Mock sample for your project: Control API v1

Integrate with "Control API v1" from ably.net in no time with Mockoon's ready to use mock sample

Control API v1

ably.net

Version: 1.0.14


Use this API in your project

Speed up your application development by using "Control API v1" ready-to-use mock sample. Mocking this API will help you accelerate your development lifecycles and allow you to stop relying on an external API to get the job done. No more API keys to provision, accesses to configure or unplanned downtime, just work.
Enhance your development infrastructure by mocking third party APIs during integrating testing.

Description

Use the Control API to manage your applications, namespaces, keys, queues, rules, and more.
Detailed information on using this API can be found in the Ably developer documentation.
Control API is currently in Beta.

Other APIs in the same category

LUIS Authoring Client

azure.com

Amazon Neptune

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 Lex Model Building V2

AmazonMWAA

Amazon Managed Workflows for Apache Airflow This section contains the Amazon Managed Workflows for Apache Airflow (MWAA) API reference documentation. For more information, see What Is Amazon MWAA?.

FabricAdminClient

azure.com
File share operation endpoints and objects.

MonitorManagementClient

azure.com

Content Moderator Client

azure.com
You use the API to scan your content as it is generated. Content Moderator then processes your content and sends the results along with relevant information either back to your systems or to the built-in review tool. You can use this information to take decisions e.g. take it down, send to human judge, etc.
When using the API, images need to have a minimum of 128 pixels and a maximum file size of 4MB.
Text can be at most 1024 characters long.
If the content passed to the text API or the image API exceeds the size limits, the API will return an error code that informs about the issue.

SqlManagementClient

azure.com
The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.

Amazon Kinesis Video Streams

AWS IoT SiteWise

Welcome to the IoT SiteWise API Reference. IoT SiteWise is an Amazon Web Services service that connects Industrial Internet of Things (IIoT) devices to the power of the Amazon Web Services Cloud. For more information, see the IoT SiteWise User Guide. For information about IoT SiteWise quotas, see Quotas in the IoT SiteWise User Guide.

Azure Maps Resource Provider

azure.com
Resource Provider

Amazon Macie 2

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect your sensitive data in AWS. Macie automates the discovery of sensitive data, such as PII and intellectual property, to provide you with insight into the data that your organization stores in AWS. Macie also provides an inventory of your Amazon S3 buckets, which it continually monitors for you. If Macie detects sensitive data or potential data access issues, it generates detailed findings for you to review and act upon as necessary.