Mock sample for your project: Amazon Rekognition API

The operations for managing an Amazon MSK cluster.

Amazon Honeycode is a fully managed service that allows you to quickly build mobile and web apps for teams—without programming. Build Honeycode apps for managing almost anything, like projects, customers, operations, approvals, resources, and even your team.

AWS IoT Core Device Advisor is a cloud-based, fully managed test capability for validating IoT devices during device software development. Device Advisor provides pre-built tests that you can use to validate IoT devices for reliable and secure connectivity with AWS IoT Core before deploying devices to production. By using Device Advisor, you can confirm that your devices can connect to AWS IoT Core, follow security best practices and, if applicable, receive software updates from IoT Device Management. You can also download signed qualification reports to submit to the AWS Partner Network to get your device qualified for the AWS Partner Device Catalog without the need to send your device in and wait for it to be tested.

This is AWS WAF Classic Regional documentation. For more information, see AWS WAF Classic in the developer guide. For the latest version of AWS WAF, use the AWS WAFV2 API and see the AWS WAF Developer Guide. With the latest version, AWS WAF has a single set of endpoints for regional and global use. This is the AWS WAF Regional Classic API Reference for using AWS WAF Classic with the AWS resources, Elastic Load Balancing (ELB) Application Load Balancers and API Gateway APIs. The AWS WAF Classic actions and data types listed in the reference are available for protecting Elastic Load Balancing (ELB) Application Load Balancers and API Gateway APIs. You can use these actions and data types by means of the endpoints listed in AWS Regions and Endpoints. This guide is for developers who need detailed information about the AWS WAF Classic API actions, data types, and errors. For detailed information about AWS WAF Classic features and an overview of how to use the AWS WAF Classic API, see the AWS WAF Classic in the developer guide.

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.

Amazon Managed Blockchain is a fully managed service for creating and managing blockchain networks using open-source frameworks. Blockchain allows you to build applications where multiple parties can securely and transparently run transactions and share data without the need for a trusted, central authority. Managed Blockchain supports the Hyperledger Fabric and Ethereum open-source frameworks. Because of fundamental differences between the frameworks, some API actions or data types may only apply in the context of one framework and not the other. For example, actions related to Hyperledger Fabric network members such as CreateMember and DeleteMember do not apply to Ethereum. The description for each action indicates the framework or frameworks to which it applies. Data types and properties that apply only in the context of a particular framework are similarly indicated.

Amazon Neptune Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. This interface reference for Amazon Neptune contains documentation for a programming or command line interface you can use to manage Amazon Neptune. Note that Amazon Neptune 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 Lex Build-Time Actions Amazon Lex is an AWS service for building conversational voice and text interfaces. Use these actions to create, update, and delete conversational bots for new and existing client applications.

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.

Kinesis Video Streams Signaling Service is a intermediate service that establishes a communication channel for discovering peers, transmitting offers and answers in order to establish peer-to-peer connection in webRTC technology.

Amazon CloudWatch Application Insights Amazon CloudWatch Application Insights is a service that helps you detect common problems with your applications. It enables you to pinpoint the source of issues in your applications (built with technologies such as Microsoft IIS, .NET, and Microsoft SQL Server), by providing key insights into detected problems. After you onboard your application, CloudWatch Application Insights identifies, recommends, and sets up metrics and logs. It continuously analyzes and correlates your metrics and logs for unusual behavior to surface actionable problems with your application. For example, if your application is slow and unresponsive and leading to HTTP 500 errors in your Application Load Balancer (ALB), Application Insights informs you that a memory pressure problem with your SQL Server database is occurring. It bases this analysis on impactful metrics and log errors.

Amazon Lightsail is the easiest way to get started with Amazon Web Services (AWS) for developers who need to build websites or web applications. It includes everything you need to launch your project quickly - instances (virtual private servers), container services, storage buckets, managed databases, SSD-based block storage, static IP addresses, load balancers, content delivery network (CDN) distributions, DNS management of registered domains, and resource snapshots (backups) - for a low, predictable monthly price. You can manage your Lightsail resources using the Lightsail console, Lightsail API, AWS Command Line Interface (AWS CLI), or SDKs. For more information about Lightsail concepts and tasks, see the Amazon Lightsail Developer Guide. This API Reference provides detailed information about the actions, data types, parameters, and errors of the Lightsail service. For more information about the supported AWS Regions, endpoints, and service quotas of the Lightsail service, see Amazon Lightsail Endpoints and Quotas in the AWS General Reference.

Amazon DevOps Guru is a fully managed service that helps you identify anomalous behavior in business critical operational applications. You specify the AWS resources that you want DevOps Guru to cover, then the Amazon CloudWatch metrics and AWS CloudTrail events related to those resources are analyzed. When anomalous behavior is detected, DevOps Guru creates an insight that includes recommendations, related events, and related metrics that can help you improve your operational applications. For more information, see What is Amazon DevOps Guru. You can specify 1 or 2 Amazon Simple Notification Service topics so you are notified every time a new insight is created. You can also enable DevOps Guru to generate an OpsItem in AWS Systems Manager for each insight to help you manage and track your work addressing insights. To learn about the DevOps Guru workflow, see How DevOps Guru works. To learn about DevOps Guru concepts, see Concepts in DevOps Guru.

The Admin Storage Management Client.

The Azure BatchAI Management API.

Azure software plans let users create and manage licenses for various software used in Azure.

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 MQ is a managed message broker service for Apache ActiveMQ and RabbitMQ that makes it easy to set up and operate message brokers in the cloud. A message broker allows software applications and components to communicate using various programming languages, operating systems, and formal messaging protocols.

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 Route 53 is a highly available and scalable Domain Name System (DNS) web service.

The Admin Commerce Management Client.

Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording Events.