Monte Carlo
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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.
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.
Follow these steps to configure the Monte Carlo integration within Paradime.
Log in to your Monte Carlo account.
Follow the instructions in to generate:
API Key
API ID
From the Paradime home page, click the Settings icon (⚙️) on the bottom right hand side of the screen
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
In the same Bolt Schedules section, add:
MONTECARLO_PROJECT_NAME
Set a value for the project name
Copy the Connection ID from the logs.
Add the following variable:
MONTECARLO_CONNECTION_ID
Click Save to confirm.
RUN_MONTECARLO_UPLOAD
Set its value to TRUE
.
By the end of this step, your Monte Carlo environment variables should include:
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
or dbt 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.
Navigate to Workspaces >
You can reuse or create any name that aligns with your dbt models.
The Connection ID identifies the warehouse or lake connection in Monte Carlo. You can do this by retrieving the connection UUID via the API through the by running the below query.
If you prefer you can also use the command in the Monte Carlo CLI to retrieve your connection ID (UUID).
Go back to the section in Paradime.
In the same section, add the following variable:
For more details on the logs that Montecarlo will ingest check the .