An Integrated Hybrid Lakehouse Framework for Data Governance, Data Lineage, and Enterprise Analytics

Abstract

Present-day companies gather data from various databases, cloud computing, applications, and analytics. Nonetheless, the problem arises when a company has fragmented data which leads to problems such as data governance, data lineage, data quality, accessibility, and analytical trust. This research offers a conceptual model of a lakehouse solution with integrated data governance, data lineage, and analytics for the enterprise. The proposed methodology is qualitative, inductive, and based on secondary data. The secondary sources consist of articles from peer-reviewed journals, conference papers, technical reports, industry reports, and organizational reports. The literature review consists of concepts associated with Lakehouse Architecture, Data Governance, Metadata Management, Data Lineage, Data Quality, Cloud Computing, Business Intelligence, Real-Time Analytics, Artificial Intelligence, and Machine Learning. The findings prove that lakehouse architecture supports scalable storage, flexible processing, open format interoperability, and various analytical workloads. Still, there are several technical and operational challenges. These gaps have been addressed in the suggested framework by means of centralized metadata, automated governance, lineage from end to end, standardized data, quality assurance, and constant monitoring. The combination of these features may contribute to higher transparency, reliability, availability, and consistency of data analysis. Moreover, business intelligence, real-time analytics, AI, and machine learning can be enabled by the usage of data which has been governed and traceable. Thus, the research shows how the combination of governance and lineage with the help of lakehouse design pattern can improve enterprise analytics. This solution eliminates unnecessary duplications in data platforms while improving the visibility throughout the whole data lifecycle.