> For the complete documentation index, see [llms.txt](https://docs.feast.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feast.dev/master/project/adr/adr-0011-data-quality-monitoring.md).

# ADR-0011: Data Quality Monitoring

## Status

Superseded — The original external-library-based validation has been replaced by Feast's native [Feature Quality Monitoring](/master/how-to-guides/feature-monitoring.md) system (`feast monitor run`).

## Context

Data quality issues can significantly impact ML model performance. Several complex data problems needed to be addressed:

* **Data consistency**: New training datasets can differ significantly from previous datasets, potentially requiring changes in model architecture.
* **Upstream pipeline bugs**: Bugs in upstream pipelines can cause invalid values to overwrite existing valid values in an online store.
* **Training/serving skew**: Distribution shift between training and serving data can decrease model performance.

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

## Decision

Introduce a Data Quality Monitoring (DQM) module that validates datasets against user-curated rules.

### Original Design (now replaced)

The original validation process used a **reference dataset** and a **profiler** pattern:

1. User prepares a reference dataset (saved from a known-good historical retrieval).
2. User defines a profiler function that produces a profile (set of expectations) from a dataset.
3. Validation is performed by comparing the tested dataset against the reference profile.

This approach was limited to historical retrieval only, required additional dependencies, and offered no built-in UI or automation.

### Current Design

The current system (`feast monitor run`) provides:

* Automatic metric computation (null rates, percentiles, histograms) with no external dependencies
* Monitoring across batch data and serving logs
* CLI and REST API for automation
* Built-in UI monitoring dashboard
* Support for all offline store backends via SQL push-down

See [Feature Quality Monitoring](/master/how-to-guides/feature-monitoring.md) for full documentation.

## Consequences

### Positive

* Users can detect data quality issues before they affect model training.
* Native integration requires no extra dependencies.
* Covers both batch data and serving logs.
* Built-in UI provides immediate visibility into feature health.
* Baselines computed automatically on `feast apply`.

### Negative

* Migration required from the original profiler-based approach.

## References

* Original RFC: Feast RFC-027: Data Quality Monitoring
* Implementation: `sdk/python/feast/monitoring/`
* Documentation: [Data Quality Monitoring](/master/reference/dqm.md)
* [Feature Quality Monitoring guide](/master/how-to-guides/feature-monitoring.md)


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