API

Monte Carlo’s API enables seamless integration with your data ecosystem, allowing data teams to programmatically monitor, manage, and extract insights from their Monte Carlo Environment. With endpoints designed for flexibility and scalability, the API supports workflows such as querying monitor and alert metadata, bulk updates to MC settings and configurations, and opportunities for automation.

Monte Carlo’s UI is a GraphQL interface, enabling users to query and interact with their data efficiently. This flexible API allows you to tailor requests to retrieve precisely the data you need, empowering seamless integration with your workflows and tools. Every piece of information presented in the UI can be retrieved programmatically, empowering customers to leverage Monte Carlo’s data for custom integrations, automations, and analysis directly within their own environments.

API Endpoint

Since we have are a GraphQL interface, you will be making all calls to the following one endpoint:

https://api.getmontecarlo.com/graphql

Why Use the API?

Monte Carlo’s API provides direct access to the same powerful functionality available in the UI, offering:

• Automation: Streamline workflows by automating repetitive tasks.
• Customization: Build solutions tailored to your organization’s needs.
• Flexibility: Integrate Monte Carlo's capabilities into your existing tools and processes.
• Scalability: Efficiently manage large-scale operations by deploying configuration or settings changes across your environment. Use the API to perform bulk updates, such as adjusting alert configurations, modifying monitoring settings, or applying changes to multiple data assets simultaneously, saving time and reducing manual effort as your data environment grows.

Getting Started

1. Authenticate: Use your API key to securely access endpoints.
2. Explore Endpoints: Visit the API Reference to browse available endpoints.
3. Try It Out: Test API calls using a REST API client or Monte Carlo's API Explorer.

Example Use Cases

• Presenting data health metrics and incident analytics on dashboards (e.g. on DataDog)
• Enabling automatic custom monitoring configurations when adding new tables in your ETL code
• Augmenting the lineage that Monte Carlo automatically detects with additional resources and dependencies (e.g. external data sources, streaming sources, ML models, custom BI reports and other upstream/downstream dependencies)
• Performing custom analysis on your data ecosystem to better understand how assets are being used, typically for capacity planning, performance optimization, data debt reduction, etc
• Feeding additional tags and metadata into Monte Carlo’s catalog for a complete view and easier discovery

Determining Which Calls Are Best for Your Use Case

Every piece of information presented in the UI can be retrieved programmatically, empowering customers to leverage Monte Carlo’s data for custom integrations, automations, and analysis directly within their own environments.

By inspecting any MC page you can examine the queries used to populate the page, along with the variables they require. See this Knowledge Based Article for more suggestions on how to find which queries you need.

See this page to check out some example calls of our customers’ most common use cases.