Mock sample for your project: AWS Marketplace Catalog Service API

Amazon Mobile Analytics is a service for collecting, visualizing, and understanding app usage data at scale.

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.

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

You can use Amazon CloudWatch Logs to monitor, store, and access your log files from EC2 instances, CloudTrail, and other sources. You can then retrieve the associated log data from CloudWatch Logs using the CloudWatch console, CloudWatch Logs commands in the Amazon Web Services CLI, CloudWatch Logs API, or CloudWatch Logs SDK. You can use CloudWatch Logs to: Monitor logs from EC2 instances in real-time : You can use CloudWatch Logs to monitor applications and systems using log data. For example, CloudWatch Logs can track the number of errors that occur in your application logs and send you a notification whenever the rate of errors exceeds a threshold that you specify. CloudWatch Logs uses your log data for monitoring so no code changes are required. For example, you can monitor application logs for specific literal terms (such as "NullReferenceException") or count the number of occurrences of a literal term at a particular position in log data (such as "404" status codes in an Apache access log). When the term you are searching for is found, CloudWatch Logs reports the data to a CloudWatch metric that you specify. Monitor CloudTrail logged events : You can create alarms in CloudWatch and receive notifications of particular API activity as captured by CloudTrail. You can use the notification to perform troubleshooting. Archive log data : You can use CloudWatch Logs to store your log data in highly durable storage. You can change the log retention setting so that any log events older than this setting are automatically deleted. The CloudWatch Logs agent makes it easy to quickly send both rotated and non-rotated log data off of a host and into the log service. You can then access the raw log data when you need it.

Amazon GuardDuty is a continuous security monitoring service that analyzes and processes the following data sources: VPC Flow Logs, AWS CloudTrail event logs, and DNS logs. It uses threat intelligence feeds (such as lists of malicious IPs and domains) and machine learning to identify unexpected, potentially unauthorized, and malicious activity within your AWS environment. This can include issues like escalations of privileges, uses of exposed credentials, or communication with malicious IPs, URLs, or domains. For example, GuardDuty can detect compromised EC2 instances that serve malware or mine bitcoin. GuardDuty also monitors AWS account access behavior for signs of compromise. Some examples of this are unauthorized infrastructure deployments such as EC2 instances deployed in a Region that has never been used, or unusual API calls like a password policy change to reduce password strength. GuardDuty informs you of the status of your AWS environment by producing security findings that you can view in the GuardDuty console or through Amazon CloudWatch events. For more information, see the Amazon GuardDuty User Guide .

Amazon Kinesis Data Streams Service API Reference Amazon Kinesis Data Streams is a managed service that scales elastically for real-time processing of streaming big data.

API for AWS Elemental MediaConnect

AWS IoT Events monitors your equipment or device fleets for failures or changes in operation, and triggers actions when such events occur. You can use AWS IoT Events Data API commands to send inputs to detectors, list detectors, and view or update a detector's status. For more information, see What is AWS IoT Events? in the AWS IoT Events Developer Guide.

Savings Plans are a pricing model that offer significant savings on AWS usage (for example, on Amazon EC2 instances). You commit to a consistent amount of usage, in USD per hour, for a term of 1 or 3 years, and receive a lower price for that usage. For more information, see the AWS Savings Plans User Guide.

AWS IoT Secure Tunneling AWS IoT Secure Tunnling enables you to create remote connections to devices deployed in the field. For more information about how AWS IoT Secure Tunneling works, see AWS IoT Secure Tunneling.

Amazon Managed Blockchain is a fully managed service for creating and managing blockchain networks using open-source frameworks. Blockchain allows you to build applications where multiple parties can securely and transparently run transactions and share data without the need for a trusted, central authority. Managed Blockchain supports the Hyperledger Fabric and Ethereum open-source frameworks. Because of fundamental differences between the frameworks, some API actions or data types may only apply in the context of one framework and not the other. For example, actions related to Hyperledger Fabric network members such as CreateMember and DeleteMember do not apply to Ethereum. The description for each action indicates the framework or frameworks to which it applies. Data types and properties that apply only in the context of a particular framework are similarly indicated.

Creates an Azure Data Lake Analytics job client.

Azure Customer Lockbox API Reference

The Container Service Client.

The service is used to perform ink layout and recognition of written words and shapes. Ink strokes passed to the service are recognized and organized into recognition results in the response

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

The Data Migration Service helps people migrate their data from on-premise database servers to Azure, or from older database software to newer software. The service manages one or more workers that are joined to a customer's virtual network, which is assumed to provide connectivity to their databases. To avoid frequent updates to the resource provider, data migration tasks are implemented by the resource provider in a generic way as task resources, each of which has a task type (which identifies the type of work to run), input, and output. The client is responsible for providing appropriate task type and inputs, which will be passed through unexamined to the machines that implement the functionality, and for understanding the output, which is passed back unexamined to the client.

The Azure Data Catalog Resource Provider Services API.

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.

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.

Azure Bot Service is a platform for creating smart conversational agents.