A mathematical approach for Data Management Transformation: Bridging Data Lakes and Data Fabrics with Advanced Analytics

Abstract

Over the last decade, big data and advanced analytics have changed a lot, and as a result, data management has been transformed from the legacy data lake to the modern data fabric architecture. The transformation is founded on the increasing demand for seamless, real-time integration and access between disparate data sources in order to turn actionable insights more quickly. Data lakes are good at centralizing high amounts of structured and unstructured data from different sources but lack governance, scalability, and timely analytics. In contrast, data fabrics provide an integrated intelligent layer that connects diverse data environments, ensuring superior agility, better data quality, and more optimized performance. Data fabrics utilize advanced analytics and machine learning to dynamically evaluate data flows and change them accordingly, automate integration processes and help organisations get just-in-time and just-enough data driven decisions quicker. In this paper well address how organizations are embracing data fabric solutions to mitigate the weaknesses of data lakes, and deliver results like faster insights, better compliance, and more accurate predictive analytics. The value of this change is demonstrated with relevant use cases in the real-world reinforcing that it will act as a catalyst for innovation and boost operational effectiveness. These findings highlight the significance of data fabrics as the future solution for enterprises aiming for a higher competitive advantage in an emerging data-driven world.