{"ID":2841374,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12277","arxiv_id":"2511.12277","title":"DataOps-driven CI/CD for analytics repositories","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.12277","url_pdf":"https://arxiv.org/pdf/2511.12277v1","authors":"[\"Dmytro Valiaiev\"]","published":"2025-11-15T16:09:47Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.DL\",\"cs.SE\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}