Scalable Lakehouse Architectures Using Bronze-Silver-Gold Modeling for Enterprise Analytics
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Abstract
This paper presents a comprehensive study on the design and implementation of scalable lakehouse architectures using the Bronze–Silver–Gold data modeling paradigm to support enterprise-grade analytics. As organizations increasingly adopt unified data platforms that combine the strengths of data lakes and data warehouses, the lakehouse model has emerged as a transformative solution for managing diverse, large-scale datasets while enabling high-performance analytics. The research examines how the multi-layered Bronze–Silver–Gold approach structures raw ingestion, refined transformations, and business-ready curated data to improve data quality, governance, reliability, and analytical efficiency. Through architectural analysis, workload evaluation, and real-world implementation insights, the study demonstrates how lakehouse environments built on modern frameworks such as Delta Lake, Apache Iceberg, and Apache Hudi ensure schema enforcement, transactional consistency, scalable metadata management, and improved query performance. The findings highlight the effectiveness of this modeling pattern in accelerating AI/ML workflows, enabling streaming and batch unification, supporting diverse analytical workloads, and fostering end-to-end data lifecycle management. Recommendations and best practices are provided to guide enterprises in building resilient, future-ready lakehouse systems
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Inmon, W. H. (2005). Building the data warehouse (4th ed.). Wiley.
Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling (3rd ed.). Wiley.
Fang, H., & Zhang, J. (2016). Big data in finance: Data lakes, analytics, and governance. Journal of Financial Data Science, 1(1), 45–56.
Hai, R., Geisler, S., & Quix, C. (2016). Constance: An intelligent data lake system. Proceedings of the 2016 International Conference on Management of Data, 2097–2100.
Madera, C., & Laurent, A. (2016). The next information architecture evolution: The data lake wave. Proceedings of the 2016 Eighth International Conference on Information, Process, and Knowledge Management, 38–44.
Dixon, J. (2010). Pentaho, Hadoop, and data lakes. Pentaho Blog. Retrieved from https://www.pentaho.com (original source introducing the term data lake).
Giebler, C., Gröger, C., Hoos, E., Schwarz, H., & Mitschang, B. (2019). Model-driven data lake management. Proceedings of the 2019 IEEE International Conference on Big Data, 3012–3021.
Armstrong, D., & Delaney, P. (2017). Data governance challenges in large-scale analytics platforms. International Journal of Information Management, 37(6), 673–682.
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., … Zaharia, M. (2016). MLlib: Machine learning in Apache Spark. Journal of Machine Learning Research, 17(34), 1–7.
Zaharia, M., Das, T., Li, H., Hunter, T., Shenker, S., & Stoica, I. (2013). Discretized streams: Fault-tolerant streaming computation at scale. Proceedings of the 2013 ACM Symposium on Operating Systems Principles, 423–438.
Stein, B., & Morrison, A. (2014). The enterprise data lake: Better integration and deeper analytics. PricewaterhouseCoopers Technology Report, 1–12.
Sawadogo, P. N., & Darmont, J. (2019). On data lake architectures and metadata management. International Conference on Big Data Analytics and Knowledge Discovery, 227–241.
Marz, N., & Warren, J. (2015). Big data: Principles and best practices of scalable realtime data systems. Manning Publications.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47, 98–115.