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.
Setting Up the Integration
Follow these steps to configure the Monte Carlo integration within Paradime.
Step 1: Generate API Key and API ID
Log in to your Monte Carlo account.
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".
Step 2: Add API Credentials to Paradime
From the Paradime home page, click the Settings icon (⚙️) on the bottom right hand side of the screen
Navigate to Workspaces > Environment Variables
In the Bolt Schedules section, add the following variables and their respective values from Step 1:
MCD_DEFAULT_API_TOKEN
MCD_DEFAULT_API_ID
Click the Save icon (💾)
Step 3: Set Your Project Name
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.
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.
If you prefer you can also use the list command in the Monte Carlo CLI to retrieve your connection ID (UUID).
Step 5: Add the Connection ID
Copy the Connection ID from the logs.
Go back to the Environment Variables section in Paradime.
Add the following variable:
MONTECARLO_CONNECTION_ID
Click Save to confirm.
Step 6: Enable the Integration
This Flag will enable uploading automatically all schedules dbt run artifacts to Montecarlo.
In the same Environment Variables section, add the following variable:
RUN_MONTECARLO_UPLOAD
Set its value to
TRUE
.
By the end of this step, your Monte Carlo environment variables should include:
Testing the Integration
To verify the integration, run the following steps in Paradime's Bolt:
Trigger a Run for one of you Bolt schedule which which contains either
dbt build
,dbt run
ordbt test
command.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.
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