Sarcouncil Journal of Engineering and Computer Sciences

Sarcouncil Journal of Engineering and Computer Sciences

An Open access peer reviewed international Journal
Publication Frequency- Monthly
Publisher Name-SARC Publisher

ISSN Online- 2945-3585
Country of origin-PHILIPPINES
Impact Factor- 3.7
Language- English

Keywords

Editors

Metadata-Driven Architecture: A Paradigm Shift in Data Engineering Pipeline Design

Keywords: Metadata-driven architecture, declarative pipelines, data engineering, separation of concerns, modular transformation.

Abstract: This article examines the development of data engineering practices through metadata-driven, modular, and manifesto pipeline architecture. Traditional approaches for the development of the pipeline often result in complex, hard-to-routine systems that struggle on a scale with organizational needs. By taking advantage of metadata definitions as the foundation of pipeline construction, organizations can achieve significant improvements in flexibility, maintenance, and operational efficiency. The separation between logical definition and physical execution creates a paradigm change that fundamentally changes how the concept and implementation of data workflows are made. Definition, orchards enabled more abstraction, improvement, and enhanced governance capabilities, containing extended architectural, corrected, and executed components. This architectural pattern represents a departure from traditional procedural implementation, emphasizing the manifesto specifications that describe the desired results rather than the implementation details. Whatever is needed to complete, clear delimitation between them and how it is executed, promotes standardization, facilitates cross-team cooperation, and increases system adaptability to develop business requirements. Despite the implementation challenges related to initial investment, developer adaptation, and performance ideas, the metadata-driven approach provides an adequate advantage in technical, operational, and governance dimensions. This architectural pattern represents a fundamental advancement in data engineering methods, enabling organizations to improve data complexity by reducing technical debt.

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