Enhancing Data Lakehouse Architecture with DevOps and MLops Practices

Abstract

Data Lakehouse architectures represent a pivotal shift in data management, ushering in a new era of data strategy. By combining data lakes' extensive data storage capabilities with the efficiency in processing and querying data found in data warehouses, Data Lakehouse architectures are at the forefront of data management innovation. This paper delves into how DevOps and MLops procedures can be implemented within a Data Lakehouse framework to enhance automation, scalability, and maintainability. DevOps enables continuous integration and deployment, thereby enhancing the cycles of operation and responsiveness of systems. MLOps automates the entire lifecycle of machine learning models, ensuring they are scalable and well-integrated into production. The convergence of these methodologies within the Data Lakehouse significantly enhances data strategy with better operational efficiency,
acting as a strong enabler for real-time, data-driven decision-making. Architectural advancements comprise the additional roles of DevOps, MLOps, and how they have influenced and affected the agility and accuracy of business insight-one gets to learn from this study.