Mock sample for your project: AWS Security Token Service API

AWS OpsWorks CM AWS OpsWorks for configuration management (CM) is a service that runs and manages configuration management servers. You can use AWS OpsWorks CM to create and manage AWS OpsWorks for Chef Automate and AWS OpsWorks for Puppet Enterprise servers, and add or remove nodes for the servers to manage. Glossary of terms Server : A configuration management server that can be highly-available. The configuration management server runs on an Amazon Elastic Compute Cloud (EC2) instance, and may use various other AWS services, such as Amazon Relational Database Service (RDS) and Elastic Load Balancing. A server is a generic abstraction over the configuration manager that you want to use, much like Amazon RDS. In AWS OpsWorks CM, you do not start or stop servers. After you create servers, they continue to run until they are deleted. Engine : The engine is the specific configuration manager that you want to use. Valid values in this release include ChefAutomate and Puppet. Backup : This is an application-level backup of the data that the configuration manager stores. AWS OpsWorks CM creates an S3 bucket for backups when you launch the first server. A backup maintains a snapshot of a server's configuration-related attributes at the time the backup starts. Events : Events are always related to a server. Events are written during server creation, when health checks run, when backups are created, when system maintenance is performed, etc. When you delete a server, the server's events are also deleted. Account attributes : Every account has attributes that are assigned in the AWS OpsWorks CM database. These attributes store information about configuration limits (servers, backups, etc.) and your customer account. Endpoints AWS OpsWorks CM supports the following endpoints, all HTTPS. You must connect to one of the following endpoints. Your servers can only be accessed or managed within the endpoint in which they are created. opsworks-cm.us-east-1.amazonaws.com opsworks-cm.us-east-2.amazonaws.com opsworks-cm.us-west-1.amazonaws.com opsworks-cm.us-west-2.amazonaws.com opsworks-cm.ap-northeast-1.amazonaws.com opsworks-cm.ap-southeast-1.amazonaws.com opsworks-cm.ap-southeast-2.amazonaws.com opsworks-cm.eu-central-1.amazonaws.com opsworks-cm.eu-west-1.amazonaws.com For more information, see AWS OpsWorks endpoints and quotas in the AWS General Reference. Throttling limits All API operations allow for five requests per second with a burst of 10 requests per second.

Elastic Load Balancing A load balancer can distribute incoming traffic across your EC2 instances. This enables you to increase the availability of your application. The load balancer also monitors the health of its registered instances and ensures that it routes traffic only to healthy instances. You configure your load balancer to accept incoming traffic by specifying one or more listeners, which are configured with a protocol and port number for connections from clients to the load balancer and a protocol and port number for connections from the load balancer to the instances. Elastic Load Balancing supports three types of load balancers: Application Load Balancers, Network Load Balancers, and Classic Load Balancers. You can select a load balancer based on your application needs. For more information, see the Elastic Load Balancing User Guide. This reference covers the 2012-06-01 API, which supports Classic Load Balancers. The 2015-12-01 API supports Application Load Balancers and Network Load Balancers. To get started, create a load balancer with one or more listeners using CreateLoadBalancer. Register your instances with the load balancer using RegisterInstancesWithLoadBalancer. All Elastic Load Balancing operations are idempotent, which means that they complete at most one time. If you repeat an operation, it succeeds with a 200 OK response code.

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.

Amazon EventBridge helps you to respond to state changes in your Amazon Web Services resources. When your resources change state, they automatically send events to an event stream. You can create rules that match selected events in the stream and route them to targets to take action. You can also use rules to take action on a predetermined schedule. For example, you can configure rules to: Automatically invoke an Lambda function to update DNS entries when an event notifies you that Amazon EC2 instance enters the running state. Direct specific API records from CloudTrail to an Amazon Kinesis data stream for detailed analysis of potential security or availability risks. Periodically invoke a built-in target to create a snapshot of an Amazon EBS volume. For more information about the features of Amazon EventBridge, see the Amazon EventBridge User Guide.

Amazon Data Lifecycle Manager With Amazon Data Lifecycle Manager, you can manage the lifecycle of your Amazon Web Services resources. You create lifecycle policies, which are used to automate operations on the specified resources. Amazon DLM supports Amazon EBS volumes and snapshots. For information about using Amazon DLM with Amazon EBS, see Automating the Amazon EBS Snapshot Lifecycle in the Amazon EC2 User Guide.

AWS Fault Injection Simulator is a managed service that enables you to perform fault injection experiments on your AWS workloads. For more information, see the AWS Fault Injection Simulator User Guide.

The operations for managing an Amazon MSK cluster.

AWS IoT Events monitors your equipment or device fleets for failures or changes in operation, and triggers actions when such events occur. You can use AWS IoT Events API operations to create, read, update, and delete inputs and detector models, and to list their versions.

AWS IoT Wireless API documentation

Provides APIs for creating and managing Amazon Forecast resources.

Elastic Load Balancing A load balancer distributes incoming traffic across targets, such as your EC2 instances. This enables you to increase the availability of your application. The load balancer also monitors the health of its registered targets and ensures that it routes traffic only to healthy targets. You configure your load balancer to accept incoming traffic by specifying one or more listeners, which are configured with a protocol and port number for connections from clients to the load balancer. You configure a target group with a protocol and port number for connections from the load balancer to the targets, and with health check settings to be used when checking the health status of the targets. Elastic Load Balancing supports the following types of load balancers: Application Load Balancers, Network Load Balancers, Gateway Load Balancers, and Classic Load Balancers. This reference covers the following load balancer types: Application Load Balancer - Operates at the application layer (layer 7) and supports HTTP and HTTPS. Network Load Balancer - Operates at the transport layer (layer 4) and supports TCP, TLS, and UDP. Gateway Load Balancer - Operates at the network layer (layer 3). For more information, see the Elastic Load Balancing User Guide. All Elastic Load Balancing operations are idempotent, which means that they complete at most one time. If you repeat an operation, it succeeds.

EC2 Image Builder is a fully managed Amazon Web Services service that makes it easier to automate the creation, management, and deployment of customized, secure, and up-to-date "golden" server images that are pre-installed and pre-configured with software and settings to meet specific IT standards.

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.

Amazon WorkLink is a cloud-based service that provides secure access to internal websites and web apps from iOS and Android phones. In a single step, your users, such as employees, can access internal websites as efficiently as they access any other public website. They enter a URL in their web browser, or choose a link to an internal website in an email. Amazon WorkLink authenticates the user's access and securely renders authorized internal web content in a secure rendering service in the AWS cloud. Amazon WorkLink doesn't download or store any internal web content on mobile devices.

An API for QnAMaker Service

The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure MariaDB resources including servers, databases, firewall rules, VNET rules, log files and configurations with new business model.

The Compute Management Client.

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.

An API for face detection, verification, and identification.

Use these REST APIs for performing operations on Email Templates associated with your Azure API Management deployment.

REST APIs for orchestrating deployments using the Azure Deployment Manager (ADM). See https://docs.microsoft.com/en-us/azure/azure-resource-manager/deployment-manager-overview for more information.