Mock sample for your project: Azure SQL Database Datamasking Policies and Rules API

The SAP HANA on Azure Management Client.

Use these APIs to manage Azure Websites resources through the Azure Resource Manager. All task operations conform to the HTTP/1.1 protocol specification and each operation returns an x-ms-request-id header that can be used to obtain information about the request. You must make sure that requests made to these resources are secure. For more information, see https://msdn.microsoft.com/en-us/library/azure/dn790557.aspx.

Use this API to manage the IoTSpaces service instances in your Azure subscription.

REST API for Azure Spring Cloud

Use these REST APIs to get the analytics reports associated with your Azure API Management deployment.

The Container Service Client.

The Compute Management Client.

Azure Feature Exposure Control (AFEC) provides a mechanism for the resource providers to control feature exposure to users. Resource providers typically use this mechanism to provide public/private preview for new features prior to making them generally available. Users need to explicitly register for AFEC features to get access to such functionality.

The Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

The Microsoft Azure Managed Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to programmatically view, control, change, and monitor your entire Azure network centrally and with ease.

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.

AzureBridge Admin Client.

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in AWS. Macie automates the discovery of sensitive data, such as PII and intellectual property, to provide you with insight into the data that your organization stores in AWS. Macie also provides an inventory of your Amazon S3 buckets, which it continually monitors for you. If Macie detects sensitive data or potential data access issues, it generates detailed findings for you to review and act upon as necessary.

Identity and Access Management Identity and Access Management (IAM) is a web service for securely controlling access to Amazon Web Services services. With IAM, you can centrally manage users, security credentials such as access keys, and permissions that control which Amazon Web Services resources users and applications can access. For more information about IAM, see Identity and Access Management (IAM) and the Identity and Access Management User Guide.

API for AWS Elemental MediaLive

Describes all of the AWS IoT 1-Click device-related API operations for the service.
Also provides sample requests, responses, and errors for the supported web services
protocols.

Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.

AWS Elemental MediaPackage

This is the Amazon Rekognition API reference.

Amazon Route 53 is a highly available and scalable Domain Name System (DNS) web service.

This is the Amazon Lookout for Metrics API Reference. For an introduction to the service with tutorials for getting started, visit Amazon Lookout for Metrics Developer Guide.

AWS Elemental MediaConvert