Designing a Cloud-Native Data Lakehouse for Real-Time Business Intelligence

Abstract

The emergence of cloud-native architectures and advanced analytics has catalyzed a paradigm shift in business intelligence (BI), enabling organizations to derive insights from vast and diverse datasets in real time. Traditional data warehouses have been limited in their ability to handle high-velocity, high-volume, and high-variety data, making them increasingly inadequate for modern decision-making requirements. Data lakehouses, a hybrid between data lakes and data warehouses, have emerged as a compelling solution, combining the scalability and flexibility of data lakes with the reliability, governance, and performance of data warehouses. This paper investigates the design and implementation of a cloud-native data lakehouse architecture to facilitate real-time BI. The study emphasizes the integration of modern cloud platforms, distributed storage systems, and advanced analytics engines to enable near-instantaneous data ingestion, transformation, and visualization. The architecture incorporates principles such as serverless computing, decoupled storage and compute, and event-driven processing, which collectively enhance scalability, fault tolerance, and cost-efficiency. A central focus of the research is on addressing common challenges associated with real-time analytics, including data consistency, schema evolution, governance, and security. Through a systematic analysis of current literature, industry case studies, and practical implementations, the study identifies best practices for optimizing performance and ensuring reliability in a cloud-native lakehouse environment. Additionally, the paper explores the integration of machine learning (ML) and artificial intelligence (AI) pipelines within the lakehouse architecture to support predictive and prescriptive analytics, enabling organizations to derive actionable insights from streaming and batch data simultaneously. The results demonstrate that a well-designed cloud-native data lakehouse not only improves query performance and operational efficiency but also provides a unified platform for diverse BI workloads, including ad hoc analytics, dashboards, reporting, and advanced ML tasks. Moreover, the architecture supports multi-tenancy, dynamic scaling, and real-time data governance, which are critical for compliance, auditability, and operational resilience. The findings underscore the importance of leveraging cloud-native features such as object storage, distributed compute engines, and orchestration frameworks to achieve both performance and cost-effectiveness. In conclusion, the research highlights that cloud-native data lakehouses represent a transformative approach to BI, enabling organizations to meet the demands of modern data-driven decision-making while maintaining flexibility, scalability, and governance standards. The study provides practical recommendations for organizations seeking to adopt or optimize cloud-native lakehouse architectures, emphasizing the importance of careful design, automation, and continuous monitoring to achieve real-time insights and competitive advantage.