MODERN CLOUD-NATIVE LAKEHOUSE ARCHITECTURES: MULTI-CLOUD APPROACHES FOR ADVANCED ANALYTICS AND BUSINESS INTELLIGENCE

Abstract

The transformation of data management has accelerated the adoption of cloud-native lakehouse architectures that leverage serverless computing and multi-cloud strategies. Traditional data lakes and warehouses face challenges in scalability, cost, and agility, which modern lakehouses address by integrating decentralized, cloud-agnostic, and serverless approaches. This article examines the complexities and advantages of architecting cloud-native lakehouses that prioritize interoperability and flexibility across multiple cloud platforms. Serverless technologies enable dynamic resource allocation, reducing operational overhead and costs, while multi-cloud strategies improve resilience and vendor neutrality by distributing workloads across providers. This work introduces a framework utilizing containerization, orchestration, and infrastructure-as-code (IaC) with Terraform to streamline cloud transitions. Through practical implementation and evaluation, we demonstrate the effectiveness of a serverless, multi-cloud lakehouse in managing diverse analytics workloads, ensuring compliance, and optimizing performance. Results show that adopting these strategies significantly enhances cost efficiency, scalability, and governance, positioning cloud-native lakehouses as a leading solution for data-driven enterprises.