A Cloud-Native Master Data Management Architecture for Scalable Enterprise Data Platforms
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P115Keywords:
Cloud-Native Architecture, Master Data Management (MDM), Microservices, Data Governance, Event-Driven Architecture, Distributed Systems, Enterprise Data Platforms, Data QualityAbstract
In the era of digital transformation, enterprises generate and manage vast volumes of data across distributed systems, creating significant challenges in maintaining data consistency, quality, and governance. Master Data Management (MDM) is an important aspect that can help in solving these issues by developing a single and authoritative perspective of the core business entities. Nevertheless, conventional MDM systems, that are usually monolithic in design, are unable to achieve the scalability, adaptability and real time processing needs of the current enterprise world. This paper suggests a cloud-native MDM architecture that will be used to provide scalable and resilient enterprise data platforms. The architecture also uses a microservice architecture, containerization and event driven processing to facilitate real-time data integration and synchronization of heterogeneous systems. It has built up layers, among them, data ingestion, processing and standardization, core master data services, metadata governance, API-driven access, and smooth data flow and high availability are guaranteed. The high data quality processes, e.g. entity resolution, deduplication, and validation are incorporated that ensure correct and consistent master records. Experimental analysis shows that a large merit is gained in terms of throughput, the reduction of latency and data quality as compared to the traditional methods. The offered system is also capable of increasing efficiency in the work due to elastic scalability and lowering the infrastructure expenses. The study informs about the prospects of cloud-native MDM designs as drivers of agile, dependable, and high-throughput data management application, and their aptness to large-scale enterprise app usage and contemporary data ecosystems.
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