Data Quality Monitoring

Feast's Data Quality Monitoring (DQM) system computes, stores, and serves statistical metrics for every registered feature. It gives you visibility into feature health — distributions, null rates, percentiles, histograms — across batch data and feature serving logs.

Its goal is to address several complex data problems:

• 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.

Overview

Feast's DQM system works natively with your configured offline store — no additional infrastructure or external dependencies are required. The workflow is:

1. Register features — run feast apply to register feature views. If auto_baseline: true is configured, baseline metrics are computed automatically.
2. Schedule monitoring — run feast monitor run on a schedule (daily recommended) to compute metrics across multiple time windows.
3. Read metrics — query metrics via the REST API or view them in the Feast UI.

Configuration

Enable DQM in your feature_store.yaml:

data_quality_monitoring:
  auto_baseline: true

Computing Metrics

Auto mode (recommended for production):

feast monitor run

This detects the latest event timestamp in the source data and computes metrics for 5 time windows: daily, weekly, biweekly, monthly, and quarterly.

Target a specific feature view:

feast monitor run --feature-view driver_stats

Explicit date range:

feast monitor run \
  --feature-view driver_stats \
  --start-date 2025-01-01 \
  --end-date 2025-01-07 \
  --granularity weekly

Set a manual baseline:

feast monitor run \
  --feature-view driver_stats \
  --start-date 2025-01-01 \
  --end-date 2025-03-31 \
  --granularity daily \
  --set-baseline

Monitoring Feature Serving Logs

If your feature services have logging configured, you can compute metrics from the actual features served to models in production:

feast monitor run --source-type log

Reading Metrics

Metrics are accessible via the REST API:

GET /monitoring/metrics/features?project=my_project&feature_view_name=driver_stats&granularity=daily

See the Feature Quality Monitoring guide for full API reference, UI integration, and orchestrator examples.