Commit fd68d65

* **data quality / drift detection**: Feast now includes built-in [Feature Quality Monitoring](how-to-guides/feature-monitoring.md) that computes statistical metrics (null rates, distributions, percentiles), detects drift across batch data and serving logs, and provides a monitoring UI dashboard. The older Great Expectations integration is deprecated.

* [\[Deprecated\] Validating historical features with Great Expectations](tutorials/validating-historical-features.md)

* [\[Deprecated\] Data quality monitoring (Great Expectations)](reference/dqm.md)

Feast needed a mechanism to validate data at retrieval time to catch these issues before they affect model training or serving.

Introduce a Data Quality Monitoring (DQM) module that validates datasets against user-curated rules, initially targeting historical retrieval (training dataset generation).

The validation process uses a **reference dataset** and a **profiler** pattern:

from feast.dqm.profilers.ge_profiler import ge_profiler

from great_expectations.dataset import Dataset

from great_expectations.core.expectation_suite import ExpectationSuite

def my_profiler(dataset: Dataset) -> ExpectationSuite:

dataset.expect_column_max_to_be_between("column", 1, 2)

dataset.expect_column_values_to_not_be_null("important_feature")

return dataset.get_expectation_suite()

Validation is triggered during historical feature retrieval via a `validation_reference` parameter:

from feast import FeatureStore

store = FeatureStore(".")

job = store.get_historical_features(...)

df = job.to_df(

validation_reference=store

.get_saved_dataset("my_reference_dataset")

.as_reference(profiler=my_profiler)

)

If validation fails, a `ValidationFailed` exception is raised with details for all expectations that didn't pass. If validation succeeds, the materialized dataset is returned normally.

- **Profiler-based approach**: Users define their own validation rules via profiler functions rather than Feast prescribing fixed validation rules.

- **Great Expectations integration**: Leverages an established data validation framework rather than building custom validation logic.

- **Validation at retrieval time**: Validation is performed when datasets are materialized (`.to_df()` or `.to_arrow()`), not during ingestion.

- **ValidationReference as a registry object**: Saved datasets and their validation references are stored in the Feast registry for reuse.

- Flexible profiler pattern allows custom validation rules per use case.

- Integration with Great Expectations provides a rich set of built-in expectations.

- Reference datasets provide a baseline for detecting data drift.

- Currently limited to historical retrieval; online store write/read validation is planned but not yet implemented.

- Dependency on Great Expectations adds to the install footprint (optional via `feast[ge]`).

- Automatic profiling capabilities are limited; manual expectation crafting is recommended.

This ADR documents the original GE-based approach which is now **deprecated**. It has been superseded by Feast's built-in [Feature Quality Monitoring](../how-to-guides/feature-monitoring.md) system (introduced in 2025), which provides:

- Automatic metric computation (null rates, percentiles, histograms) with no external dependencies

- Monitoring across batch data and serving logs

- CLI (`feast monitor run`) and REST API for automation

- Built-in UI monitoring dashboard

- Support for all offline store backends via SQL push-down

The GE-based integration may be removed in a future release.

- Original RFC: Feast RFC-027: Data Quality Monitoring

- Implementation: `sdk/python/feast/dqm/`, `sdk/python/feast/saved_dataset.py`

- Documentation: [Data Quality Monitoring (deprecated)](../reference/dqm.md)

- **New system:** [Feature Quality Monitoring](../how-to-guides/feature-monitoring.md)

* [Experimental] Data quality monitoring, which allows you to validate your training data with Great Expectations. Future work will allow you to detect issues with upstream data pipelines and check for training-serving skew.

Feast 0.18 includes the first milestone of our data quality monitoring work. Many users have requested ways to validate their training and serving data, as well as monitor for training-serving skew. Feast 0.18 allows users to validate their training data through an integration with [Great Expectations](https://greatexpectations.io/). Users can declare one of the previously generated training datasets as a reference for this validation by persisting it as a "saved dataset" (see previous section). More details about future milestones of data quality monitoring can be found [here](https://docs.feastsite.wpenginepowered.com/v/master/reference/data-quality). There's also a [tutorial on validating historical features](https://docs.feastsite.wpenginepowered.com/v/master/how-to-guides/validation/validating-historical-features) that demonstrates all new concepts in action.

Feast datasets allow for conveniently saving dataframes that include both features and entities to be subsequently used for data analysis and model training. Data Quality Monitoring was the original motivation for creating the dataset concept. Note that the Great Expectations-based validation that used saved datasets is now deprecated in favor of Feast's built-in [Feature Quality Monitoring](../../how-to-guides/feature-monitoring.md) system, which does not require saved datasets.

* `dqm/` covers data quality monitoring. The legacy Great Expectations profiler (`profilers/ge_profiler`) is deprecated; see [`monitoring/`](../../sdk/python/feast/monitoring/) for the current built-in monitoring system.