A Domain Driven Data Architecture to Improvement of Data Quality in Distributed Data Systems
Keywords:
Domain-Driven Design, Data Architecture, Data Quality, Distributed DatasetsAbstract
Sometimes scattered among several systems and departments, organizations struggle to manage vast amounts of data. As data increases in bulk and complexity, especially when datasets are distributed across many platforms with different formats and architectures, ensuring consistent quality is increasingly difficult. of breaking down complex data systems into smaller, controllable components each under control of their respective domain, a domain-driven data architecture offers a solution. Using domain-driven design (DDD) ideas, companies might improve data management by means of clear ownership definition, data validation and transformation techniques, and synchronizing assurance across many systems. This approach helps to manage data quality in distant locations by allowing a more ordered, coherent structure. The execution of domain-specific data validation and transformation is a fundamental element of this architecture as it guarantees that every dataset conforms with quality requirements before processing or distribution throughout systems. By combining this domain-centric approach with present technologies such as data warehouses, lakes, and governance systems, one may improve data quality management at every stage of the data life. This paper shows how domain-driven design improves data quality, hence increasing dependability, accessibility, and consistency across companies, using actual case studies from several fields.
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