Mock sample for your project: AuthorizationManagementClient API

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

The Azure Data Catalog Resource Provider Services API.

An API for face detection, verification, and identification.

Storage system operation endpoints and objects.

The Computer Vision API provides state-of-the-art algorithms to process images and return information. For example, it can be used to determine if an image contains mature content, or it can be used to find all the faces in an image. It also has other features like estimating dominant and accent colors, categorizing the content of images, and describing an image with complete English sentences. Additionally, it can also intelligently generate images thumbnails for displaying large images effectively.

The Admin Backup Management Client.

Logical subnet operation endpoints and objects.

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 tenant entity associated with your Azure API Management deployment. Using this entity you can manage properties and configuration that apply to the entire API Management service instance.

The HDInsight Management Client.

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.

AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use the AWS Data Exchange APIs to create, update, manage, and access file-based data set in the AWS Cloud. As a subscriber, you can view and access the data sets that you have an entitlement to through a subscription. You can use the APIS to download or copy your entitled data sets to Amazon S3 for use across a variety of AWS analytics and machine learning services. As a provider, you can create and manage your data sets that you would like to publish to a product. Being able to package and provide your data sets into products requires a few steps to determine eligibility. For more information, visit the AWS Data Exchange User Guide. A data set is a collection of data that can be changed or updated over time. Data sets can be updated using revisions, which represent a new version or incremental change to a data set. A revision contains one or more assets. An asset in AWS Data Exchange is a piece of data that can be stored as an Amazon S3 object. The asset can be a structured data file, an image file, or some other data file. Jobs are asynchronous import or export operations used to create or copy assets.

Amazon Simple Email Service This document contains reference information for the Amazon Simple Email Service (Amazon SES) API, version 2010-12-01. This document is best used in conjunction with the Amazon SES Developer Guide. For a list of Amazon SES endpoints to use in service requests, see Regions and Amazon SES in the Amazon SES Developer Guide.

IoT data IoT data enables secure, bi-directional communication between Internet-connected things (such as sensors, actuators, embedded devices, or smart appliances) and the Amazon Web Services cloud. It implements a broker for applications and things to publish messages over HTTP (Publish) and retrieve, update, and delete shadows. A shadow is a persistent representation of your things and their state in the Amazon Web Services cloud. Find the endpoint address for actions in IoT data by running this CLI command: aws iot describe-endpoint --endpoint-type iot:Data-ATS The service name used by Amazon Web ServicesSignature Version 4 to sign requests is: iotdevicegateway.

Amazon QuickSight API Reference Amazon QuickSight is a fully managed, serverless business intelligence service for the Amazon Web Services Cloud that makes it easy to extend data and insights to every user in your organization. This API reference contains documentation for a programming interface that you can use to manage Amazon QuickSight.

Definition of the public APIs exposed by Amazon Machine Learning

Amazon EventBridge helps you to respond to state changes in your Amazon Web Services resources. When your resources change state, they automatically send events to an event stream. You can create rules that match selected events in the stream and route them to targets to take action. You can also use rules to take action on a predetermined schedule. For example, you can configure rules to: Automatically invoke an Lambda function to update DNS entries when an event notifies you that Amazon EC2 instance enters the running state. Direct specific API records from CloudTrail to an Amazon Kinesis data stream for detailed analysis of potential security or availability risks. Periodically invoke a built-in target to create a snapshot of an Amazon EBS volume. For more information about the features of Amazon EventBridge, see the Amazon EventBridge User Guide.

AWS Data Pipeline configures and manages a data-driven workflow called a pipeline. AWS Data Pipeline handles the details of scheduling and ensuring that data dependencies are met so that your application can focus on processing the data. AWS Data Pipeline provides a JAR implementation of a task runner called AWS Data Pipeline Task Runner. AWS Data Pipeline Task Runner provides logic for common data management scenarios, such as performing database queries and running data analysis using Amazon Elastic MapReduce (Amazon EMR). You can use AWS Data Pipeline Task Runner as your task runner, or you can write your own task runner to provide custom data management. AWS Data Pipeline implements two main sets of functionality. Use the first set to create a pipeline and define data sources, schedules, dependencies, and the transforms to be performed on the data. Use the second set in your task runner application to receive the next task ready for processing. The logic for performing the task, such as querying the data, running data analysis, or converting the data from one format to another, is contained within the task runner. The task runner performs the task assigned to it by the web service, reporting progress to the web service as it does so. When the task is done, the task runner reports the final success or failure of the task to the web service.

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.

This section provides documentation for the Amazon CodeGuru Reviewer API operations. CodeGuru Reviewer is a service that uses program analysis and machine learning to detect potential defects that are difficult for developers to find and recommends fixes in your Java and Python code. By proactively detecting and providing recommendations for addressing code defects and implementing best practices, CodeGuru Reviewer improves the overall quality and maintainability of your code base during the code review stage. For more information about CodeGuru Reviewer, see the Amazon CodeGuru Reviewer User Guide. To improve the security of your CodeGuru Reviewer API calls, you can establish a private connection between your VPC and CodeGuru Reviewer by creating an interface VPC endpoint. For more information, see CodeGuru Reviewer and interface VPC endpoints (Amazon Web Services PrivateLink) in the Amazon CodeGuru Reviewer 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.