Mock sample for your project: AWS Lambda 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 Textract detects and analyzes text in documents and converts it into machine-readable text. This is the API reference documentation for Amazon Textract.

Contains all data plane API operations and data types for the Amazon SageMaker Feature Store. Use this API to put, delete, and retrieve (get) features from a feature store. Use the following operations to configure your OnlineStore and OfflineStore features, and to create and manage feature groups: CreateFeatureGroup DeleteFeatureGroup DescribeFeatureGroup ListFeatureGroups

Amazon Lightsail is the easiest way to get started with Amazon Web Services (AWS) for developers who need to build websites or web applications. It includes everything you need to launch your project quickly - instances (virtual private servers), container services, storage buckets, managed databases, SSD-based block storage, static IP addresses, load balancers, content delivery network (CDN) distributions, DNS management of registered domains, and resource snapshots (backups) - for a low, predictable monthly price. You can manage your Lightsail resources using the Lightsail console, Lightsail API, AWS Command Line Interface (AWS CLI), or SDKs. For more information about Lightsail concepts and tasks, see the Amazon Lightsail Developer Guide. This API Reference provides detailed information about the actions, data types, parameters, and errors of the Lightsail service. For more information about the supported AWS Regions, endpoints, and service quotas of the Lightsail service, see Amazon Lightsail Endpoints and Quotas in the AWS General Reference.

Amazon CloudWatch monitors your Amazon Web Services (Amazon Web Services) resources and the applications you run on Amazon Web Services in real time. You can use CloudWatch to collect and track metrics, which are the variables you want to measure for your resources and applications. CloudWatch alarms send notifications or automatically change the resources you are monitoring based on rules that you define. For example, you can monitor the CPU usage and disk reads and writes of your Amazon EC2 instances. Then, use this data to determine whether you should launch additional instances to handle increased load. You can also use this data to stop under-used instances to save money. In addition to monitoring the built-in metrics that come with Amazon Web Services, you can monitor your own custom metrics. With CloudWatch, you gain system-wide visibility into resource utilization, application performance, and operational health.

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.

AWS Single Sign-On Portal is a web service that makes it easy for you to assign user access to AWS SSO resources such as the user portal. Users can get AWS account applications and roles assigned to them and get federated into the application. For general information about AWS SSO, see What is AWS Single Sign-On? in the AWS SSO User Guide. This API reference guide describes the AWS SSO Portal operations that you can call programatically and includes detailed information on data types and errors. AWS provides SDKs that consist of libraries and sample code for various programming languages and platforms, such as Java, Ruby, .Net, iOS, or Android. The SDKs provide a convenient way to create programmatic access to AWS SSO and other AWS services. For more information about the AWS SDKs, including how to download and install them, see Tools for Amazon Web Services.

This is the Amazon Lookout for Vision API Reference. It provides descriptions of actions, data types, common parameters, and common errors. Amazon Lookout for Vision enables you to find visual defects in industrial products, accurately and at scale. It uses computer vision to identify missing components in an industrial product, damage to vehicles or structures, irregularities in production lines, and even minuscule defects in silicon wafers — or any other physical item where quality is important such as a missing capacitor on printed circuit boards.

Amazon Macie Classic Amazon Macie Classic is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Macie Classic recognizes sensitive data such as personally identifiable information (PII) or intellectual property, and provides you with dashboards and alerts that give visibility into how this data is being accessed or moved. For more information, see the Amazon Macie Classic User Guide.

Use these REST APIs for getting the network connectivity status of your Azure API Management deployment. When the API Management service is deployed inside a Virtual Network, it needs to have access to other Azure resources it depends on. This also gives details about the DNS Servers visible to Azure API Management deployment.

The Admin Subscriptions Management Client.

Use these REST APIs for querying APIs. Operations and Products by tags in your Azure API Management deployment.

The Amazon API Gateway Management API allows you to directly manage runtime aspects of your deployed APIs. To use it, you must explicitly set the SDK's endpoint to point to the endpoint of your deployed API. The endpoint will be of the form https://{api-id}.execute-api.{region}.amazonaws.com/{stage}, or will be the endpoint corresponding to your API's custom domain and base path, if applicable.

Edge gateway operation endpoints and objects.

Amazon HealthLake is a HIPAA eligibile service that allows customers to store, transform, query, and analyze their FHIR-formatted data in a consistent fashion in the cloud.

This section provides documentation for the AWS RoboMaker API operations.

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.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These operations enable you to get deny assignments. A deny assignment describes the set of actions on resources that are denied for Azure Active Directory users.

AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use the AWS Data Exchange APIs to create, update, manage, and access file-based data set in the AWS Cloud. As a subscriber, you can view and access the data sets that you have an entitlement to through a subscription. You can use the APIS to download or copy your entitled data sets to Amazon S3 for use across a variety of AWS analytics and machine learning services. As a provider, you can create and manage your data sets that you would like to publish to a product. Being able to package and provide your data sets into products requires a few steps to determine eligibility. For more information, visit the AWS Data Exchange User Guide. A data set is a collection of data that can be changed or updated over time. Data sets can be updated using revisions, which represent a new version or incremental change to a data set. A revision contains one or more assets. An asset in AWS Data Exchange is a piece of data that can be stored as an Amazon S3 object. The asset can be a structured data file, an image file, or some other data file. Jobs are asynchronous import or export operations used to create or copy assets.