DataOps: Bridging the Gap Between Legacy and Modern Systems for Seamless Data Orchestration

Abstract

The modern enterprise operates in a complex data landscape, where legacy systems coexist with modern, event-driven microservices architectures. This heterogeneity poses significant challenges in data integration, management, and analysis. DataOps, a methodology that applies DevOps principles to the data lifecycle, offers a solution to these challenges. This paper explores the implementation of DataOps in such a hybrid environment, focusing on strategies for integrating and orchestrating data from diverse sources, ensuring data quality, and enabling efficient data-driven decision-making. The paper also highlights the crucial role of Data Lakes and Data Lake houses in facilitating seamless data orchestration, providing a scalable and flexible foundation for storing, processing, and analyzing data from both legacy and modern systems.