The Future of Cloud-Native Lakehouses: Leveraging Serverless and Multi-Cloud Strategies for Data Flexibility

The evolution of data architecture paradigms has reached a critical inflection point with the emergence of cloud-native lakehouses that combine the flexibility of data lakes with the performance and reliability of data warehouses. This paper explores the transformative potential of serverless computing and multi-cloud strategies in advancing lakehouse architectures beyond traditional single-cloud constraints. As organizations grapple with exponentially growing data volumes distributed across heterogeneous cloud environments, the need for cloud-agnostic, serverless-enabled lakehouse solutions becomes paramount. This research presents a comprehensive framework for designing and implementing cloud-native lakehouses that leverage serverless computing paradigms including AWS Lambda, Azure Functions, and Google Cloud Functions to eliminate infrastructure management overhead while ensuring optimal resource utilization and cost efficiency. The proposed architecture incorporates multi-cloud data platforms such as Databricks and Snowflake to enable seamless data processing across cloud boundaries, while utilizing Infrastructure as Code (IaC) tools like Terraform for automated, consistent infrastructure provisioning and management. The paper introduces a novel methodology that abstracts cloud provider dependencies through containerization and orchestration technologies, enabling organizations to achieve true cloud portability and vendor independence. Through comprehensive analysis of performance metrics, cost optimization strategies, and operational complexity reduction, this research demonstrates that serverless multi-cloud lakehouse architectures can deliver superior agility, scalability, and resilience compared to traditional monolithic data platforms. Empirical evaluation across diverse workload patterns reveals significant improvements in resource utilization efficiency and total cost of ownership while maintaining stringent data governance and compliance requirements.