CLOUD-NATIVE LAKEHOUSES: MULTI-CLOUD STRATEGIES FOR BUSINESS INTELLIGENCE AND DATA ANALYTICS

Abstract

The rapid evolution of data management architectures has necessitated a paradigm shift towards cloud-native lakehouses that integrate serverless computing and multi-cloud strategies. Traditional data lakes and warehouses have limitations in scalability, cost, and agility, which cloud-native lakehouses aim to overcome by embracing decentralized, serverless, and provider-agnostic solutions. This paper explores the challenges and opportunities in designing cloud-native lakehouses that maximize interoperability and flexibility across cloud environments. Serverless computing offers a compelling solution by enabling organizations to dynamically allocate compute resources based on demand, reducing costs and operational complexity. Multi-cloud strategies enhance resilience and performance by distributing workloads across different cloud providers, ensuring vendor neutrality and mitigating service disruptions. Our methodology introduces a framework leveraging containerization, orchestration, and Terraform-based infrastructure as code (IaC) for seamless cloud transitions. Through technical implementation and experimentation, we demonstrate the efficiency of a serverless multi-cloud lakehouse architecture in handling diverse workloads, ensuring compliance, and optimizing performance. Our findings indicate that adopting serverless and multi-cloud strategies significantly enhances cost efficiency, scalability, and governance in cloud-native lakehouses, making them an ideal choice for modern enterprises.