Mock sample for your project: AzureStack Azure Bridge Client API

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure PostgreSQL resources including servers, databases, firewall rules, VNET rules, security alert policies, log files and configurations with new business model.

The Admin Commerce Management Client.

REST APIs for Azure Advisor

Use these REST APIs for performing operations on the ApiVersionSet entity associated with your Azure API Management deployment. Using this entity you create and manage API Version Sets that are used to group APIs for consistent versioning.

Azure Application Insights client for Annotations for a component.

Azure Application Insights client for Components.

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

An API for face detection, verification, and identification.

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.

The APIs listed in this specification can be used to manage Deployment Scripts resource through the Azure Resource Manager.

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 Managed Service for Prometheus

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 S3 on Outposts provides access to S3 on Outposts operations.

Provides AWS Marketplace business intelligence data on-demand.

Amazon Kinesis Analytics Overview This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only supports SQL applications. Version 2 of the API supports SQL and Java applications. For more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation. This is the Amazon Kinesis Analytics v1 API Reference. The Amazon Kinesis Analytics Developer Guide provides additional information.

AWS Import/Export Service AWS Import/Export accelerates transferring large amounts of data between the AWS cloud and portable storage devices that you mail to us. AWS Import/Export transfers data directly onto and off of your storage devices using Amazon's high-speed internal network and bypassing the Internet. For large data sets, AWS Import/Export is often faster than Internet transfer and more cost effective than upgrading your connectivity.

Amazon Lex Build-Time Actions Amazon Lex is an AWS service for building conversational voice and text interfaces. Use these actions to create, update, and delete conversational bots for new and existing client applications.

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.

Amazon Kendra is a service for indexing large document sets.