Mock sample for your project: Azure SQL Database API spec

Apis for customer in enterprise agreement migrate to new pricing model or rollback to legacy pricing model.

Edge gateway pool operation endpoints and objects.

Storage operation results.

Provides create, read, update, delete, and failover functionality for Azure SQL Database disaster recovery configurations.

Public IP Address admin endpoints and objects.

Dev Spaces REST API

Logical subnet operation endpoints and objects.

Azure Active Directory Client.

Virtual Network admin operation endpoints and objects.

APIs to manage web application firewall rules for Azure CDN

The DevTest Labs Client.

The Azure Data Catalog Resource Provider Services API.

The Azure Management Groups API enables consolidation of multiple subscriptions/resources into an organizational hierarchy and centrally manage access control, policies, alerting and reporting for those resources.

Azure Customer Lockbox API Reference

Provides create, read, update and delete functionality for Azure SQL Database resources including servers, databases, elastic pools, recommendations, and operations.

The Container Service Client.

The Compute Management 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.