Mock sample for your project: SqlManagementClient 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.

Storage subsystem operation endpoints and objects.

APIs to manage web application firewall rules for Azure CDN

The Admin Storage Management Client.

The Admin Infrastructure Insights Management Client.

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.

Use these REST APIs for performing operations on logger entity Azure API Management deployment.The Logger entity in API Management represents an event sink that you can use to log API Management events. Currently the Logger entity supports logging API Management events to Azure EventHub.

These are the Network Experiment APIs.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These operations enable you to manage role definitions and role assignments. A role definition describes the set of actions that can be performed on resources. A role assignment grants access to Azure Active Directory users.

Use these REST APIs for performing operations on who is going to receive notifications associated with your Azure API Management deployment.

API spec for Microsoft.Security (Azure Security Center) resource provider

Use these REST APIs for managing OAuth2 servers configuration in your Azure API Management deployment. OAuth 2.0 can be used to authorize developer accounts for Azure API Management. For more information refer to How to OAuth2.

Azure Log Analytics API reference for Query Packs management.

The Admin Backup Management Client.

The Application Migration Service service.

IoT Analytics allows you to collect large amounts of device data, process messages, and store them. You can then query the data and run sophisticated analytics on it. IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks and data visualization through integration with Amazon QuickSight. Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of other data from external sources. IoT Analytics automates the steps required to analyze data from IoT devices. IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, you can analyze your data by running queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices.

Azure Application Insights client for ProactiveDetection configurations of a component.

AWS Elemental MediaPackage VOD

Azure Active Directory Client.

Amazon Kinesis Data Streams Service API Reference Amazon Kinesis Data Streams is a managed service that scales elastically for real-time processing of streaming big data.