Mock sample for your project: AWS IoT Events Data API

Amazon Kinesis Analytics Overview This documentation is for version 1 of the Amazon Kinesis Data Analytics API, which only supports SQL applications. Version 2 of the API supports SQL and Java applications. For more information about version 2, see Amazon Kinesis Data Analytics API V2 Documentation. This is the Amazon Kinesis Analytics v1 API Reference. The Amazon Kinesis Analytics Developer Guide provides additional information.

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.

Cloud9 Cloud9 is a collection of tools that you can use to code, build, run, test, debug, and release software in the cloud. For more information about Cloud9, see the Cloud9 User Guide. Cloud9 supports these operations: CreateEnvironmentEC2 : Creates an Cloud9 development environment, launches an Amazon EC2 instance, and then connects from the instance to the environment. CreateEnvironmentMembership : Adds an environment member to an environment. DeleteEnvironment : Deletes an environment. If an Amazon EC2 instance is connected to the environment, also terminates the instance. DeleteEnvironmentMembership : Deletes an environment member from an environment. DescribeEnvironmentMemberships : Gets information about environment members for an environment. DescribeEnvironments : Gets information about environments. DescribeEnvironmentStatus : Gets status information for an environment. ListEnvironments : Gets a list of environment identifiers. ListTagsForResource : Gets the tags for an environment. TagResource : Adds tags to an environment. UntagResource : Removes tags from an environment. UpdateEnvironment : Changes the settings of an existing environment. UpdateEnvironmentMembership : Changes the settings of an existing environment member for an environment.

Batch Using Batch, you can run batch computing workloads on the Cloud. Batch computing is a common means for developers, scientists, and engineers to access large amounts of compute resources. Batch uses the advantages of this computing workload to remove the undifferentiated heavy lifting of configuring and managing required infrastructure. At the same time, it also adopts a familiar batch computing software approach. Given these advantages, Batch can help you to efficiently provision resources in response to jobs submitted, thus effectively helping you to eliminate capacity constraints, reduce compute costs, and deliver your results more quickly. As a fully managed service, Batch can run batch computing workloads of any scale. Batch automatically provisions compute resources and optimizes workload distribution based on the quantity and scale of your specific workloads. With Batch, there's no need to install or manage batch computing software. This means that you can focus your time and energy on analyzing results and solving your specific problems.

Welcome to the Amazon AppFlow API reference. This guide is for developers who need detailed information about the Amazon AppFlow API operations, data types, and errors. Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between software as a service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and Amazon Web Services like Amazon S3 and Amazon Redshift. Use the following links to get started on the Amazon AppFlow API: Actions : An alphabetical list of all Amazon AppFlow API operations. Data types : An alphabetical list of all Amazon AppFlow data types. Common parameters : Parameters that all Query operations can use. Common errors : Client and server errors that all operations can return. If you're new to Amazon AppFlow, we recommend that you review the Amazon AppFlow User Guide. Amazon AppFlow API users can use vendor-specific mechanisms for OAuth, and include applicable OAuth attributes (such as auth-code and redirecturi) with the connector-specific ConnectorProfileProperties when creating a new connector profile using Amazon AppFlow API operations. For example, Salesforce users can refer to the Authorize Apps with OAuth documentation.

AWS Auto Scaling Use AWS Auto Scaling to create scaling plans for your applications to automatically scale your scalable AWS resources. API Summary You can use the AWS Auto Scaling service API to accomplish the following tasks: Create and manage scaling plans Define target tracking scaling policies to dynamically scale your resources based on utilization Scale Amazon EC2 Auto Scaling groups using predictive scaling and dynamic scaling to scale your Amazon EC2 capacity faster Set minimum and maximum capacity limits Retrieve information on existing scaling plans Access current forecast data and historical forecast data for up to 56 days previous To learn more about AWS Auto Scaling, including information about granting IAM users required permissions for AWS Auto Scaling actions, see the AWS Auto Scaling User Guide.

The Amazon AppIntegrations service enables you to configure and reuse connections to external applications. For information about how you can use external applications with Amazon Connect, see Set up pre-built integrations in the Amazon Connect Administrator Guide.

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 Cloud Directory Amazon Cloud Directory is a component of the AWS Directory Service that simplifies the development and management of cloud-scale web, mobile, and IoT applications. This guide describes the Cloud Directory operations that you can call programmatically and includes detailed information on data types and errors. For information about Cloud Directory features, see AWS Directory Service and the Amazon Cloud Directory Developer Guide.

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.

IoT Greengrass brings local compute, messaging, data management, sync, and ML inference capabilities to edge devices. This enables devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks. Local devices can also communicate securely with Amazon Web Services IoT Core and export IoT data to the Amazon Web Services Cloud. IoT Greengrass developers can use Lambda functions and components to create and deploy applications to fleets of edge devices for local operation. IoT Greengrass Version 2 provides a new major version of the IoT Greengrass Core software, new APIs, and a new console. Use this API reference to learn how to use the IoT Greengrass V2 API operations to manage components, manage deployments, and core devices. For more information, see What is IoT Greengrass? in the IoT Greengrass V2 Developer Guide.

Identity and Access Management Access Analyzer helps identify potential resource-access risks by enabling you to identify any policies that grant access to an external principal. It does this by using logic-based reasoning to analyze resource-based policies in your Amazon Web Services environment. An external principal can be another Amazon Web Services account, a root user, an IAM user or role, a federated user, an Amazon Web Services service, or an anonymous user. You can also use IAM Access Analyzer to preview and validate public and cross-account access to your resources before deploying permissions changes. This guide describes the Identity and Access Management Access Analyzer operations that you can call programmatically. For general information about IAM Access Analyzer, see Identity and Access Management Access Analyzer in the IAM User Guide. To start using IAM Access Analyzer, you first need to create an analyzer.

Logical network operation endpoints and objects.

The Admin Subscriptions Management Client.

The Admin Backup Management Client.

The Admin Subscriptions Management Client.

The REST API specification for Ably.

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

Edge gateway operation endpoints and objects.

Drive operation endpoints and objects.

This is the Resource Access Manager API Reference. This documentation provides descriptions and syntax for each of the actions and data types in RAM. RAM is a service that helps you securely share your Amazon Web Services resources across Amazon Web Services accounts and within your organization or organizational units (OUs) in Organizations. For supported resource types, you can also share resources with IAM roles and IAM users. If you have multiple Amazon Web Services accounts, you can use RAM to share those resources with other accounts. To learn more about RAM, see the following resources: Resource Access Manager product page Resource Access Manager User Guide