2025/11/15 by Valiaiev, Dmytro
#Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Electrical engineering #Software Engineering (cs.SE) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2511.12277
The proliferation of SQL for data processing has often occurred without the rigor of traditional software development, leading to siloed efforts, logic replication, and increased risk. This ad-hoc approach hampers data governance and makes validation nearly impossible. Organizations are adopting DataOps, a methodology combining Agile, Lean, and DevOps principles to address these challenges to treat analytics pipelines as production systems. However, a standardized framework for implementing DataOps is lacking. This perspective proposes a qualitative design for a DataOps-aligned validation framework. It introduces a DataOps Controls Scorecard, derived from a multivocal literature review, which distills key concepts into twelve testable controls. These controls are then mapped to a modular, extensible CI/CD pipeline framework designed to govern a single source of truth (SOT) SQL repository. The framework consists of five stages: Lint, Optimize, Parse, Validate, and Observe, each containing specific, automated checks. A Requirements Traceability Matrix (RTM) demonstrates how each high-level control is enforced by concrete pipeline checks, ensuring qualitative completeness. This approach provides a structured mechanism for enhancing data quality, governance, and collaboration, allowing teams to scale analytics development with transparency and control.