A Conceptual Lakehouse-DevOps Integration Model for Scalable Financial Analytics in Multi-Cloud Environments

The growing complexity of financial analytics in distributed, multi-cloud environments has exposed the limitations of traditional data architectures and disconnected operational workflows. This paper introduces a conceptual integration model that unites the Lakehouse paradigm with DevOps principles to address the need for scalable, agile, and compliant financial data systems. The proposed model is structured into four layers: data ingestion, Lakehouse storage, DevOps orchestration, and analytics output, each designed to ensure modularity, automation, and cross-cloud interoperability. By embedding continuous integration, pipeline automation, and observability into the analytics lifecycle, the model enhances agility in delivering real-time insights while preserving rigorous governance and auditability. Through theoretical grounding and architectural synthesis, this work identifies critical integration points where DevOps accelerates the development and reliability of Lakehouse-powered financial workflows. It also explores strategic implications such as improved scalability for high-frequency trading, embedded regulatory compliance, and operational cost efficiency. The paper concludes by outlining academic and practical applications of the model and proposing future directions for MLOps integration and empirical validation. This framework offers a forward-looking reference architecture for financial institutions seeking to modernize data platforms in an increasingly regulated and cloud-native world.