Mock sample for your project: Azure SQL Database API

Extracts information from forms and images into structured data.

Microsoft Customer Engagement Fabric

Use these APIs to manage Azure Front Door resources through the Azure Resource Manager. You must make sure that requests made to these resources are secure.

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 assignments. A role assignment grants access to Azure Active Directory users.

The HDInsight Management Client.

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.

To manage and control access to your resources, you can define customized policies and assign them at a scope.

Azure Service Fabric Resource Provider API Client

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.

Azure Resource Graph API Reference

The HDInsight Management Client.

Resource Groups Tagging API

Azure DevOps Resource Provider

The Admin Subscriptions Management Client.

The Admin Backup Management Client.

Infrastructure role operation endpoints and objects.

REST API for Azure Blockchain Service

APIs for managing software update configurations.

Amazon Data Lifecycle Manager With Amazon Data Lifecycle Manager, you can manage the lifecycle of your Amazon Web Services resources. You create lifecycle policies, which are used to automate operations on the specified resources. Amazon DLM supports Amazon EBS volumes and snapshots. For information about using Amazon DLM with Amazon EBS, see Automating the Amazon EBS Snapshot Lifecycle in the Amazon EC2 User Guide.

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 API Gateway Amazon API Gateway helps developers deliver robust, secure, and scalable mobile and web application back ends. API Gateway allows developers to securely connect mobile and web applications to APIs that run on AWS Lambda, Amazon EC2, or other publicly addressable web services that are hosted outside of AWS.