Enterprise Data Mesh Architectures for Scalable and Distributed Analytics
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P103Keywords:
Enterprise Data Mesh, Distributed Analytics, Domain-Oriented Data Products, Self-Serve Data Platform, Event-Driven IngestionAbstract
Businesses are facing the volcanic increase in data volume, variety and dispersal in hybrid and multi-cloud setup, which is revealing the constraints of the centralized data warehouse, as well as the monolithic data lakes. Data mesh has become a socio-technical framework that devolves data ownership to domain teams with shared standards on interoperability, governance and security. The presented paper offers a reference enterprise data mesh architecture to meet scalable and distributed analytics. The architecture is designed to follow four pillars, namely domain-oriented data products, a self-service data and machine learning platform, standardized interoperability layer, and a federated computational governance model. Are outlining an implementation framework which is event-driven ingestion, API-first data product exposure, unified metadata management, distributed query capabilities and cross-domain orchestration. Based on the recent empirical findings and synthesized benchmarks, Present the discussion on the gains in the time-to-insight, data quality, reusability, and governance effectiveness and on the measures of scalability in the latency and the number of concurrent users. Other challenges such as organizational resistance, tooling fragmentation, and standards gaps are also analyzed in the paper and then the paper outlines future directions in AI-augmented governance and formal, data product specifications. The proposed model is an effective roadmap that enables enterprises to operationalize data mesh and achieve real-time, reliable analytics at scale by aligning organizational design and a strong layered architecture
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