Mock sample for your project: ApiManagementClient API

Azure Digital Twins Client for managing DigitalTwinsInstance

Provides read, delete, and failover functionality for Azure SQL Database replication links.

Edge gateway pool operation endpoints and objects.

Azure Log Analytics API reference for Query Packs management.

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

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.

Use these REST APIs for performing operations on API entity and their Operations associated with your Azure API Management deployment.

REST APIs for Azure Active Directory Connect Health

Use these REST APIs for performing retrieve a collection of Apis associated with a tag in Azure API Management deployment.

Azure Enterprise Knowledge Graph Service is a platform for creating knowledge graphs at scale.

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

The Azure Analysis Services Web API provides a RESTful set of web services that enables users to create, retrieve, update, and delete Analysis Services servers

The Bing Custom Search API lets you send a search query to Bing and get back search results customized to meet your custom search definition.

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.

Amazon RDS Performance Insights Amazon RDS Performance Insights enables you to monitor and explore different dimensions of database load based on data captured from a running DB instance. The guide provides detailed information about Performance Insights data types, parameters and errors. When Performance Insights is enabled, the Amazon RDS Performance Insights API provides visibility into the performance of your DB instance. Amazon CloudWatch provides the authoritative source for AWS service-vended monitoring metrics. Performance Insights offers a domain-specific view of DB load. DB load is measured as Average Active Sessions. Performance Insights provides the data to API consumers as a two-dimensional time-series dataset. The time dimension provides DB load data for each time point in the queried time range. Each time point decomposes overall load in relation to the requested dimensions, measured at that time point. Examples include SQL, Wait event, User, and Host. To learn more about Performance Insights and Amazon Aurora DB instances, go to the Amazon Aurora User Guide. To learn more about Performance Insights and Amazon RDS DB instances, go to the Amazon RDS User Guide.

AWS Lake Formation Defines the public endpoint for the AWS Lake Formation service.

Kinesis Video Streams Signaling Service is a intermediate service that establishes a communication channel for discovering peers, transmitting offers and answers in order to establish peer-to-peer connection in webRTC technology.

EC2 Image Builder is a fully managed Amazon Web Services service that makes it easier to automate the creation, management, and deployment of customized, secure, and up-to-date "golden" server images that are pre-installed and pre-configured with software and settings to meet specific IT standards.

IP pool operation endpoints and objects.

SageMaker Edge Manager dataplane service for communicating with active agents.

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.