Monte Carlo Commands

Overview

Paradime Bolt integrates with by Montecarlo feeding the manifest file, run results file, and optionally, a file containing the logs generated by dbt™ during command execution. This will make dbt™ metadata available within the Monte Carlo dashboard.

Prerequisites

• Configure the Monte Carlo integration.

• Whenever a dbt command is executed, files in the target directory will be overwritten. So you will need to run the MC CLI command after each dbt command.

Note

If you want to enable Paradime to automatically send all dbt™ artifacts for all runs, you can choose to set the variableRUN_MONTECARLO_UPLOAD to TRUE.

import dbt-run#

Import dbt run artifacts CLI

montecarlo import dbt-run [OPTIONS]

Flag	Type	Description
--project-name	Optional, TEXT	Project name (perhaps a logical group of dbt models, analogous to a workspace in Paradime) - Default: default-project
--job-name	Optional, TEXT	Job name (perhaps a logical sequence of dbt executions, analogous to a schedule name in Paradime) - Default: default-job
--manifest	Required, TEXT	Path to the dbt manifest file (manifest.json)
--run-results	Required, TEXT	Path to the dbt run results file (run_results.json)
--logs	Required, TEXT	Path to a file containing dbt run logs
--connection-id	Required, TEXT	Identifier of warehouse or lake connection to use to resolve dbt models to tables. Required if you have more than one warehouse or lake connection.

Check Monte Carlo's CLI Reference for a complete list of CLI arguments.

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Now, let's learn how to configure the Trigger Types of a Bolt Schedule.