Mock sample for your project: AmazonApiGatewayManagementApi API

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.

Amazon Managed Service for Prometheus

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.

The Amazon Braket API Reference provides information about the operations and structures supported in Amazon Braket.

AWS IoT Wireless API documentation

AWS Amplify Admin API

Amazon AppStream 2.0 This is the Amazon AppStream 2.0 API Reference. This documentation provides descriptions and syntax for each of the actions and data types in AppStream 2.0. AppStream 2.0 is a fully managed, secure application streaming service that lets you stream desktop applications to users without rewriting applications. AppStream 2.0 manages the AWS resources that are required to host and run your applications, scales automatically, and provides access to your users on demand. You can call the AppStream 2.0 API operations by using an interface VPC endpoint (interface endpoint). For more information, see Access AppStream 2.0 API Operations and CLI Commands Through an Interface VPC Endpoint in the Amazon AppStream 2.0 Administration Guide. To learn more about AppStream 2.0, see the following resources: Amazon AppStream 2.0 product page Amazon AppStream 2.0 documentation

The AWS Migration Hub API methods help to obtain server and application migration status and integrate your resource-specific migration tool by providing a programmatic interface to Migration Hub. Remember that you must set your AWS Migration Hub home region before you call any of these APIs, or a HomeRegionNotSetException error will be returned. Also, you must make the API calls while in your home region.

Amazon Web Services Single Sign On (SSO) is a cloud SSO service that makes it easy to centrally manage SSO access to multiple Amazon Web Services accounts and business applications. This guide provides information on SSO operations which could be used for access management of Amazon Web Services accounts. For information about Amazon Web Services SSO features, see the Amazon Web Services Single Sign-On User Guide. Many operations in the SSO APIs rely on identifiers for users and groups, known as principals. For more information about how to work with principals and principal IDs in Amazon Web Services SSO, see the Amazon Web Services SSO Identity Store API Reference.

The Amazon Chime API (application programming interface) is designed for developers to perform key tasks, such as creating and managing Amazon Chime accounts, users, and Voice Connectors. This guide provides detailed information about the Amazon Chime API, including operations, types, inputs and outputs, and error codes. It also includes some server-side API actions to use with the Amazon Chime SDK. For more information about the Amazon Chime SDK, see Using the Amazon Chime SDK in the Amazon Chime Developer Guide. You can use an AWS SDK, the AWS Command Line Interface (AWS CLI), or the REST API to make API calls. We recommend using an AWS SDK or the AWS CLI. Each API operation includes links to information about using it with a language-specific AWS SDK or the AWS CLI. Using an AWS SDK You don't need to write code to calculate a signature for request authentication. The SDK clients authenticate your requests by using access keys that you provide. For more information about AWS SDKs, see the AWS Developer Center. Using the AWS CLI Use your access keys with the AWS CLI to make API calls. For information about setting up the AWS CLI, see Installing the AWS Command Line Interface in the AWS Command Line Interface User Guide. For a list of available Amazon Chime commands, see the Amazon Chime commands in the AWS CLI Command Reference. Using REST APIs If you use REST to make API calls, you must authenticate your request by providing a signature. Amazon Chime supports signature version 4. For more information, see Signature Version 4 Signing Process in the Amazon Web Services General Reference. When making REST API calls, use the service name chime and REST endpoint https://service.chime.aws.amazon.com. Administrative permissions are controlled using AWS Identity and Access Management (IAM). For more information, see Identity and Access Management for Amazon Chime in the Amazon Chime Administration Guide.

SageMaker Edge Manager dataplane service for communicating with active agents.

Amazon Macie Classic Amazon Macie Classic is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Macie Classic recognizes sensitive data such as personally identifiable information (PII) or intellectual property, and provides you with dashboards and alerts that give visibility into how this data is being accessed or moved. For more information, see the Amazon Macie Classic User Guide.

Provides APIs for creating and managing Amazon Forecast resources.

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.

Logical network operation endpoints and objects.

Azure Application Insights client for web test based alerting.

REST API for Azure Spring Cloud

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.

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 User Subscription Management Client.

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.

Public IP Address admin endpoints and objects.