Mock sample for your project: Azure SQL Database API

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Scale unit node operation endpoints and objects.

The HDInsight Management Client.

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.

The HDInsight Management Client.

The Admin Backup Management Client.

Azure Log Analytics API reference for management of saved Queries within Query Packs.

The Admin Infrastructure Insights Management Client.

These APIs allow end users to operate on Azure Machine Learning Web Services resources. They support the following operations: Create or update a web service Get a web service Patch a web service Delete a web service Get All Web Services in a Resource Group Get All Web Services in a Subscription Get Web Services Keys

The Admin Subscriptions Management Client.

Batch Using Batch, you can run batch computing workloads on the Cloud. Batch computing is a common means for developers, scientists, and engineers to access large amounts of compute resources. Batch uses the advantages of this computing workload to remove the undifferentiated heavy lifting of configuring and managing required infrastructure. At the same time, it also adopts a familiar batch computing software approach. Given these advantages, Batch can help you to efficiently provision resources in response to jobs submitted, thus effectively helping you to eliminate capacity constraints, reduce compute costs, and deliver your results more quickly. As a fully managed service, Batch can run batch computing workloads of any scale. Batch automatically provisions compute resources and optimizes workload distribution based on the quantity and scale of your specific workloads. With Batch, there's no need to install or manage batch computing software. This means that you can focus your time and energy on analyzing results and solving your specific problems.

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.

The service for managing third party addons.

Amazon Simple Notification Service Amazon Simple Notification Service (Amazon SNS) is a web service that enables you to build distributed web-enabled applications. Applications can use Amazon SNS to easily push real-time notification messages to interested subscribers over multiple delivery protocols. For more information about this product see the Amazon SNS product page. For detailed information about Amazon SNS features and their associated API calls, see the Amazon SNS Developer Guide. For information on the permissions you need to use this API, see Identity and access management in Amazon SNS in the Amazon SNS Developer Guide. We also provide SDKs that enable you to access Amazon SNS from your preferred programming language. The SDKs contain functionality that automatically takes care of tasks such as: cryptographically signing your service requests, retrying requests, and handling error responses. For a list of available SDKs, go to Tools for Amazon Web Services.

Deployment Admin Client.

Amazon DynamoDB Amazon DynamoDB Streams provides API actions for accessing streams and processing stream records. To learn more about application development with Streams, see Capturing Table Activity with DynamoDB Streams in the Amazon DynamoDB Developer Guide.

Deployment Admin Client.

Provides APIs for creating and managing Amazon SageMaker resources. Other Resources: Amazon SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

Amazon Timestream is a fast, scalable, fully managed time series database service that makes it easy to store and analyze trillions of time series data points per day. With Timestream, you can easily store and analyze IoT sensor data to derive insights from your IoT applications. You can analyze industrial telemetry to streamline equipment management and maintenance. You can also store and analyze log data and metrics to improve the performance and availability of your applications. Timestream is built from the ground up to effectively ingest, process, and store time series data. It organizes data to optimize query processing. It automatically scales based on the volume of data ingested and on the query volume to ensure you receive optimal performance while inserting and querying data. As your data grows over time, Timestream’s adaptive query processing engine spans across storage tiers to provide fast analysis while reducing costs.