Authors : Krishna Prisad Bajgai; Dr. Bhoj Raj Ghimire

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/2frp7nuj

Scribd : https://tinyurl.com/5n8y3du9

DOI : https://doi.org/10.5281/zenodo.14921215

Abstract : Organizations face challenges in managing diverse, large-scale datasets while ensuring scalability, efficiency, and quality. Traditional data lakes and warehouses often fall short in modern big data environments. The Lakehouse architecture unit both, but cloud implementation faces issues like optimized ingestion, efficient storage, and integration of multiple data engines. Sectors like healthcare and agriculture struggle with real-time data and IoT, leading to inefficiencies. Current research highlights gaps in performance, scalability, and the integration of advanced analytics. Future work should focus on improving large dataset handling, real-time processing, and machine learning integration for better decision-making and performance.

Keywords : Lakehouse Architecture, Big Data Integration, Cloud Computing, Real-Time Data Processing, Federated Governance, Machine Learning.

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