Data Quality as Metadata — Checks, Profiles, and Contracts Derived From Structure

2026-02-16 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Traditional data quality implementations, often relying on ad-hoc SQL queries, are inherently fragile due to their decentralized nature, lack of visibility, and difficulty in propagation and migration. The Metadata-Driven Data Engineering (MDDE) approach addresses these issues by treating data quality rules as integral metadata rather than isolated SQL scripts. This method emphasizes that data quality originates from the underlying data structure, where metadata defines critical elements such as primary keys, mandatory attributes, relationships, data types, and stereotypes. By embedding quality logic directly within the metadata, MDDE aims to create a more robust, visible, and manageable system for ensuring data integrity across various projects and migrations.

Key takeaway

For Data Engineers struggling with fragmented data quality checks, adopting a Metadata-Driven Data Engineering (MDDE) approach is crucial. By defining quality rules as metadata, you can centralize logic, improve visibility, and ensure consistent data integrity across projects and migrations. This shift reduces fragility and simplifies maintenance compared to scattered SQL scripts.

Key insights

Treating data quality rules as metadata enhances visibility, propagation, and robustness over ad-hoc SQL queries.

Principles

• Quality logic should be metadata, not ad-hoc SQL.
• Data quality originates from defined data structure.

In practice

• Define primary keys in metadata.
• Specify mandatory attributes in metadata.

Topics

• Data Quality Management
• Metadata Management
• Data Governance
• Data Structure Definition

Best for: AI Architect, CTO, VP of Engineering/Data, Data Engineer, Data Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.