@kapa.ai I have this DAG in airflow:

dbt_freshness = dbt_task_factory.get_task(
        dag_id="freshness_run",
        arguments=[
            "dbt",
            "source",
            "freshness",
        ],
        pool="dbt",
        extra_env_vars=extra_env_vars,
    )

if the freshness test fails on some models, will there be any alerts?

Running

dbt source freshness

alone is not enough to trigger Elementary alerts. You need additional steps after the freshness run. What's Missing Elementary does not automatically capture

dbt source freshness

results unless you take one of the following approaches: Option 1: dbt 1.8+ (recommended) Add this flag to your

dbt_project.yml

to enable automatic collection via on-run-end hooks:

flags:
  source_freshness_run_project_hooks: True

Then run

edr monitor

after the freshness command. [Collect source freshness] Option 2: dbt < 1.8 (or as a fallback) You must manually upload the results before monitoring:

dbt source freshness
edr run-operation upload-source-freshness --project-dir <path to your project>
edr monitor

[Collect source freshness] Updated Airflow DAG Your DAG should be extended to include the upload and monitor steps, for example:

dbt_freshness >> upload_source_freshness >> edr_monitor

Where: •

upload_source_freshness

runs

edr run-operation upload-source-freshness --project-dir <path>

•

edr_monitor

runs

edr monitor

with your Slack/Teams configuration > Note:

dbt source freshness

and

upload-source-freshness

must run on the same machine. [Teams alerts guide] Also, by default Elementary only alerts on errors, not warnings. If you want warning-level freshness alerts too, add this to your `dbt_project.yml`:

vars:
  disable_warn_alerts: false

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@kapa.ai how to run

edr monitor

after the freshness command exactly?

Here's how to add the

edr monitor

step after your freshness task in Airflow, using `BashOperator`:

from airflow.operators.bash_operator import BashOperator

upload_source_freshness = BashOperator(
    task_id='upload_source_freshness',
    bash_command=f"edr run-operation upload-source-freshness --project-dir {os.getenv('DBT_PROJECT_DIR')}"
)

edr_monitor = BashOperator(
    task_id='elementary_monitor',
    bash_command=f"edr monitor --profiles-dir {os.getenv('DBT_PROFILES_DIR')} --slack-token {os.getenv('SLACK_TOKEN')} --slack-channel-name {os.getenv('EDR_SLACK_CHANNEL')}"
)

dbt_freshness >> upload_source_freshness >> edr_monitor

The order matters: [Collect source freshness] 1.

dbt source freshness

— runs the freshness checks 2.

edr run-operation upload-source-freshness

— uploads the results to Elementary's tables 3.

edr monitor

— reads the uploaded results and sends alerts > Note: Steps 1 and 2 must run on the same machine. [Teams alerts guide] Also make sure

edr

is installed in your Airflow worker environment:

pip install 'elementary-data[<your-warehouse>]'

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