Research

The requirement for specialization in data management systems has evolved faster than our software development practices. After decades of organic growth, this situation has created a siloed landscape composed of hundreds of products developed and maintained as monoliths, with limited reuse between systems. This fragmentation has resulted in developers often reinventing the wheel, increased maintenance costs, and slowed down innovation. It has also affected the end users, who are often required to learn the idiosyncrasies of dozens of incompatible SQL and non-SQL API dialects, and settle for systems with incomplete functionality and inconsistent semantics. In this vision paper, considering the recent popularity of open source projects aimed at standardizing different aspects of the data stack, we advocate for a paradigm shift in how data management systems are designed. We believe that by decomposing these into a modular stack of reusable components, development can be streamlined while creating a more consistent experience for users. Towards that goal, we describe the state-of-the-art, principal open source technologies, and highlight open questions and areas where additional research is needed. We hope this work will foster collaboration, motivate further research, and promote a more composable future for data management.