How to Integrate Feast with Great Expectations for Data Validation

To integrate Feast with Great Expectations for data validation, install the feast[ge] extra, define a profiler function using the @ge_profiler decorator that returns a Great Expectations ExpectationSuite, create a validation reference from a saved dataset, and pass that reference to RetrievalJob.to_df() to automatically validate historical features against your expectations.

Feast's Data Quality Monitoring (DQM) module provides native integration with Great Expectations, allowing you to enforce data quality constraints on historical feature retrievals within the feast-dev/feast repository. This integration compares incoming feature datasets against reference datasets using Great Expectations ExpectationSuite objects generated by user-defined profiler functions.

Architecture Overview

The integration architecture centers on three core components implemented in sdk/python/feast/dqm/:

• GEProfiler and GEProfile: Located in sdk/python/feast/dqm/profilers/ge_profiler.py, the GEProfiler class wraps user-defined functions into a Profile object that produces a GEProfile. The GEProfile.validate() method executes the Great Expectations suite against incoming DataFrames.

• @ge_profiler decorator: This decorator, defined in ge_profiler.py lines 94-104, registers a function that accepts a PandasDataset and returns an ExpectationSuite, converting it into a Feast-compatible profiler.

• validation_reference: A SavedDataset annotated with a profiler via as_reference(), stored as a reference point for subsequent validation runs.

The data flow follows this path: historical retrieval job generates a DataFrame → GEProfile.validate() invokes great_expectations.validate() → produces a ValidationReport → raises ValidationFailed if expectations are violated.

Step-by-Step Integration

Install Feast with Great Expectations Support

Install the optional Great Expectations dependency using the ge extra declared in pyproject.toml:

pip install 'feast[ge]'

This installs great_expectations>=0.15.41,<1 as specified in the project's dependency configuration.

Define a Great Expectations Profiler

Create a profiler function decorated with @ge_profiler that receives a PandasDataset and returns an ExpectationSuite. This example from sdk/python/feast/dqm/profilers/ge_profiler.py demonstrates statistical profiling:

from feast.dqm.profilers.ge_profiler import ge_profiler
from great_expectations.core import ExpectationSuite
from great_expectations.dataset import PandasDataset

DELTA = 0.1  # tolerance for mean/quantile checks

@ge_profiler
def stats_profiler(ds: PandasDataset) -> ExpectationSuite:
    # Range checks for specific columns

    ds.expect_column_values_to_be_between("avg_speed", min_value=0, max_value=60, mostly=0.99)
    ds.expect_column_values_to_be_between("total_miles_travelled", min_value=0, max_value=500, mostly=0.99)
    
    # Mean-based expectations with tolerance

    mean_trip = ds.trip_count.mean()
    ds.expect_column_mean_to_be_between("trip_count",
                                       min_value=mean_trip * (1 - DELTA),
                                       max_value=mean_trip * (1 + DELTA))
    
    mean_earn = ds.earned_per_hour.mean()
    ds.expect_column_mean_to_be_between("earned_per_hour",
                                       min_value=mean_earn * (1 - DELTA),
                                       max_value=mean_earn * (1 + DELTA))
    
    # Quantile checks

    qs = [0.5, 0.75, 0.9, 0.95]
    q_vals = ds.avg_fare.quantile(qs)
    ds.expect_column_quantile_values_to_be_between(
        "avg_fare",
        quantile_ranges={"quantiles": qs,
                        "value_ranges": [[None, v] for v in q_vals]}
    )
    return ds.get_expectation_suite()

The @ge_profiler decorator converts this function into a GEProfiler instance compatible with Feast's DQM pipeline.

Create a Reference Dataset

Generate a validation reference from a saved historical dataset, attaching your profiler:

# Assuming `store` is a FeatureStore instance

ds = store.get_saved_dataset("my_training_ds")
validation_reference = ds.as_reference(name="reference_2021_jun", profiler=stats_profiler)

This creates a reference point containing the ExpectationSuite generated by your profiler against the reference data.

Run Retrieval with Validation

Execute a historical feature retrieval job, passing the validation reference to trigger automatic validation:

from feast.dqm.errors import ValidationFailed

# Create retrieval job

job = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "trip_stats:total_miles_travelled",
        "trip_stats:total_trip_seconds",
        "trip_stats:total_earned",
        "trip_stats:trip_count",
        "on_demand_stats:avg_fare",
        "on_demand_stats:avg_trip_seconds",
        "on_demand_stats:avg_speed",
        "on_demand_stats:earned_per_hour",
    ]
)

# Retrieve with validation

try:
    df = job.to_df(validation_reference=validation_reference)
except ValidationFailed as exc:
    print("Validation errors:")
    print(exc.validation_report)  # Contains list of failing ValidationErrors

else:
    print("All expectations passed – dataset is valid")

When to_df() or to_arrow() is called with validation_reference, Feast automatically runs the Great Expectations suite against the retrieved DataFrame.

Handle Validation Failures

The ValidationFailed exception, imported from feast.dqm.errors, contains a validation_report attribute detailing which expectations failed. This report includes observed values, missing counts, and other diagnostic information from Great Expectations, allowing you to inspect data quality issues programmatically.

Complete Working Example

Here is a minimal end-to-end implementation combining all components:

# 1️⃣ Install (run once)

# !pip install 'feast[ge]'

# 2️⃣ Imports

from feast import FeatureStore
from feast.dqm.profilers.ge_profiler import ge_profiler
from feast.dqm.errors import ValidationFailed
from great_expectations.core import ExpectationSuite
from great_expectations.dataset import PandasDataset
import pandas as pd

# 3️⃣ Define profiler

@ge_profiler
def my_profiler(ds: PandasDataset) -> ExpectationSuite:
    ds.expect_column_values_to_be_between("avg_speed", min_value=0, max_value=60)
    ds.expect_column_values_to_be_between("total_miles_travelled", min_value=0, max_value=500)
    return ds.get_expectation_suite()

# 4️⃣ Load store & create reference

store = FeatureStore(".")
ref_ds = store.get_saved_dataset("my_training_ds")
reference = ref_ds.as_reference(name="ref_june_2021", profiler=my_profiler)

# 5️⃣ Prepare entity dataframe

entity_df = pd.DataFrame({
    "taxi_id": ["taxi_1", "taxi_2"],
    "event_timestamp": pd.to_datetime(["2021-06-01", "2021-06-02"])
})

# 6️⃣ Retrieve features with validation

job = store.get_historical_features(
    entity_df=entity_df,
    features=["trip_stats:total_miles_travelled", "on_demand_stats:avg_fare"]
)

try:
    df = job.to_df(validation_reference=reference)
    print(df.head())
except ValidationFailed as err:
    print("Data quality issues detected:")
    print(err.validation_report)

Key Source Files

File	Purpose	Direct Link
sdk/python/feast/dqm/profilers/ge_profiler.py	Implements GEProfiler, GEProfile, and the ge_profiler decorator that wraps user functions into Great Expectations profilers.	https://github.com/feast-dev/feast/blob/master/sdk/python/feast/dqm/profilers/ge_profiler.py
sdk/python/feast/dqm/errors.py	Defines ValidationFailed exception class that aggregates failing expectations into a validation report.	https://github.com/feast-dev/feast/blob/master/sdk/python/feast/dqm/errors.py
docs/tutorials/validating-historical-features.md	End-to-end tutorial demonstrating the complete validation workflow with code examples.	https://github.com/feast-dev/feast/blob/master/docs/tutorials/validating-historical-features.md
docs/reference/dqm.md	API reference for Data Quality Monitoring, including validation_reference usage.	https://github.com/feast-dev/feast/blob/master/docs/reference/dqm.md
pyproject.toml	Declares the optional ge extra dependency (great_expectations>=0.15.41,<1).	https://github.com/feast-dev/feast/blob/master/pyproject.toml

Summary

Integrating Feast with Great Expectations for data validation involves five key steps:

• Install the feast[ge] extra to include Great Expectations as a dependency.
• Define a profiler function using the @ge_profiler decorator that creates an ExpectationSuite from a PandasDataset.
• Create a validation reference from a saved dataset using as_reference(), attaching your profiler to establish a baseline.
• Trigger validation by passing the reference to RetrievalJob.to_df() or to_arrow(), which automatically runs expectations against retrieved data.
• Handle failures by catching the ValidationFailed exception from feast.dqm.errors and inspecting the validation_report for detailed diagnostics.

This integration leverages the GEProfiler and GEProfile classes in sdk/python/feast/dqm/profilers/ge_profiler.py to bridge Feast's feature store operations with Great Expectations' validation engine.

Frequently Asked Questions

How do I install Feast with Great Expectations support?

Install Feast with the ge extra using pip: pip install 'feast[ge]'. This installs great_expectations>=0.15.41,<1 as defined in the project's pyproject.toml, enabling the Data Quality Monitoring module that connects Feast with Great Expectations.

What is the role of the @ge_profiler decorator in Feast?

The @ge_profiler decorator, defined in sdk/python/feast/dqm/profilers/ge_profiler.py, converts a user-defined function into a GEProfiler instance. This function must accept a Great Expectations PandasDataset and return an ExpectationSuite, allowing Feast to automatically run these expectations during feature retrieval.

How do I create a validation reference in Feast?

Create a validation reference by calling as_reference() on a saved dataset object, passing a name and your profiler function: validation_reference = ds.as_reference(name="reference_2021_jun", profiler=stats_profiler). This stores the ExpectationSuite generated from the reference data for comparison against future retrievals.

What happens when Great Expectations validation fails in Feast?

When validation fails, Feast raises a ValidationFailed exception from feast.dqm.errors. This exception contains a validation_report attribute with detailed information about which expectations failed, including observed values and missing counts from Great Expectations, allowing you to programmatically handle data quality issues.

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