{% hint style="warning" %} Deprecated: The Great Expectations-based validation described on this page is deprecated and will be removed in a future release. It has been superseded by Feast's built-in Feature Quality Monitoring system, which provides richer metrics (histograms, percentiles, drift detection), works across batch data and serving logs, requires no external dependencies, and includes a built-in UI dashboard.

Please migrate to the new monitoring system. See the Feature Quality Monitoring guide for setup instructions. {% endhint %}

The following documents the deprecated Great Expectations-based validation that was previously the only DQM option in Feast. This integration relied on pip install 'feast[ge]' and only supported validation during historical retrieval.

The legacy validation process consists of the following steps:

1. User prepares reference dataset (only saved datasets from historical retrieval are supported).
2. User defines a profiler function that produces a profile using Great Expectations.
3. Validation of the tested dataset is performed with the reference dataset and profiler provided as parameters.

pip install 'feast[ge]'

This integration uses Great Expectation's ExpectationSuite as the dataset profile format. The user defines a profiler function that receives a dataset and returns an ExpectationSuite.

from great_expectations.dataset import Dataset
from great_expectations.core.expectation_suite import ExpectationSuite

from feast.dqm.profilers.ge_profiler import ge_profiler

@ge_profiler
def manual_profiler(dataset: Dataset) -> ExpectationSuite:
    dataset.expect_column_max_to_be_between("column", 1, 2)
    return dataset.get_expectation_suite()

During retrieval of historical features, validation_reference can be passed as a parameter to methods .to_df(validation_reference=...) or .to_arrow(validation_reference=...) of RetrievalJob. If validation is successful, the materialized dataset is returned. Otherwise, feast.dqm.errors.ValidationFailed exception is raised with details for expectations that didn't pass.

from feast import FeatureStore

fs = FeatureStore(".")

job = fs.get_historical_features(...)
job.to_df(
    validation_reference=fs
        .get_saved_dataset("my_reference_dataset")
        .as_reference(profiler=manual_profiler)
)

The new Feature Quality Monitoring system replaces this integration with:

To migrate:

1. Enable DQM in feature_store.yaml:

   data_quality_monitoring:
     auto_baseline: true

2. Run feast apply to compute baseline metrics automatically.

3. Schedule feast monitor run for ongoing monitoring.

4. Remove the feast[ge] dependency from your requirements.