your own cluster management and configuration management systems or worry about scaling your management infrastructure.

Directory Service Directory Service is a web service that makes it easy for you to setup and run directories in the Amazon Web Services cloud, or connect your Amazon Web Services resources with an existing self-managed Microsoft Active Directory. This guide provides detailed information about Directory Service operations, data types, parameters, and errors. For information about Directory Services features, see Directory Service and the Directory Service Administration Guide. Amazon Web Services provides SDKs that consist of libraries and sample code for various programming languages and platforms (Java, Ruby, .Net, iOS, Android, etc.). The SDKs provide a convenient way to create programmatic access to Directory Service and other Amazon Web Services services. For more information about the Amazon Web Services SDKs, including how to download and install them, see Tools for Amazon Web Services.

Amazon Managed Service for Prometheus

AWS CodeDeploy AWS CodeDeploy is a deployment service that automates application deployments to Amazon EC2 instances, on-premises instances running in your own facility, serverless AWS Lambda functions, or applications in an Amazon ECS service. You can deploy a nearly unlimited variety of application content, such as an updated Lambda function, updated applications in an Amazon ECS service, code, web and configuration files, executables, packages, scripts, multimedia files, and so on. AWS CodeDeploy can deploy application content stored in Amazon S3 buckets, GitHub repositories, or Bitbucket repositories. You do not need to make changes to your existing code before you can use AWS CodeDeploy. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications, without many of the risks associated with error-prone manual deployments. AWS CodeDeploy Components Use the information in this guide to help you work with the following AWS CodeDeploy components: Application : A name that uniquely identifies the application you want to deploy. AWS CodeDeploy uses this name, which functions as a container, to ensure the correct combination of revision, deployment configuration, and deployment group are referenced during a deployment. Deployment group : A set of individual instances, CodeDeploy Lambda deployment configuration settings, or an Amazon ECS service and network details. A Lambda deployment group specifies how to route traffic to a new version of a Lambda function. An Amazon ECS deployment group specifies the service created in Amazon ECS to deploy, a load balancer, and a listener to reroute production traffic to an updated containerized application. An EC2/On-premises deployment group contains individually tagged instances, Amazon EC2 instances in Amazon EC2 Auto Scaling groups, or both. All deployment groups can specify optional trigger, alarm, and rollback settings. Deployment configuration : A set of deployment rules and deployment success and failure conditions used by AWS CodeDeploy during a deployment. Deployment : The process and the components used when updating a Lambda function, a containerized application in an Amazon ECS service, or of installing content on one or more instances. Application revisions : For an AWS Lambda deployment, this is an AppSpec file that specifies the Lambda function to be updated and one or more functions to validate deployment lifecycle events. For an Amazon ECS deployment, this is an AppSpec file that specifies the Amazon ECS task definition, container, and port where production traffic is rerouted. For an EC2/On-premises deployment, this is an archive file that contains source content—source code, webpages, executable files, and deployment scripts—along with an AppSpec file. Revisions are stored in Amazon S3 buckets or GitHub repositories. For Amazon S3, a revision is uniquely identified by its Amazon S3 object key and its ETag, version, or both. For GitHub, a revision is uniquely identified by its commit ID. This guide also contains information to help you get details about the instances in your deployments, to make on-premises instances available for AWS CodeDeploy deployments, to get details about a Lambda function deployment, and to get details about Amazon ECS service deployments. AWS CodeDeploy Information Resources AWS CodeDeploy User Guide AWS CodeDeploy API Reference Guide AWS CLI Reference for AWS CodeDeploy AWS CodeDeploy Developer Forum

Amazon Elastic Container Registry Amazon Elastic Container Registry (Amazon ECR) is a managed container image registry service. Customers can use the familiar Docker CLI, or their preferred client, to push, pull, and manage images. Amazon ECR provides a secure, scalable, and reliable registry for your Docker or Open Container Initiative (OCI) images. Amazon ECR supports private repositories with resource-based permissions using IAM so that specific users or Amazon EC2 instances can access repositories and images. Amazon ECR has service endpoints in each supported Region. For more information, see Amazon ECR endpoints in the Amazon Web Services General Reference.

Amazon EMR is a web service that makes it easier to process large amounts of data efficiently. Amazon EMR uses Hadoop processing combined with several Amazon Web Services services to do tasks such as web indexing, data mining, log file analysis, machine learning, scientific simulation, and data warehouse management.

AWS CodePipeline Overview This is the AWS CodePipeline API Reference. This guide provides descriptions of the actions and data types for AWS CodePipeline. Some functionality for your pipeline can only be configured through the API. For more information, see the AWS CodePipeline User Guide. You can use the AWS CodePipeline API to work with pipelines, stages, actions, and transitions. Pipelines are models of automated release processes. Each pipeline is uniquely named, and consists of stages, actions, and transitions. You can work with pipelines by calling: CreatePipeline, which creates a uniquely named pipeline. DeletePipeline, which deletes the specified pipeline. GetPipeline, which returns information about the pipeline structure and pipeline metadata, including the pipeline Amazon Resource Name (ARN). GetPipelineExecution, which returns information about a specific execution of a pipeline. GetPipelineState, which returns information about the current state of the stages and actions of a pipeline. ListActionExecutions, which returns action-level details for past executions. The details include full stage and action-level details, including individual action duration, status, any errors that occurred during the execution, and input and output artifact location details. ListPipelines, which gets a summary of all of the pipelines associated with your account. ListPipelineExecutions, which gets a summary of the most recent executions for a pipeline. StartPipelineExecution, which runs the most recent revision of an artifact through the pipeline. StopPipelineExecution, which stops the specified pipeline execution from continuing through the pipeline. UpdatePipeline, which updates a pipeline with edits or changes to the structure of the pipeline. Pipelines include stages. Each stage contains one or more actions that must complete before the next stage begins. A stage results in success or failure. If a stage fails, the pipeline stops at that stage and remains stopped until either a new version of an artifact appears in the source location, or a user takes action to rerun the most recent artifact through the pipeline. You can call GetPipelineState, which displays the status of a pipeline, including the status of stages in the pipeline, or GetPipeline, which returns the entire structure of the pipeline, including the stages of that pipeline. For more information about the structure of stages and actions, see AWS CodePipeline Pipeline Structure Reference. Pipeline stages include actions that are categorized into categories such as source or build actions performed in a stage of a pipeline. For example, you can use a source action to import artifacts into a pipeline from a source such as Amazon S3. Like stages, you do not work with actions directly in most cases, but you do define and interact with actions when working with pipeline operations such as CreatePipeline and GetPipelineState. Valid action categories are: Source Build Test Deploy Approval Invoke Pipelines also include transitions, which allow the transition of artifacts from one stage to the next in a pipeline after the actions in one stage complete. You can work with transitions by calling: DisableStageTransition, which prevents artifacts from transitioning to the next stage in a pipeline. EnableStageTransition, which enables transition of artifacts between stages in a pipeline. Using the API to integrate with AWS CodePipeline For third-party integrators or developers who want to create their own integrations with AWS CodePipeline, the expected sequence varies from the standard API user. To integrate with AWS CodePipeline, developers need to work with the following items: Jobs, which are instances of an action. For example, a job for a source action might import a revision of an artifact from a source. You can work with jobs by calling: AcknowledgeJob, which confirms whether a job worker has received the specified job. GetJobDetails, which returns the details of a job. PollForJobs, which determines whether there are any jobs to act on. PutJobFailureResult, which provides details of a job failure. PutJobSuccessResult, which provides details of a job success. Third party jobs, which are instances of an action created by a partner action and integrated into AWS CodePipeline. Partner actions are created by members of the AWS Partner Network. You can work with third party jobs by calling: AcknowledgeThirdPartyJob, which confirms whether a job worker has received the specified job. GetThirdPartyJobDetails, which requests the details of a job for a partner action. PollForThirdPartyJobs, which determines whether there are any jobs to act on. PutThirdPartyJobFailureResult, which provides details of a job failure. PutThirdPartyJobSuccessResult, which provides details of a job success.

Amazon Athena is an interactive query service that lets you use standard SQL to analyze data directly in Amazon S3. You can point Athena at your data in Amazon S3 and run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to set up or manage. You pay only for the queries you run. Athena scales automatically—executing queries in parallel—so results are fast, even with large datasets and complex queries. For more information, see What is Amazon Athena in the Amazon Athena User Guide. If you connect to Athena using the JDBC driver, use version 1.1.0 of the driver or later with the Amazon Athena API. Earlier version drivers do not support the API. For more information and to download the driver, see Accessing Amazon Athena with JDBC. For code samples using the Amazon Web Services SDK for Java, see Examples and Code Samples in the Amazon Athena User Guide.

You use the AmazonCloudSearch2013 API to upload documents to a search domain and search those documents. The endpoints for submitting UploadDocuments, Search, and Suggest requests are domain-specific. To get the endpoints for your domain, use the Amazon CloudSearch configuration service DescribeDomains action. The domain endpoints are also displayed on the domain dashboard in the Amazon CloudSearch console. You submit suggest requests to the search endpoint. For more information, see the Amazon CloudSearch Developer Guide.

The Azure Storage Management API.

The Admin Fabric Management Client.

Amazon Kinesis Data Firehose API Reference Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Elasticsearch Service (Amazon ES), Amazon Redshift, and Splunk.

Storage subsystem operation endpoints and objects.

Amazon Macie Classic Amazon Macie Classic is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Macie Classic recognizes sensitive data such as personally identifiable information (PII) or intellectual property, and provides you with dashboards and alerts that give visibility into how this data is being accessed or moved. For more information, see the Amazon Macie Classic User Guide.

With Application Auto Scaling, you can configure automatic scaling for the following resources: Amazon AppStream 2.0 fleets Amazon Aurora Replicas Amazon Comprehend document classification and entity recognizer endpoints Amazon DynamoDB tables and global secondary indexes throughput capacity Amazon ECS services Amazon ElastiCache for Redis clusters (replication groups) Amazon EMR clusters Amazon Keyspaces (for Apache Cassandra) tables Lambda function provisioned concurrency Amazon Managed Streaming for Apache Kafka broker storage Amazon SageMaker endpoint variants Spot Fleet (Amazon EC2) requests Custom resources provided by your own applications or services API Summary The Application Auto Scaling service API includes three key sets of actions: Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets. Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history. Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the RegisterScalableTarget API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling. To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.

Amazon Web Services Snow Device Management documentation.

Config Config provides a way to keep track of the configurations of all the Amazon Web Services resources associated with your Amazon Web Services account. You can use Config to get the current and historical configurations of each Amazon Web Services resource and also to get information about the relationship between the resources. An Amazon Web Services resource can be an Amazon Compute Cloud (Amazon EC2) instance, an Elastic Block Store (EBS) volume, an elastic network Interface (ENI), or a security group. For a complete list of resources currently supported by Config, see Supported Amazon Web Services resources. You can access and manage Config through the Amazon Web Services Management Console, the Amazon Web Services Command Line Interface (Amazon Web Services CLI), the Config API, or the Amazon Web Services SDKs for Config. This reference guide contains documentation for the Config API and the Amazon Web Services CLI commands that you can use to manage Config. The Config API uses the Signature Version 4 protocol for signing requests. For more information about how to sign a request with this protocol, see Signature Version 4 Signing Process. For detailed information about Config features and their associated actions or commands, as well as how to work with Amazon Web Services Management Console, see What Is Config in the Config Developer Guide.

Azure client for Workbook.

Operations and objects for transcribing speech to text.

The Admin Commerce Management Client.