Mock sample for your project: Amazon MemoryDB API

Amazon Kinesis Data Streams Service API Reference Amazon Kinesis Data Streams is a managed service that scales elastically for real-time processing of streaming big data.

Amazon Web Services Price List Service API (Amazon Web Services Price List Service) is a centralized and convenient way to programmatically query Amazon Web Services for services, products, and pricing information. The Amazon Web Services Price List Service uses standardized product attributes such as Location, Storage Class, and Operating System, and provides prices at the SKU level. You can use the Amazon Web Services Price List Service to build cost control and scenario planning tools, reconcile billing data, forecast future spend for budgeting purposes, and provide cost benefit analysis that compare your internal workloads with Amazon Web Services. Use GetServices without a service code to retrieve the service codes for all AWS services, then GetServices with a service code to retreive the attribute names for that service. After you have the service code and attribute names, you can use GetAttributeValues to see what values are available for an attribute. With the service code and an attribute name and value, you can use GetProducts to find specific products that you're interested in, such as an AmazonEC2 instance, with a Provisioned IOPS volumeType. Service Endpoint Amazon Web Services Price List Service API provides the following two endpoints: https://api.pricing.us-east-1.amazonaws.com https://api.pricing.ap-south-1.amazonaws.com

This is the Amazon Lookout for Vision API Reference. It provides descriptions of actions, data types, common parameters, and common errors. Amazon Lookout for Vision enables you to find visual defects in industrial products, accurately and at scale. It uses computer vision to identify missing components in an industrial product, damage to vehicles or structures, irregularities in production lines, and even minuscule defects in silicon wafers — or any other physical item where quality is important such as a missing capacitor on printed circuit boards.

Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.

Contact Lens for Amazon Connect enables you to analyze conversations between customer and agents, by using speech transcription, natural language processing, and intelligent search capabilities. It performs sentiment analysis, detects issues, and enables you to automatically categorize contacts. Contact Lens for Amazon Connect provides both real-time and post-call analytics of customer-agent conversations. For more information, see Analyze conversations using Contact Lens in the Amazon Connect Administrator 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.

This is the AWS Proton Service API Reference. It provides descriptions, syntax and usage examples for each of the actions and data types for the AWS Proton service. The documentation for each action shows the Query API request parameters and the XML response. Alternatively, you can use the AWS CLI to access an API. For more information, see the AWS Command Line Interface User Guide. The AWS Proton service is a two-pronged automation framework. Administrators create service templates to provide standardized infrastructure and deployment tooling for serverless and container based applications. Developers, in turn, select from the available service templates to automate their application or service deployments. Because administrators define the infrastructure and tooling that AWS Proton deploys and manages, they need permissions to use all of the listed API operations. When developers select a specific infrastructure and tooling set, AWS Proton deploys their applications. To monitor their applications that are running on AWS Proton, developers need permissions to the service create, list, update and delete API operations and the service instance list and update API operations. To learn more about AWS Proton administration, see the AWS Proton Administrator Guide. To learn more about deploying serverless and containerized applications on AWS Proton, see the AWS Proton User Guide. Ensuring Idempotency When you make a mutating API request, the request typically returns a result before the asynchronous workflows of the operation are complete. Operations might also time out or encounter other server issues before they're complete, even if the request already returned a result. This might make it difficult to determine whether the request succeeded. Moreover, you might need to retry the request multiple times to ensure that the operation completes successfully. However, if the original request and the subsequent retries are successful, the operation occurs multiple times. This means that you might create more resources than you intended. Idempotency ensures that an API request action completes no more than one time. With an idempotent request, if the original request action completes successfully, any subsequent retries complete successfully without performing any further actions. However, the result might contain updated information, such as the current creation status. The following lists of APIs are grouped according to methods that ensure idempotency. Idempotent create APIs with a client token The API actions in this list support idempotency with the use of a client token. The corresponding AWS CLI commands also support idempotency using a client token. A client token is a unique, case-sensitive string of up to 64 ASCII characters. To make an idempotent API request using one of these actions, specify a client token in the request. We recommend that you don't reuse the same client token for other API requests. If you don’t provide a client token for these APIs, a default client token is automatically provided by SDKs. Given a request action that has succeeded: If you retry the request using the same client token and the same parameters, the retry succeeds without performing any further actions other than returning the original resource detail data in the response. If you retry the request using the same client token, but one or more of the parameters are different, the retry throws a ValidationException with an IdempotentParameterMismatch error. Client tokens expire eight hours after a request is made. If you retry the request with the expired token, a new resource is created. If the original resource is deleted and you retry the request, a new resource is created. Idempotent create APIs with a client token: CreateEnvironmentTemplateVersion CreateServiceTemplateVersion CreateEnvironmentAccountConnection Idempotent create APIs Given a request action that has succeeded: If you retry the request with an API from this group, and the original resource hasn't been modified, the retry succeeds without performing any further actions other than returning the original resource detail data in the response. If the original resource has been modified, the retry throws a ConflictException. If you retry with different input parameters, the retry throws a ValidationException with an IdempotentParameterMismatch error. Idempotent create APIs: CreateEnvironmentTemplate CreateServiceTemplate CreateEnvironment CreateService Idempotent delete APIs Given a request action that has succeeded: When you retry the request with an API from this group and the resource was deleted, its metadata is returned in the response. If you retry and the resource doesn't exist, the response is empty. In both cases, the retry succeeds. Idempotent delete APIs: DeleteEnvironmentTemplate DeleteEnvironmentTemplateVersion DeleteServiceTemplate DeleteServiceTemplateVersion DeleteEnvironmentAccountConnection Asynchronous idempotent delete APIs Given a request action that has succeeded: If you retry the request with an API from this group, if the original request delete operation status is DELETEINPROGRESS, the retry returns the resource detail data in the response without performing any further actions. If the original request delete operation is complete, a retry returns an empty response. Asynchronous idempotent delete APIs: DeleteEnvironment DeleteService

The Application Migration Service service.

AWS Lake Formation Defines the public endpoint for the AWS Lake Formation service.

Amazon CloudWatch monitors your Amazon Web Services (Amazon Web Services) resources and the applications you run on Amazon Web Services in real time. You can use CloudWatch to collect and track metrics, which are the variables you want to measure for your resources and applications. CloudWatch alarms send notifications or automatically change the resources you are monitoring based on rules that you define. For example, you can monitor the CPU usage and disk reads and writes of your Amazon EC2 instances. Then, use this data to determine whether you should launch additional instances to handle increased load. You can also use this data to stop under-used instances to save money. In addition to monitoring the built-in metrics that come with Amazon Web Services, you can monitor your own custom metrics. With CloudWatch, you gain system-wide visibility into resource utilization, application performance, and operational health.

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.

Azure Application Insights client for API keys of a component.

REST API for Azure SignalR Service

Microsoft Azure Mobile Engagement REST APIs.

Azure Monitor client to create/update/delete Scheduled Query Rules

Mixed Reality Resource Provider Proxy API

The Azure Storage Management 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.

Use this REST API to get all the issues across an Azure Api Management service.

Azure Application Insights client for web test based alerting.