How do I add metadata to a DBT asset check that will show me the data causing the failure?

To add metadata to a DBT asset check in Dagster, you can use the

AssetCheckResult

object to attach metadata when defining your asset check. This metadata can include information about the data causing the failure. Here's a step-by-step guide based on the documentation: 1. Define your DBT asset as a software-defined asset in Dagster. 2. Create an asset check function that will perform the check on your DBT asset. Within this function, you can execute your data quality logic and determine if the check passes or fails. 3. If the check fails, you can include metadata in the

AssetCheckResult

that provides details about the data causing the failure. This metadata should be a dictionary where the keys are strings and the values are JSON-serializable. Here's an example of how you might define an asset check with metadata for a DBT asset:

python from dagster import asset_check, AssetCheckResult, AssetKey import pandas as pd  @asset_check(asset_key=AssetKey("my_dbt_asset")) def dbt_asset_check(context):     # Perform your data quality check logic here     # For example, load your DBT asset data and check for failures     df = pd.read_csv("path_to_your_data.csv")     failed_rows = df[df['some_column'] == 'some_failure_condition']          # If there are any failed rows, return an AssetCheckResult with metadata     if not failed_rows.empty:         metadata = {             "failed_rows": failed_rows.to_dict('records'),  # Convert DataFrame to a list of dicts         }         return AssetCheckResult(             passed=False,             metadata=metadata,         )     else:         return AssetCheckResult(passed=True)

In this example, if the check fails, the

failed_rows

metadata will include the data causing the failure, serialized as a list of dictionaries. This metadata will be attached to the asset check result and can be viewed in the Dagster UI. Please note that the metadata must be JSON-serializable, so you may need to convert complex data structures (like Pandas DataFrames) into a format that can be serialized, such as a list of dictionaries as shown above. Keep in mind that the documentation mentions that Asset Checks are an experimental feature and the API may change in future releases. Always refer to the latest Dagster documentation for the most up-to-date guidance on implementing asset checks.

@Scout Can I have the DBT test results reported directly as the metadata?

As of my knowledge cutoff date in early 2023, Dagster does not provide a built-in way to directly report DBT test results as metadata for an asset check. However, Dagster does allow you to define custom asset checks that can include arbitrary metadata, and you can manually extract information from DBT test results and include it as metadata in an

AssetCheckResult

. To achieve this, you would typically run your DBT tests using Dagster's

DbtCliResource

or similar, capture the output of the DBT test command (which includes the test results), parse the relevant information from the output, and then construct an

AssetCheckResult

with the parsed test results as metadata. Here's a conceptual example of how you might approach this:

python from dagster import asset, AssetCheckResult, AssetKey from dagster_dbt import dbt_cli_resource  @asset(required_resource_keys={"dbt"}) def my_dbt_asset(context):     # Run your DBT tests using the DbtCliResource     result = context.resources.dbt.run(operation="test")          # Parse the DBT test results from the result object     # This will depend on the structure of the DBT test output     test_results = parse_dbt_test_results(result)          # Include the test results as metadata in an AssetCheckResult     metadata = {         "dbt_test_results": test_results     }     return AssetCheckResult(         passed=all(test.passed for test in test_results),         metadata=metadata,     )  def parse_dbt_test_results(dbt_result):     # Implement parsing logic here based on the structure of the DBT test output     # This is a placeholder function and needs to be implemented based on actual DBT output     pass

In this example,

parse_dbt_test_results

is a placeholder function that you would need to implement to parse the actual DBT test results from the output of the

dbt test

command. The

dbt_cli_resource

is used to run the DBT tests, and the results are then included in the metadata of the

AssetCheckResult

. Please note that the actual implementation will depend on the format of the DBT test output and how you want to present the test results in the metadata. You may need to refer to the DBT documentation to understand the structure of the test output and how to extract the necessary information. Remember to check the latest Dagster and DBT documentation for any updates or new features that might simplify this process or provide new capabilities for integrating DBT test results with Dagster asset checks.