Monte Carlo

What is Monte Carlo?

Monte Carlo is a leading data observability platform that helps data teams monitor, resolve, and prevent data quality issues. It provides insights into the health, freshness, and lineage of your data assets across your entire data stack.

Value of Monte Carlo with Paradime

Integrating Monte Carlo with Paradime enables teams to centralize data observability and enhance the monitoring of production jobs (Bolt schedules) and dbt™ models. Key benefits include:

• Enhanced Observability: Overlay dbt™ context onto Monte Carlo's lineage graph for easier troubleshooting.

• Incident Detection: Detect and centralize dbt™ model errors, test failures, and other data incidents in one place.

• Run Insights: Visualize dbt™ job execution times, success/error statuses, and run histories.

• Simplified Impact Analysis: Evaluate downstream and upstream impacts of dbt™ transformations on table updates.

With this integration, data teams can proactively address failures, optimize dbt™ models, and ensure reliable data pipelines.

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Setting Up the Integration

Follow these steps to configure the Monte Carlo integration within Paradime.

Step 1: Generate API Key and API ID

1. Log in to your Monte Carlo account.

2. Follow the instructions in Monte Carlo Docs to generate:

   • API Key

   • API ID

The key is required to be generated with the "Editor" or "Owner" roles, for example if you create a Service Account Key you need to select "Editors" or "Account Owners" under "Authorization Groups".

If you're using a personal key, the user that generated it needs to be an "Editor" or "Owner".

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Step 2: Add API Credentials to Paradime

1. In the Paradime UI, go to: Settings → Workspace → Environment Variables.

2. Add the following variables and their respective values from Step 1:

   • MCD_DEFAULT_API_TOKEN

   • MCD_DEFAULT_API_ID

3. Click Save to confirm the changes.

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Step 3: Set Your Project Name

1. In the same Environment Variables section, add the following variable:

   • MONTECARLO_PROJECT_NAME

2. Set a value for the project name.

You can reuse your existing dbt project name or create any name that aligns with your dbt models.

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Step 4: Obtain the Connection ID

The Connection ID identifies the warehouse or lake connection in Monte Carlo. Follow these steps to retrieve it:

1. Navigate to Bolt in Paradime.

2. Create a temporary Bolt schedule:

   • Go to Schedules → + New Schedule → Create New Schedule

   • Provide a schedule name.

3. In the command settings, enter the following Monte Carlo command:

   montecarlo integrations list

4. Click Deploy.

5. From the Bolt Home Screen, select the newly created schedule and click Run.

6. In the Run History, open the most recent execution logs.

7. Locate the Connection ID in the Run Logs.

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Step 5: Add the Connection ID

1. Copy the Connection ID from the logs.

2. Go back to the Environment Variables section in Paradime.

3. Add the following variable:

   • MONTECARLO_CONNECTION_ID

4. Click Save to confirm.

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Step 6: Enable the Integration

1. In the same Environment Variables section, add the following variable:

   • RUN_MONTECARLO_UPLOAD

2. Set its value to TRUE.

By the end of this step, your Monte Carlo environment variables should include:

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Testing the Integration

To verify the integration, run the following steps in Paradime's Bolt:

1. Trigger a Run for one of you Bolt schedule which which contains either dbt build, dbt run or dbt test command.

2. Verify the results in Monte Carlo:

   • Check the lineage graph for updated dbt™ context.

   

   • View job statuses, model run results, and test outcomes.

   

For more details on the logs that Montecarlo will ingest check the Montecarlo dbt integration documentation.