Mock sample for your project: AWS AppSync API

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 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 Managed Service for Prometheus

Amazon GameLift Service GameLift provides solutions for hosting session-based multiplayer game servers in the cloud, including tools for deploying, operating, and scaling game servers. Built on AWS global computing infrastructure, GameLift helps you deliver high-performance, high-reliability, low-cost game servers while dynamically scaling your resource usage to meet player demand. About GameLift solutions Get more information on these GameLift solutions in the GameLift Developer Guide. GameLift managed hosting -- GameLift offers a fully managed service to set up and maintain computing machines for hosting, manage game session and player session life cycle, and handle security, storage, and performance tracking. You can use automatic scaling tools to balance player demand and hosting costs, configure your game session management to minimize player latency, and add FlexMatch for matchmaking. Managed hosting with Realtime Servers -- With GameLift Realtime Servers, you can quickly configure and set up ready-to-go game servers for your game. Realtime Servers provides a game server framework with core GameLift infrastructure already built in. Then use the full range of GameLift managed hosting features, including FlexMatch, for your game. GameLift FleetIQ -- Use GameLift FleetIQ as a standalone service while hosting your games using EC2 instances and Auto Scaling groups. GameLift FleetIQ provides optimizations for game hosting, including boosting the viability of low-cost Spot Instances gaming. For a complete solution, pair the GameLift FleetIQ and FlexMatch standalone services. GameLift FlexMatch -- Add matchmaking to your game hosting solution. FlexMatch is a customizable matchmaking service for multiplayer games. Use FlexMatch as integrated with GameLift managed hosting or incorporate FlexMatch as a standalone service into your own hosting solution. About this API Reference This reference guide describes the low-level service API for Amazon GameLift. With each topic in this guide, you can find links to language-specific SDK guides and the AWS CLI reference. Useful links: GameLift API operations listed by tasks GameLift tools and resources

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.

AWS App Runner AWS App Runner is an application service that provides a fast, simple, and cost-effective way to go directly from an existing container image or source code to a running service in the AWS cloud in seconds. You don't need to learn new technologies, decide which compute service to use, or understand how to provision and configure AWS resources. App Runner connects directly to your container registry or source code repository. It provides an automatic delivery pipeline with fully managed operations, high performance, scalability, and security. For more information about App Runner, see the AWS App Runner Developer Guide. For release information, see the AWS App Runner Release Notes. To install the Software Development Kits (SDKs), Integrated Development Environment (IDE) Toolkits, and command line tools that you can use to access the API, see Tools for Amazon Web Services. Endpoints For a list of Region-specific endpoints that App Runner supports, see AWS App Runner endpoints and quotas in the AWS General Reference.

The operations for managing an Amazon MSK cluster.

Amazon Inspector Amazon Inspector enables you to analyze the behavior of your AWS resources and to identify potential security issues. For more information, see Amazon Inspector User Guide.

Welcome to the Audit Manager API reference. This guide is for developers who need detailed information about the Audit Manager API operations, data types, and errors. Audit Manager is a service that provides automated evidence collection so that you can continuously audit your Amazon Web Services usage, and assess the effectiveness of your controls to better manage risk and simplify compliance. Audit Manager provides pre-built frameworks that structure and automate assessments for a given compliance standard. Frameworks include a pre-built collection of controls with descriptions and testing procedures, which are grouped according to the requirements of the specified compliance standard or regulation. You can also customize frameworks and controls to support internal audits with unique requirements. Use the following links to get started with the Audit Manager API: Actions : An alphabetical list of all Audit Manager API operations. Data types : An alphabetical list of all Audit Manager 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 Audit Manager, we recommend that you review the Audit Manager User Guide.

App Mesh is a service mesh based on the Envoy proxy that makes it easy to monitor and control microservices. App Mesh standardizes how your microservices communicate, giving you end-to-end visibility and helping to ensure high availability for your applications. App Mesh gives you consistent visibility and network traffic controls for every microservice in an application. You can use App Mesh with Amazon Web Services Fargate, Amazon ECS, Amazon EKS, Kubernetes on Amazon Web Services, and Amazon EC2. App Mesh supports microservice applications that use service discovery naming for their components. For more information about service discovery on Amazon ECS, see Service Discovery in the Amazon Elastic Container Service Developer Guide. Kubernetes kube-dns and coredns are supported. For more information, see DNS for Services and Pods in the Kubernetes documentation.

Use these APIs to manage Azure Front Door resources through the Azure Resource Manager. You must make sure that requests made to these resources are secure.

The Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

The Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

The Managed Service Identity Client.

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

APIs to manage web application firewall rules for Azure CDN

Azure Cosmos DB Database Service Resource Provider REST API

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

The Compute Management Client.

The Azure Data Catalog Resource Provider Services API.