Paper Title
MODERN CLOUD-NATIVE LAKEHOUSE ARCHITECTURES: MULTI-CLOUD APPROACHES FOR ADVANCED ANALYTICS AND BUSINESS INTELLIGENCE
Keywords
- cloud computing
- multi-cloud
- data analytics
- lakehouse architecture
- serverless
- infrastructure as code
- business intelligence
Publication Info
Volume: 16 | Issue: 1 | Pages: 4157-4174
Published On
February, 2025
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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.
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