--- name: great-expectations-validator-10f8a5b1c31e description: Data quality validation skill using Great Expectations for schema validation, expectation suites, data documentation, and automated data quality checks in ML pipelines. allowed-tools: Read, Grep, Write, Bash, Edit, Glob graph: domains: [domain:data-science] specializations: [specialization:data-science-ml] skillAreas: [skill-area:data-quality, skill-area:data-pipeline-testing] roles: [role:data-scientist, role:ml-ops-engineer] workflows: [workflow:data-quality-monitoring] --- # Great Expectations Validator Validate data quality using Great Expectations for comprehensive data testing, documentation, and quality monitoring. ## Overview This skill provides capabilities for data quality validation using Great Expectations (GX), the leading open-source library for data quality. It enables creation and execution of expectation suites, data documentation generation, and integration with ML pipelines. ## Capabilities ### Expectation Suite Management - Create and configure expectation suites - Define expectations for columns and tables - Validate data against expectations - Store and version expectation suites ### Data Validation - Schema validation (column presence, types) - Statistical validation (distributions, ranges) - Referential integrity checks - Custom SQL-based expectations - Regex pattern matching ### Data Documentation - Generate data documentation (Data Docs) - Create profiling reports - Document validation results - Build data dictionaries ### Pipeline Integration - Checkpoint configuration and execution - Batch request management - Action-based workflows (notifications, storage) - Integration with Airflow, Prefect, Dagster ### Custom Expectations - Define domain-specific expectations - Parameterized expectations - Multi-column expectations - Row-condition based expectations ## Prerequisites ### Installation ```bash pip install great_expectations>=0.18.0 ``` ### Optional Connectors ```bash # Database connectors pip install great_expectations[sqlalchemy] # Cloud storage pip install great_expectations[s3] # AWS pip install great_expectations[gcs] # GCP pip install great_expectations[azure] # Azure # Spark support pip install great_expectations[spark] ``` ## Usage Patterns ### Initialize Great Expectations Project ```bash # Initialize GX project great_expectations init # Creates: # great_expectations/ # ├── great_expectations.yml # ├── expectations/ # ├── checkpoints/ # ├── plugins/ # └── uncommitted/ ``` ### Create Expectation Suite from Profiler ```python import great_expectations as gx # Initialize context context = gx.get_context() # Add datasource datasource = context.sources.add_pandas("my_datasource") data_asset = datasource.add_csv_asset("customers", filepath_or_buffer="customers.csv") # Create batch request batch_request = data_asset.build_batch_request() # Create expectation suite with profiler expectation_suite = context.add_or_update_expectation_suite("customer_suite") validator = context.get_validator( batch_request=batch_request, expectation_suite_name="customer_suite" ) # Profile and generate expectations validator.expect_column_to_exist("customer_id") validator.expect_column_values_to_be_unique("customer_id") validator.expect_column_values_to_not_be_null("customer_id") validator.expect_column_values_to_be_between("age", min_value=0, max_value=120) validator.expect_column_values_to_be_in_set("status", ["active", "inactive", "pending"]) validator.expect_column_values_to_match_regex("email", r"^[\w\.-]+@[\w\.-]+\.\w+$") # Save suite validator.save_expectation_suite(discard_failed_expectations=False) ``` ### Validate Data with Checkpoint ```python import great_expectations as gx context = gx.get_context() # Create checkpoint checkpoint = context.add_or_update_checkpoint( name="customer_checkpoint", validations=[ { "batch_request": { "datasource_name": "my_datasource", "data_asset_name": "customers" }, "expectation_suite_name": "customer_suite" } ], action_list=[ { "name": "store_validation_result", "action": {"class_name": "StoreValidationResultAction"} }, { "name": "update_data_docs", "action": {"class_name": "UpdateDataDocsAction"} } ] ) # Run checkpoint result = checkpoint.run() # Check results if result.success: print("Validation passed!") else: print("Validation failed!") for validation_result in result.run_results.values(): for result in validation_result.results: if not result.success: print(f"Failed: {result.expectation_config.expectation_type}") ``` ### Common Expectations ```python # Column existence and types validator.expect_column_to_exist("column_name") validator.expect_column_values_to_be_of_type("column_name", "int64") validator.expect_table_column_count_to_equal(10) # Null handling validator.expect_column_values_to_not_be_null("column_name") validator.expect_column_values_to_be_null("deprecated_column") # Uniqueness validator.expect_column_values_to_be_unique("id_column") validator.expect_compound_columns_to_be_unique(["col1", "col2"]) # Value ranges validator.expect_column_values_to_be_between("age", min_value=0, max_value=120) validator.expect_column_min_to_be_between("score", min_value=0) validator.expect_column_max_to_be_between("score", max_value=100) # Set membership validator.expect_column_values_to_be_in_set("status", ["A", "B", "C"]) validator.expect_column_distinct_values_to_be_in_set("category", ["cat1", "cat2"]) # String patterns validator.expect_column_values_to_match_regex("email", r"^[\w\.-]+@[\w\.-]+\.\w+$") validator.expect_column_value_lengths_to_be_between("code", min_value=5, max_value=10) # Statistical validator.expect_column_mean_to_be_between("value", min_value=50, max_value=100) validator.expect_column_stdev_to_be_between("value", min_value=0, max_value=20) validator.expect_column_proportion_of_unique_values_to_be_between("id", min_value=0.9) ``` ## Integration with Babysitter SDK ### Task Definition Example ```javascript const dataValidationTask = defineTask({ name: 'great-expectations-validation', description: 'Validate data quality using Great Expectations', inputs: { dataPath: { type: 'string', required: true }, expectationSuiteName: { type: 'string', required: true }, checkpointName: { type: 'string' }, failOnError: { type: 'boolean', default: true } }, outputs: { success: { type: 'boolean' }, validationResults: { type: 'object' }, failedExpectations: { type: 'array' }, dataDocsUrl: { type: 'string' } }, async run(inputs, taskCtx) { return { kind: 'skill', title: `Validate data: ${inputs.expectationSuiteName}`, skill: { name: 'great-expectations-validator', context: { operation: 'validate', dataPath: inputs.dataPath, expectationSuiteName: inputs.expectationSuiteName, checkpointName: inputs.checkpointName, failOnError: inputs.failOnError } }, io: { inputJsonPath: `tasks/${taskCtx.effectId}/input.json`, outputJsonPath: `tasks/${taskCtx.effectId}/result.json` } }; } }); ``` ## MCP Server Integration ### Using gx-mcp-server ```json { "mcpServers": { "great-expectations": { "command": "uvx", "args": ["gx-mcp-server"], "env": { "GX_CONTEXT_ROOT": "./great_expectations" } } } } ``` ### Available MCP Tools - `gx_list_datasources` - List configured datasources - `gx_list_expectation_suites` - List expectation suites - `gx_run_checkpoint` - Execute a checkpoint - `gx_validate_data` - Validate data against suite - `gx_get_validation_results` - Retrieve validation results ## ML Pipeline Integration ### Training Data Validation ```python def validate_training_data(df, suite_name="training_data_suite"): """Validate training data before model training.""" context = gx.get_context() # Add dataframe as datasource datasource = context.sources.add_pandas("training_data") data_asset = datasource.add_dataframe_asset("df") batch_request = data_asset.build_batch_request(dataframe=df) # Validate checkpoint = context.add_or_update_checkpoint( name="training_validation", validations=[{ "batch_request": batch_request, "expectation_suite_name": suite_name }] ) result = checkpoint.run() if not result.success: failed = [r for r in result.run_results.values() for r in r.results if not r.success] raise ValueError(f"Training data validation failed: {len(failed)} expectations failed") return True ``` ### Feature Quality Checks ```python # Expectations for ML features validator.expect_column_values_to_not_be_null("feature_1", mostly=0.95) validator.expect_column_values_to_be_between("feature_1", min_value=-3, max_value=3) # Standard scaled validator.expect_column_proportion_of_unique_values_to_be_between("categorical_feature", min_value=0.001) validator.expect_column_kl_divergence_to_be_less_than("feature_1", partition_object=reference_distribution, threshold=0.1) ``` ## Best Practices 1. **Version Expectation Suites**: Store suites in version control 2. **Use Checkpoints**: Always validate through checkpoints for consistency 3. **Set Mostly Parameter**: Allow for small data quality issues with `mostly=0.95` 4. **Generate Data Docs**: Document your data for team visibility 5. **Fail Fast**: Validate data early in pipelines 6. **Custom Expectations**: Create domain-specific expectations for your use case ## References - [Great Expectations Documentation](https://docs.greatexpectations.io/) - [GX MCP Server](https://github.com/davidf9999/gx-mcp-server) - [Expectation Gallery](https://greatexpectations.io/expectations/) - [GX Cloud](https://greatexpectations.io/cloud/) --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Development & Engineering, Operations, Product Management Roles: Business Process Analyst, Data Analyst, Data Scientist, ML / AI Engineer, Product Manager Specialties: API, Backend Tools: Great_Expectations Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 7f2d83c32188be2a14f0ae186331f2b82481c4bc83d38a41ce524cb2931a48e0