Mock sample for your project: Amazon WorkLink 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 Server Migration Service AWS Server Migration Service (AWS SMS) makes it easier and faster for you to migrate your on-premises workloads to AWS. To learn more about AWS SMS, see the following resources: AWS Server Migration Service product page AWS Server Migration Service User Guide

Provides APIs for creating and managing Amazon SageMaker resources. Other Resources: Amazon SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

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 Elemental MediaPackage

This section provides documentation for the AWS RoboMaker API operations.

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.

Amplify enables developers to develop and deploy cloud-powered mobile and web apps. The Amplify Console provides a continuous delivery and hosting service for web applications. For more information, see the Amplify Console User Guide. The Amplify Framework is a comprehensive set of SDKs, libraries, tools, and documentation for client app development. For more information, see the Amplify Framework.

Amazon Athena is an interactive query service that lets you use standard SQL to analyze data directly in Amazon S3. You can point Athena at your data in Amazon S3 and run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to set up or manage. You pay only for the queries you run. Athena scales automatically—executing queries in parallel—so results are fast, even with large datasets and complex queries. For more information, see What is Amazon Athena in the Amazon Athena User Guide. If you connect to Athena using the JDBC driver, use version 1.1.0 of the driver or later with the Amazon Athena API. Earlier version drivers do not support the API. For more information and to download the driver, see Accessing Amazon Athena with JDBC. For code samples using the Amazon Web Services SDK for Java, see Examples and Code Samples in the Amazon Athena User Guide.

Welcome to the Amazon SQS API Reference. Amazon SQS is a reliable, highly-scalable hosted queue for storing messages as they travel between applications or microservices. Amazon SQS moves data between distributed application components and helps you decouple these components. For information on the permissions you need to use this API, see Identity and access management in the Amazon SQS Developer Guide. You can use Amazon Web Services SDKs to access Amazon SQS using your favorite programming language. The SDKs perform tasks such as the following automatically: Cryptographically sign your service requests Retry requests Handle error responses Additional information Amazon SQS Product Page Amazon SQS Developer Guide Making API Requests Amazon SQS Message Attributes Amazon SQS Dead-Letter Queues Amazon SQS in the Command Line Interface Amazon Web Services General Reference Regions and Endpoints

This is the Amazon Fraud Detector API Reference. This guide is for developers who need detailed information about Amazon Fraud Detector API actions, data types, and errors. For more information about Amazon Fraud Detector features, see the Amazon Fraud Detector User Guide.

Amazon API Gateway V2

DataSync DataSync is a managed data transfer service that makes it simpler for you to automate moving data between on-premises storage and Amazon Simple Storage Service (Amazon S3) or Amazon Elastic File System (Amazon EFS). This API interface reference for DataSync contains documentation for a programming interface that you can use to manage DataSync.

Amazon S3 Glacier (Glacier) is a storage solution for "cold data." Glacier is an extremely low-cost storage service that provides secure, durable, and easy-to-use storage for data backup and archival. With Glacier, customers can store their data cost effectively for months, years, or decades. Glacier also enables customers to offload the administrative burdens of operating and scaling storage to AWS, so they don't have to worry about capacity planning, hardware provisioning, data replication, hardware failure and recovery, or time-consuming hardware migrations. Glacier is a great storage choice when low storage cost is paramount and your data is rarely retrieved. If your application requires fast or frequent access to your data, consider using Amazon S3. For more information, see Amazon Simple Storage Service (Amazon S3). You can store any kind of data in any format. There is no maximum limit on the total amount of data you can store in Glacier. If you are a first-time user of Glacier, we recommend that you begin by reading the following sections in the Amazon S3 Glacier Developer Guide : What is Amazon S3 Glacier - This section of the Developer Guide describes the underlying data model, the operations it supports, and the AWS SDKs that you can use to interact with the service. Getting Started with Amazon S3 Glacier - The Getting Started section walks you through the process of creating a vault, uploading archives, creating jobs to download archives, retrieving the job output, and deleting archives.

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.

Glue Defines the public endpoint for the Glue service.

Amazon Web Services Transfer Family is a fully managed service that enables the transfer of files over the File Transfer Protocol (FTP), File Transfer Protocol over SSL (FTPS), or Secure Shell (SSH) File Transfer Protocol (SFTP) directly into and out of Amazon Simple Storage Service (Amazon S3). Amazon Web Services helps you seamlessly migrate your file transfer workflows to Amazon Web Services Transfer Family by integrating with existing authentication systems, and providing DNS routing with Amazon Route 53 so nothing changes for your customers and partners, or their applications. With your data in Amazon S3, you can use it with Amazon Web Services services for processing, analytics, machine learning, and archiving. Getting started with Amazon Web Services Transfer Family is easy since there is no infrastructure to buy and set up.

Lambda Overview This is the Lambda API Reference. The Lambda Developer Guide provides additional information. For the service overview, see What is Lambda, and for information about how the service works, see Lambda: How it Works in the Lambda Developer Guide.

Use these REST APIs for performing operations on API entity and their Operations associated with your Azure API Management deployment.

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.

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.

Amazon Timestream is a fast, scalable, fully managed time series database service that makes it easy to store and analyze trillions of time series data points per day. With Timestream, you can easily store and analyze IoT sensor data to derive insights from your IoT applications. You can analyze industrial telemetry to streamline equipment management and maintenance. You can also store and analyze log data and metrics to improve the performance and availability of your applications. Timestream is built from the ground up to effectively ingest, process, and store time series data. It organizes data to optimize query processing. It automatically scales based on the volume of data ingested and on the query volume to ensure you receive optimal performance while inserting and querying data. As your data grows over time, Timestream’s adaptive query processing engine spans across storage tiers to provide fast analysis while reducing costs.