| name | Great Expectations Generator |
| description | Generates Great Expectations suites from data profiles and business rules |
| version | 1.0.0 |
| category | Data Quality |
| skillId | SK-DEA-006 |
| allowed-tools | ["Read","Write","Edit","Glob","Grep","Bash"] |
| graph | {"domains":["domain:data-engineering"],"specializations":["specialization:data-engineering-analytics"],"skillAreas":["skill-area:data-quality","skill-area:data-validation-sanitization"],"roles":["role:data-engineer","role:analytics-engineer"],"workflows":["workflow:data-quality-monitoring"]} |
Great Expectations Generator
Overview
Generates Great Expectations suites from data profiles and business rules. This skill automates the creation of comprehensive expectation suites that enforce data quality constraints.
Capabilities
- Expectation suite generation from profiling
- Custom expectation creation
- Checkpoint configuration
- Data docs generation
- Validation result analysis
- Expectation parameterization
- Suite versioning recommendations
- Integration with dbt and Airflow
Input Schema
{
"dataProfile": "object",
"businessRules": ["object"],
"existingSuite": "object",
"strictness": "strict|moderate|lenient"
}
Output Schema
{
"expectationSuite": "object",
"checkpointConfig": "object",
"documentation": "string",
"coverageReport": {
"columnsWithExpectations": "number",
"totalExpectations": "number"
}
}
Target Processes
- Data Quality Framework
- ETL/ELT Pipeline
- dbt Project Setup
Usage Guidelines
- Provide data profile results from profiling analysis
- Define business rules that should be enforced
- Specify strictness level based on use case requirements
- Include existing suite if extending an existing configuration
Best Practices
- Start with moderate strictness and adjust based on validation results
- Include both column-level and table-level expectations
- Document business rationale for each custom expectation
- Version expectation suites alongside data transformations
- Configure appropriate data docs for stakeholder visibility