Monte Carlo

Learn how to integrate Monte Carlo with Paradime to monitor dbt models, detect data incidents, and enhance data pipeline reliability with step-by-step setup instructions and best practices.

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. From the Paradime home page, click the Settings icon (⚙️) on the bottom right hand side of the screen

2. Navigate to Workspaces > Environment Variables

3. In the Bolt Schedules section, add the following variables and their respective values from Step 1:

   • MCD_DEFAULT_API_TOKEN

   • MCD_DEFAULT_API_ID

4. Click the Save icon (💾)

Step 3: Set Your Project Name

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Step 3: Set Your Project Name

• In the same Bolt Schedules section, add:

  • MONTECARLO_PROJECT_NAME

• 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. You can do this by retrieving the connection UUID via the getUser API through the API Explorer by running the below query.

Example Query

query getConnections {
  getUser {
    email
    account {
      connections {
        uuid
        type
        warehouse {
          name
        }
      }
    }
  }
}

Example response

If you prefer you can also use the list command in the Monte Carlo CLI to retrieve your connection ID (UUID).

% montecarlo integrations list
╒════════════════════════╤══════════════════╤══════════════════════════════════════╤═════════════════════════════════════════════════════════╕
│ Integration            │ Name             │ ID                                   │ Connection           │ Created on (UTC)                 │
╞════════════════════════╪══════════════════╪══════════════════════════════════════╪══════════════════════╪══════════════════════════════════╡
│ Redshift               │ prod-redshift    │ 12345678-1234-1234-1234-123456789012 │ host: redacted       │ 2022-12-14T14:54:15.944774+00:00 │
├────────────────────────┼──────────────────┼──────────────────────────────────────┼──────────────────────┼──────────────────────────────────┤
│ BigQuery               │ prod-bigquery    │ 12345678-1234-1234-1234-123456789013 │ client_id: redacted  │ 2022-12-14T18:02:54.644654+00:00 │
╘════════════════════════╧══════════════════╧══════════════════════════════════════╧══════════════════════╧══════════════════════════════════╛

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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

This Flag will enable uploading automatically all schedules dbt run artifacts to Montecarlo.

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.