Mock sample for your project: PeeringManagementClient API

Use these REST APIs for performing operations on Diagnostic entity associated with your Azure API Management deployment. Diagnostics are used to log requests/responses in the APIM proxy.

Microsoft Azure Support Resource Provider.

REST API for Azure Blockchain Service

The AzureData management API provides a RESTful set of web APIs to manage Azure Data Resources. For example, register, delete and retrieve a SQL Server, SQL Server registration.

Cognitive Services Management Client

Creates an Azure Data Lake Store account management client.

Azure DevOps Resource Provider

The Azure Instance Metadata Client

The HDInsight Management Client.

Extracts information from forms and images into structured data.

These APIs allow end users to operate on Azure Machine Learning Compute resources. They support the following operations: Create or update a cluster Get a cluster Patch a cluster Delete a cluster Get keys for a cluster Check if updates are available for system services in a cluster Update system services in a cluster Get all clusters in a resource group Get all clusters in a subscription

AWS Elemental MediaPackage

AWS IoT Wireless API documentation

The Application Migration Service service.

Elastic Load Balancing A load balancer can distribute incoming traffic across your EC2 instances. This enables you to increase the availability of your application. The load balancer also monitors the health of its registered instances and ensures that it routes traffic only to healthy instances. You configure your load balancer to accept incoming traffic by specifying one or more listeners, which are configured with a protocol and port number for connections from clients to the load balancer and a protocol and port number for connections from the load balancer to the instances. Elastic Load Balancing supports three types of load balancers: Application Load Balancers, Network Load Balancers, and Classic Load Balancers. You can select a load balancer based on your application needs. For more information, see the Elastic Load Balancing User Guide. This reference covers the 2012-06-01 API, which supports Classic Load Balancers. The 2015-12-01 API supports Application Load Balancers and Network Load Balancers. To get started, create a load balancer with one or more listeners using CreateLoadBalancer. Register your instances with the load balancer using RegisterInstancesWithLoadBalancer. All Elastic Load Balancing operations are idempotent, which means that they complete at most one time. If you repeat an operation, it succeeds with a 200 OK response code.

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.

Fabric location operation endpoints and objects.

AzureBridge Admin Client.

AWS IoT Greengrass seamlessly extends AWS onto physical devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass ensures your devices can respond quickly to local events and operate with intermittent connectivity. AWS IoT Greengrass minimizes the cost of transmitting data to the cloud by allowing you to author AWS Lambda functions that execute locally.

WAF This is the latest version of the WAF API, released in November, 2019. The names of the entities that you use to access this API, like endpoints and namespaces, all have the versioning information added, like "V2" or "v2", to distinguish from the prior version. We recommend migrating your resources to this version, because it has a number of significant improvements. If you used WAF prior to this release, you can't use this WAFV2 API to access any WAF resources that you created before. You can access your old rules, web ACLs, and other WAF resources only through the WAF Classic APIs. The WAF Classic APIs have retained the prior names, endpoints, and namespaces. For information, including how to migrate your WAF resources to this version, see the WAF Developer Guide. WAF is a web application firewall that lets you monitor the HTTP and HTTPS requests that are forwarded to Amazon CloudFront, an Amazon API Gateway REST API, an Application Load Balancer, or an AppSync GraphQL API. WAF also lets you control access to your content. Based on conditions that you specify, such as the IP addresses that requests originate from or the values of query strings, the Amazon API Gateway REST API, CloudFront distribution, the Application Load Balancer, or the AppSync GraphQL API responds to requests either with the requested content or with an HTTP 403 status code (Forbidden). You also can configure CloudFront to return a custom error page when a request is blocked. This API guide is for developers who need detailed information about WAF API actions, data types, and errors. For detailed information about WAF features and an overview of how to use WAF, see the WAF Developer Guide. You can make calls using the endpoints listed in WAF endpoints and quotas. For regional applications, you can use any of the endpoints in the list. A regional application can be an Application Load Balancer (ALB), an Amazon API Gateway REST API, or an AppSync GraphQL API. For Amazon CloudFront applications, you must use the API endpoint listed for US East (N. Virginia): us-east-1. Alternatively, you can use one of the Amazon Web Services SDKs to access an API that's tailored to the programming language or platform that you're using. For more information, see Amazon Web Services SDKs. We currently provide two versions of the WAF API: this API and the prior versions, the classic WAF APIs. This new API provides the same functionality as the older versions, with the following major improvements: You use one API for both global and regional applications. Where you need to distinguish the scope, you specify a Scope parameter and set it to CLOUDFRONT or REGIONAL. You can define a web ACL or rule group with a single call, and update it with a single call. You define all rule specifications in JSON format, and pass them to your rule group or web ACL calls. The limits WAF places on the use of rules more closely reflects the cost of running each type of rule. Rule groups include capacity settings, so you know the maximum cost of a rule group when you use it.

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.

Pinpoint SMS and Voice Messaging public facing APIs