Mock sample for your project: ServiceFabricManagementClient API

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.

Deployment Admin Client.

The Admin Storage Management Client.

Use these REST APIs for performing operations on User entity in Azure API Management deployment. The User entity in API Management represents the developers that call the APIs of the products to which they are subscribed.

APIs to manage web application firewall rules for Azure CDN

Use these REST APIs for performing operations on Cache entity in your Azure API Management deployment. Azure API Management also allows for caching responses in an external Azure Cache for Redis. For more information refer to External Redis Cache in ApiManagement.

The Azure BatchAI Management API.

Azure Alerts Management Service provides a single pane of glass of alerts across Azure Monitor.

REST API for Azure Blockchain Service

The Admin Storage Management Client.

A client for issuing REST requests to the Azure metrics service.

API for using the Azure IoT Hub Device Provisioning Service features.

The DevTest Labs Client.

The Run Commands Client.

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

ARM Databricks

Creates a Microsoft.DataShare management client.

DataSync DataSync is a managed data transfer service that makes it simpler for you to automate moving data between on-premises storage and Amazon Simple Storage Service (Amazon S3) or Amazon Elastic File System (Amazon EFS). This API interface reference for DataSync contains documentation for a programming interface that you can use to manage DataSync.

Amazon S3 on Outposts provides access to S3 on Outposts operations.

Creates an Azure Data Lake Analytics catalog client.