Mock sample for your project: Amazon OpenSearch Service API

AWS CloudFormation CloudFormation allows you to create and manage Amazon Web Services infrastructure deployments predictably and repeatedly. You can use CloudFormation to leverage Amazon Web Services products, such as Amazon Elastic Compute Cloud, Amazon Elastic Block Store, Amazon Simple Notification Service, Elastic Load Balancing, and Auto Scaling to build highly-reliable, highly scalable, cost-effective applications without creating or configuring the underlying Amazon Web Services infrastructure. With CloudFormation, you declare all of your resources and dependencies in a template file. The template defines a collection of resources as a single unit called a stack. CloudFormation creates and deletes all member resources of the stack together and manages all dependencies between the resources for you. For more information about CloudFormation, see the CloudFormation Product Page. CloudFormation makes use of other Amazon Web Services products. If you need additional technical information about a specific Amazon Web Services product, you can find the product's technical documentation at docs.aws.amazon.com .

Provides APIs for creating and managing Amazon SageMaker resources. Other Resources: Amazon SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

Amazon Data Lifecycle Manager With Amazon Data Lifecycle Manager, you can manage the lifecycle of your Amazon Web Services resources. You create lifecycle policies, which are used to automate operations on the specified resources. Amazon DLM supports Amazon EBS volumes and snapshots. For information about using Amazon DLM with Amazon EBS, see Automating the Amazon EBS Snapshot Lifecycle in the Amazon EC2 User Guide.

The Amazon Braket API Reference provides information about the operations and structures supported in Amazon Braket.

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.

The Amazon AppIntegrations service enables you to configure and reuse connections to external applications. For information about how you can use external applications with Amazon Connect, see Set up pre-built integrations in the Amazon Connect Administrator Guide.

Batch Using Batch, you can run batch computing workloads on the Cloud. Batch computing is a common means for developers, scientists, and engineers to access large amounts of compute resources. Batch uses the advantages of this computing workload to remove the undifferentiated heavy lifting of configuring and managing required infrastructure. At the same time, it also adopts a familiar batch computing software approach. Given these advantages, Batch can help you to efficiently provision resources in response to jobs submitted, thus effectively helping you to eliminate capacity constraints, reduce compute costs, and deliver your results more quickly. As a fully managed service, Batch can run batch computing workloads of any scale. Batch automatically provisions compute resources and optimizes workload distribution based on the quantity and scale of your specific workloads. With Batch, there's no need to install or manage batch computing software. This means that you can focus your time and energy on analyzing results and solving your specific problems.

Amazon Elastic Kubernetes Service (Amazon EKS) is a managed service that makes it easy for you to run Kubernetes on Amazon Web Services without needing to stand up or maintain your own Kubernetes control plane. Kubernetes is an open-source system for automating the deployment, scaling, and management of containerized applications. Amazon EKS runs up-to-date versions of the open-source Kubernetes software, so you can use all the existing plugins and tooling from the Kubernetes community. Applications running on Amazon EKS are fully compatible with applications running on any standard Kubernetes environment, whether running in on-premises data centers or public clouds. This means that you can easily migrate any standard Kubernetes application to Amazon EKS without any code modification required.

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.

Amazon DynamoDB Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB lets you offload the administrative burdens of operating and scaling a distributed database, so that you don't have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling. With DynamoDB, you can create database tables that can store and retrieve any amount of data, and serve any level of request traffic. You can scale up or scale down your tables' throughput capacity without downtime or performance degradation, and use the AWS Management Console to monitor resource utilization and performance metrics. DynamoDB automatically spreads the data and traffic for your tables over a sufficient number of servers to handle your throughput and storage requirements, while maintaining consistent and fast performance. All of your data is stored on solid state disks (SSDs) and automatically replicated across multiple Availability Zones in an AWS region, providing built-in high availability and data durability.

Amazon S3 Glacier (Glacier) is a storage solution for "cold data." Glacier is an extremely low-cost storage service that provides secure, durable, and easy-to-use storage for data backup and archival. With Glacier, customers can store their data cost effectively for months, years, or decades. Glacier also enables customers to offload the administrative burdens of operating and scaling storage to AWS, so they don't have to worry about capacity planning, hardware provisioning, data replication, hardware failure and recovery, or time-consuming hardware migrations. Glacier is a great storage choice when low storage cost is paramount and your data is rarely retrieved. If your application requires fast or frequent access to your data, consider using Amazon S3. For more information, see Amazon Simple Storage Service (Amazon S3). You can store any kind of data in any format. There is no maximum limit on the total amount of data you can store in Glacier. If you are a first-time user of Glacier, we recommend that you begin by reading the following sections in the Amazon S3 Glacier Developer Guide : What is Amazon S3 Glacier - This section of the Developer Guide describes the underlying data model, the operations it supports, and the AWS SDKs that you can use to interact with the service. Getting Started with Amazon S3 Glacier - The Getting Started section walks you through the process of creating a vault, uploading archives, creating jobs to download archives, retrieving the job output, and deleting archives.

Amazon Elastic Container Registry Public Amazon Elastic Container Registry (Amazon ECR) is a managed container image registry service. Amazon ECR provides both public and private registries to host your container images. You 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 public repositories with this API. For information about the Amazon ECR API for private repositories, see Amazon Elastic Container Registry API Reference.

Use these REST APIs for performing operations on Identity Provider entity associated with your Azure API Management deployment. Setting up an external Identity Provider for authentication can help you manage the developer portal logins using the OAuth2 flow.

Resource provider health operation endpoints and objects.

Amazon Elasticsearch Configuration Service Use the Amazon Elasticsearch Configuration API to create, configure, and manage Elasticsearch domains. For sample code that uses the Configuration API, see the Amazon Elasticsearch Service Developer Guide. The guide also contains sample code for sending signed HTTP requests to the Elasticsearch APIs. The endpoint for configuration service requests is region-specific: es. region.amazonaws.com. For example, es.us-east-1.amazonaws.com. For a current list of supported regions and endpoints, see Regions and Endpoints.

REST APIs for Azure Advisor

Amazon Web Services Transfer Family is a fully managed service that enables the transfer of files over the File Transfer Protocol (FTP), File Transfer Protocol over SSL (FTPS), or Secure Shell (SSH) File Transfer Protocol (SFTP) directly into and out of Amazon Simple Storage Service (Amazon S3). Amazon Web Services helps you seamlessly migrate your file transfer workflows to Amazon Web Services Transfer Family by integrating with existing authentication systems, and providing DNS routing with Amazon Route 53 so nothing changes for your customers and partners, or their applications. With your data in Amazon S3, you can use it with Amazon Web Services services for processing, analytics, machine learning, and archiving. Getting started with Amazon Web Services Transfer Family is easy since there is no infrastructure to buy and set up.

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.

Provides translation between one source language and another of the same set of languages.

Use these REST APIs for performing operations on tenant entity associated with your Azure API Management deployment. Using this entity you can manage properties and configuration that apply to the entire API Management service instance.

AWS CodeCommit This is the AWS CodeCommit API Reference. This reference provides descriptions of the operations and data types for AWS CodeCommit API along with usage examples. You can use the AWS CodeCommit API to work with the following objects: Repositories, by calling the following: BatchGetRepositories, which returns information about one or more repositories associated with your AWS account. CreateRepository, which creates an AWS CodeCommit repository. DeleteRepository, which deletes an AWS CodeCommit repository. GetRepository, which returns information about a specified repository. ListRepositories, which lists all AWS CodeCommit repositories associated with your AWS account. UpdateRepositoryDescription, which sets or updates the description of the repository. UpdateRepositoryName, which changes the name of the repository. If you change the name of a repository, no other users of that repository can access it until you send them the new HTTPS or SSH URL to use. Branches, by calling the following: CreateBranch, which creates a branch in a specified repository. DeleteBranch, which deletes the specified branch in a repository unless it is the default branch. GetBranch, which returns information about a specified branch. ListBranches, which lists all branches for a specified repository. UpdateDefaultBranch, which changes the default branch for a repository. Files, by calling the following: DeleteFile, which deletes the content of a specified file from a specified branch. GetBlob, which returns the base-64 encoded content of an individual Git blob object in a repository. GetFile, which returns the base-64 encoded content of a specified file. GetFolder, which returns the contents of a specified folder or directory. PutFile, which adds or modifies a single file in a specified repository and branch. Commits, by calling the following: BatchGetCommits, which returns information about one or more commits in a repository. CreateCommit, which creates a commit for changes to a repository. GetCommit, which returns information about a commit, including commit messages and author and committer information. GetDifferences, which returns information about the differences in a valid commit specifier (such as a branch, tag, HEAD, commit ID, or other fully qualified reference). Merges, by calling the following: BatchDescribeMergeConflicts, which returns information about conflicts in a merge between commits in a repository. CreateUnreferencedMergeCommit, which creates an unreferenced commit between two branches or commits for the purpose of comparing them and identifying any potential conflicts. DescribeMergeConflicts, which returns information about merge conflicts between the base, source, and destination versions of a file in a potential merge. GetMergeCommit, which returns information about the merge between a source and destination commit. GetMergeConflicts, which returns information about merge conflicts between the source and destination branch in a pull request. GetMergeOptions, which returns information about the available merge options between two branches or commit specifiers. MergeBranchesByFastForward, which merges two branches using the fast-forward merge option. MergeBranchesBySquash, which merges two branches using the squash merge option. MergeBranchesByThreeWay, which merges two branches using the three-way merge option. Pull requests, by calling the following: CreatePullRequest, which creates a pull request in a specified repository. CreatePullRequestApprovalRule, which creates an approval rule for a specified pull request. DeletePullRequestApprovalRule, which deletes an approval rule for a specified pull request. DescribePullRequestEvents, which returns information about one or more pull request events. EvaluatePullRequestApprovalRules, which evaluates whether a pull request has met all the conditions specified in its associated approval rules. GetCommentsForPullRequest, which returns information about comments on a specified pull request. GetPullRequest, which returns information about a specified pull request. GetPullRequestApprovalStates, which returns information about the approval states for a specified pull request. GetPullRequestOverrideState, which returns information about whether approval rules have been set aside (overriden) for a pull request, and if so, the Amazon Resource Name (ARN) of the user or identity that overrode the rules and their requirements for the pull request. ListPullRequests, which lists all pull requests for a repository. MergePullRequestByFastForward, which merges the source destination branch of a pull request into the specified destination branch for that pull request using the fast-forward merge option. MergePullRequestBySquash, which merges the source destination branch of a pull request into the specified destination branch for that pull request using the squash merge option. MergePullRequestByThreeWay. which merges the source destination branch of a pull request into the specified destination branch for that pull request using the three-way merge option. OverridePullRequestApprovalRules, which sets aside all approval rule requirements for a pull request. PostCommentForPullRequest, which posts a comment to a pull request at the specified line, file, or request. UpdatePullRequestApprovalRuleContent, which updates the structure of an approval rule for a pull request. UpdatePullRequestApprovalState, which updates the state of an approval on a pull request. UpdatePullRequestDescription, which updates the description of a pull request. UpdatePullRequestStatus, which updates the status of a pull request. UpdatePullRequestTitle, which updates the title of a pull request. Approval rule templates, by calling the following: AssociateApprovalRuleTemplateWithRepository, which associates a template with a specified repository. After the template is associated with a repository, AWS CodeCommit creates approval rules that match the template conditions on every pull request created in the specified repository. BatchAssociateApprovalRuleTemplateWithRepositories, which associates a template with one or more specified repositories. After the template is associated with a repository, AWS CodeCommit creates approval rules that match the template conditions on every pull request created in the specified repositories. BatchDisassociateApprovalRuleTemplateFromRepositories, which removes the association between a template and specified repositories so that approval rules based on the template are not automatically created when pull requests are created in those repositories. CreateApprovalRuleTemplate, which creates a template for approval rules that can then be associated with one or more repositories in your AWS account. DeleteApprovalRuleTemplate, which deletes the specified template. It does not remove approval rules on pull requests already created with the template. DisassociateApprovalRuleTemplateFromRepository, which removes the association between a template and a repository so that approval rules based on the template are not automatically created when pull requests are created in the specified repository. GetApprovalRuleTemplate, which returns information about an approval rule template. ListApprovalRuleTemplates, which lists all approval rule templates in the AWS Region in your AWS account. ListAssociatedApprovalRuleTemplatesForRepository, which lists all approval rule templates that are associated with a specified repository. ListRepositoriesForApprovalRuleTemplate, which lists all repositories associated with the specified approval rule template. UpdateApprovalRuleTemplateDescription, which updates the description of an approval rule template. UpdateApprovalRuleTemplateName, which updates the name of an approval rule template. UpdateApprovalRuleTemplateContent, which updates the content of an approval rule template. Comments in a repository, by calling the following: DeleteCommentContent, which deletes the content of a comment on a commit in a repository. GetComment, which returns information about a comment on a commit. GetCommentReactions, which returns information about emoji reactions to comments. GetCommentsForComparedCommit, which returns information about comments on the comparison between two commit specifiers in a repository. PostCommentForComparedCommit, which creates a comment on the comparison between two commit specifiers in a repository. PostCommentReply, which creates a reply to a comment. PutCommentReaction, which creates or updates an emoji reaction to a comment. UpdateComment, which updates the content of a comment on a commit in a repository. Tags used to tag resources in AWS CodeCommit (not Git tags), by calling the following: ListTagsForResource, which gets information about AWS tags for a specified Amazon Resource Name (ARN) in AWS CodeCommit. TagResource, which adds or updates tags for a resource in AWS CodeCommit. UntagResource, which removes tags for a resource in AWS CodeCommit. Triggers, by calling the following: GetRepositoryTriggers, which returns information about triggers configured for a repository. PutRepositoryTriggers, which replaces all triggers for a repository and can be used to create or delete triggers. TestRepositoryTriggers, which tests the functionality of a repository trigger by sending data to the trigger target. For information about how to use AWS CodeCommit, see the AWS CodeCommit User Guide.

APIs for workload monitoring

The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.