Mock sample for your project: AppServiceEnvironments API Client

API spec for Microsoft.SecurityInsights (Azure Security Insights) resource provider

The Admin Subscriptions Management Client.

The Admin Commerce Management Client.

APIs for managing software update configurations.

The Admin Backup Management Client.

The Admin Fabric Management Client.

AzureBridge Admin Client.

Provides read and update functionality for Azure SQL Database geo backup policies.

Extracts information from forms and images into structured data.

Creates an Azure Data Lake Store account management client.

API for using the Azure IoT Hub Device Provisioning Service features.

The Anomaly Finder API detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the timeseries, another is detecting last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis.

The Container Service Client.

The Azure Data Catalog Resource Provider Services API.

Creates an Azure Data Lake Analytics account management client.

Amazon Relational Database Service Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, resizeable capacity for an industry-standard relational database and manages common database administration tasks, freeing up developers to focus on what makes their applications and businesses unique. Amazon RDS gives you access to the capabilities of a MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, or Amazon Aurora database server. These capabilities mean that the code, applications, and tools you already use today with your existing databases work with Amazon RDS without modification. Amazon RDS automatically backs up your database and maintains the database software that powers your DB instance. Amazon RDS is flexible: you can scale your DB instance's compute resources and storage capacity to meet your application's demand. As with all Amazon Web Services, there are no up-front investments, and you pay only for the resources you use. This interface reference for Amazon RDS contains documentation for a programming or command line interface you can use to manage Amazon RDS. Amazon RDS is asynchronous, which means that some interfaces might require techniques such as polling or callback functions to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a command is applied immediately, on the next instance reboot, or during the maintenance window. The reference structure is as follows, and we list following some related topics from the user guide. Amazon RDS API Reference For the alphabetical list of API actions, see API Actions. For the alphabetical list of data types, see Data Types. For a list of common query parameters, see Common Parameters. For descriptions of the error codes, see Common Errors. Amazon RDS User Guide For a summary of the Amazon RDS interfaces, see Available RDS Interfaces. For more information about how to use the Query API, see Using the Query API.

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.

Amazon Kinesis Data Analytics is a fully managed service that you can use to process and analyze streaming data using Java, SQL, or Scala. The service enables you to quickly author and run Java, SQL, or Scala code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics.