great-expectations

Great Expectations

Audience: Data engineers building validated data pipelines.

Goal: Provide GX patterns for expectation-based validation and monitoring.

Scripts

Execute GX functions from scripts/expectations.py:

from scripts.expectations import (
    get_pandas_context,
    add_dataframe_asset,
    create_basic_suite,
    run_validation
)

Usage Examples

Quick Setup

from scripts.expectations import get_pandas_context, add_dataframe_asset

context, datasource = get_pandas_context("my_datasource")
batch_request = add_dataframe_asset(datasource, "users", df)

Create Expectation Suite

from scripts.expectations import create_basic_suite

columns_config = {
    'user_id': {'not_null': True, 'unique': True, 'type': 'int'},
    'age': {'min': 0, 'max': 150},
    'status': {'values': ['active', 'inactive', 'pending']},
    'email': {'regex': r'^[\w\.-]+@[\w\.-]+\.\w+$'}
}

suite = create_basic_suite(context, "user_suite", columns_config)

Run Validation

from scripts.expectations import run_validation

results = run_validation(
    context,
    checkpoint_name="user_checkpoint",
    batch_request=batch_request,
    suite_name="user_suite"
)

if results['success']:
    print("All expectations passed!")
else:
    for failure in results['failures']:
        print(f"Failed: {failure['expectation']} on {failure['column']}")

Common Expectations Reference

Category	Expectation	Description
Table	ExpectTableRowCountToBeBetween	Row count range
Existence	ExpectColumnToExist	Column must exist
Nulls	ExpectColumnValuesToNotBeNull	No null values
Range	ExpectColumnValuesToBeBetween	Value bounds
Set	ExpectColumnValuesToBeInSet	Allowed values
Pattern	ExpectColumnValuesToMatchRegex	Regex match
Unique	ExpectColumnValuesToBeUnique	No duplicates

Data Docs

# Build and open HTML reports
context.build_data_docs()
context.open_data_docs()

Directory Structure

great_expectations/
├── great_expectations.yml     # Config
├── expectations/              # Expectation suites (JSON)
├── checkpoints/               # Checkpoint definitions
├── plugins/                   # Custom expectations
└── uncommitted/
    ├── data_docs/            # Generated HTML docs
    └── validations/          # Validation results

When to Use Great Expectations

Use Case	GX	Alternative
Pipeline monitoring	✓	-
Data warehouse validation	✓	-
Automated data docs	✓	-
Simple DataFrame checks	-	Pandera
Record-level API validation	-	Pydantic

Dependencies

great_expectations>=0.18
pandas