Mock sample for your project: AWS Elemental MediaStore API

MemoryDB for Redis is a fully managed, Redis-compatible, in-memory database that delivers ultra-fast performance and Multi-AZ durability for modern applications built using microservices architectures. MemoryDB stores the entire database in-memory, enabling low latency and high throughput data access. It is compatible with Redis, a popular open source data store, enabling you to leverage Redis’ flexible and friendly data structures, APIs, and commands.

AWS CodeDeploy AWS CodeDeploy is a deployment service that automates application deployments to Amazon EC2 instances, on-premises instances running in your own facility, serverless AWS Lambda functions, or applications in an Amazon ECS service. You can deploy a nearly unlimited variety of application content, such as an updated Lambda function, updated applications in an Amazon ECS service, code, web and configuration files, executables, packages, scripts, multimedia files, and so on. AWS CodeDeploy can deploy application content stored in Amazon S3 buckets, GitHub repositories, or Bitbucket repositories. You do not need to make changes to your existing code before you can use AWS CodeDeploy. AWS CodeDeploy makes it easier for you to rapidly release new features, helps you avoid downtime during application deployment, and handles the complexity of updating your applications, without many of the risks associated with error-prone manual deployments. AWS CodeDeploy Components Use the information in this guide to help you work with the following AWS CodeDeploy components: Application : A name that uniquely identifies the application you want to deploy. AWS CodeDeploy uses this name, which functions as a container, to ensure the correct combination of revision, deployment configuration, and deployment group are referenced during a deployment. Deployment group : A set of individual instances, CodeDeploy Lambda deployment configuration settings, or an Amazon ECS service and network details. A Lambda deployment group specifies how to route traffic to a new version of a Lambda function. An Amazon ECS deployment group specifies the service created in Amazon ECS to deploy, a load balancer, and a listener to reroute production traffic to an updated containerized application. An EC2/On-premises deployment group contains individually tagged instances, Amazon EC2 instances in Amazon EC2 Auto Scaling groups, or both. All deployment groups can specify optional trigger, alarm, and rollback settings. Deployment configuration : A set of deployment rules and deployment success and failure conditions used by AWS CodeDeploy during a deployment. Deployment : The process and the components used when updating a Lambda function, a containerized application in an Amazon ECS service, or of installing content on one or more instances. Application revisions : For an AWS Lambda deployment, this is an AppSpec file that specifies the Lambda function to be updated and one or more functions to validate deployment lifecycle events. For an Amazon ECS deployment, this is an AppSpec file that specifies the Amazon ECS task definition, container, and port where production traffic is rerouted. For an EC2/On-premises deployment, this is an archive file that contains source content—source code, webpages, executable files, and deployment scripts—along with an AppSpec file. Revisions are stored in Amazon S3 buckets or GitHub repositories. For Amazon S3, a revision is uniquely identified by its Amazon S3 object key and its ETag, version, or both. For GitHub, a revision is uniquely identified by its commit ID. This guide also contains information to help you get details about the instances in your deployments, to make on-premises instances available for AWS CodeDeploy deployments, to get details about a Lambda function deployment, and to get details about Amazon ECS service deployments. AWS CodeDeploy Information Resources AWS CodeDeploy User Guide AWS CodeDeploy API Reference Guide AWS CLI Reference for AWS CodeDeploy AWS CodeDeploy Developer Forum

Amazon Connect is a cloud-based contact center solution that makes it easy to set up and manage a customer contact center and provide reliable customer engagement at any scale. Amazon Connect enables customer contacts through voice or chat. The APIs described here are used by chat participants, such as agents and customers.

With Application Auto Scaling, you can configure automatic scaling for the following resources: Amazon AppStream 2.0 fleets Amazon Aurora Replicas Amazon Comprehend document classification and entity recognizer endpoints Amazon DynamoDB tables and global secondary indexes throughput capacity Amazon ECS services Amazon ElastiCache for Redis clusters (replication groups) Amazon EMR clusters Amazon Keyspaces (for Apache Cassandra) tables Lambda function provisioned concurrency Amazon Managed Streaming for Apache Kafka broker storage Amazon SageMaker endpoint variants Spot Fleet (Amazon EC2) requests Custom resources provided by your own applications or services API Summary The Application Auto Scaling service API includes three key sets of actions: Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets. Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history. Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the RegisterScalableTarget API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling. To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.

This section provides documentation for the Amazon CodeGuru Reviewer API operations. CodeGuru Reviewer is a service that uses program analysis and machine learning to detect potential defects that are difficult for developers to find and recommends fixes in your Java and Python code. By proactively detecting and providing recommendations for addressing code defects and implementing best practices, CodeGuru Reviewer improves the overall quality and maintainability of your code base during the code review stage. For more information about CodeGuru Reviewer, see the Amazon CodeGuru Reviewer User Guide. To improve the security of your CodeGuru Reviewer API calls, you can establish a private connection between your VPC and CodeGuru Reviewer by creating an interface VPC endpoint. For more information, see CodeGuru Reviewer and interface VPC endpoints (Amazon Web Services PrivateLink) in the Amazon CodeGuru Reviewer User Guide.

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

Amazon SimpleDB is a web service providing the core database functions of data indexing and querying in the cloud. By offloading the time and effort associated with building and operating a web-scale database, SimpleDB provides developers the freedom to focus on application development. A traditional, clustered relational database requires a sizable upfront capital outlay, is complex to design, and often requires extensive and repetitive database administration. Amazon SimpleDB is dramatically simpler, requiring no schema, automatically indexing your data and providing a simple API for storage and access. This approach eliminates the administrative burden of data modeling, index maintenance, and performance tuning. Developers gain access to this functionality within Amazon's proven computing environment, are able to scale instantly, and pay only for what they use. Visit http://aws.amazon.com/simpledb/ for more information.

This is the ACM Private CA API Reference. It provides descriptions, syntax, and usage examples for each of the actions and data types involved in creating and managing private certificate authorities (CA) for your organization. The documentation for each action shows the Query API request parameters and the XML response. Alternatively, you can use one of the AWS SDKs to access an API that's tailored to the programming language or platform that you're using. For more information, see AWS SDKs. Each ACM Private CA API operation has a quota that determines the number of times the operation can be called per second. ACM Private CA throttles API requests at different rates depending on the operation. Throttling means that ACM Private CA rejects an otherwise valid request because the request exceeds the operation's quota for the number of requests per second. When a request is throttled, ACM Private CA returns a ThrottlingException error. ACM Private CA does not guarantee a minimum request rate for APIs. To see an up-to-date list of your ACM Private CA quotas, or to request a quota increase, log into your AWS account and visit the Service Quotas console.

Pinpoint SMS and Voice Messaging public facing APIs

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.

Glue DataBrew is a visual, cloud-scale data-preparation service. DataBrew simplifies data preparation tasks, targeting data issues that are hard to spot and time-consuming to fix. DataBrew empowers users of all technical levels to visualize the data and perform one-click data transformations, with no coding required.

Amazon Route 53 API actions let you register domain names and perform related operations.

This Swagger was generated by the API Framework.

The Azure Management Groups API enables consolidation of multiple subscriptions/resources into an organizational hierarchy and centrally manage access control, policies, alerting and reporting for those resources.

The Anomaly Finder API detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the timeseries, another is detecting last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis.

The key vault client performs cryptographic key operations and vault operations against the Key Vault 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.

This Swagger was generated by the API Framework.

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.

API to Add/Remove/List Guest Diagnostics Settings Association for Azure Resources

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

Virtual Machine Image Template