Automated Data Quality Monitoring System with Great Expectations

To develop an automated data quality validation system using Great Expectations that continuously monitors datasets across pipelines, detects anomalies, enforces schema validation, and ensures reliability of enterprise data workflows.

Study data quality dimensions and validation rules.

Install and configure Great Expectations framework.

Define expectations for dataset schemas.

Integrate validation checks into ETL pipelines.

Generate automated validation reports.

Implement alert mechanisms for failures.

Store quality metrics in monitoring dashboards.

Optimize validation for large datasets.

Implement automated reprocessing for failed jobs.

Monitor data drift patterns.

Benchmark validation performance.

Document quality assurance workflow.

Conduct test scenarios with faulty data.

Prepare final performance and reliability report.