@Marvin What is the best way to log dbt runs when using prefect and dbt core. Specifically I want to log which models have run and if they have failed or passed

To log dbt runs, including the status of individual models (whether they have passed or failed), when using Prefect with dbt Core, you'll likely have to leverage Prefect's task logging capabilities alongside dbt's built-in logging. Here's a broad overview of the steps involved: 1. Run dbt Commands in Prefect: Utilize Prefect tasks to run your dbt commands (

dbt run

,

dbt test

, etc.). 2. Capture dbt Logs: dbt provides detailed logs which can be captured and processed. You can configure dbt to write its logs to a file and then read this file in your Prefect task. 3. Parse and Log Model Status: Parse the dbt logs to extract information about which models have run and their status, then use Prefect's logging facilities to log this information. Let me look up relevant documentation and examples to guide you effectively. This won't take long. ### Overview To log dbt runs using Prefect with dbt Core, you can leverage Prefect's tasks and the

prefect-dbt

integration to capture and log the status of your dbt models. ### Steps to Implement #### 1. Install Required Packages Ensure you have the

prefect-dbt

package installed to interact with dbt directly within Prefect.

sh
pip install prefect-dbt

#### 2. Define DbtCoreOperation Set up a dbt operation within your Prefect flow to run the necessary dbt commands. #### 3. Capture and Log the Output Parse the dbt run outputs to log which models have successfully run or failed. Below is a basic example demonstrating how you can achieve this:

python
import json
from prefect import task, flow
from prefect_dbt.cli import DbtCoreOperation
from prefect.logging.loggers import get_run_logger

@task
def run_dbt():
    # Define your dbt operation
    dbt_operation = DbtCoreOperation(commands=["dbt run"], project_dir="/path/to/your/dbt/project", profiles_dir="/path/to/your/dbt/profiles")
    
    # Trigger the dbt operation
    dbt_process = dbt_operation.trigger()
    dbt_process.wait_for_completion()
    
    # Fetch the result (assuming dbt logs are in JSON format)
    result = dbt_process.fetch_result()
    
    return result

@task
def log_dbt_results(result):
    logger = get_run_logger()
    output = result["output"]  # Adjust according to actual dbt output structure

    # Parse and log the dbt run output
    for model_run in output:
        model_name = model_run["model_name"]
        status = model_run["status"]
        <http://logger.info|logger.info>(f"Model {model_name} has {status}")

@flow(log_prints=True)
def dbt_workflow():
    result = run_dbt()
    log_dbt_results(result)

# Execute the flow
if __name__ == "__main__":
    dbt_workflow()

### Resources 1. Prefect Dbt Documentation 2. Example of dbt and Prefect integration in their GitHub repository here The above approach ensures that dbt run statuses are captured and logged in a structured manner using Prefect's logging capabilities. Adjust the paths and details according to your dbt project structure and logging preferences.