Is an All-in-One Database the Future?

As data challenges become more complex, specialized databases have emerged to address specific problems, leading to intricate data infrastructures often reliant on third-party data pipelines and event streaming products. Companies typically utilize multiple databases to meet diverse needs, resulting in a complicated network of interconnected systems. The pursuit of a generalist, all-in-one database that is scalable, efficient across various contexts, and commercially viable remains unfulfilled. Different database types, including relational OLTP, non-relational document stores, and memory-based caches, are optimized for particular use cases, each facing unique challenges that hinder effective consolidation. While Object-Relational Mappers (ORMs) aim to simplify database interactions, they struggle with non-relational types, highlighting the difficulty of creating a unified solution. Postgres is noted for its extensibility through plugins, making it a close contender for a comprehensive solution, yet it still does not fully meet the criteria of a one-stop-shop. The technical and practical challenges associated with developing an all-in-one database render it impractical, maintaining the modern data stack as a complex necessity. Theoretically, a universal database would face issues with optimization, data model overhead, and latency, as each type is designed for specific applications, making a singular solution inefficient. Although Postgres can be enhanced with plugins like pg_vector for vector search, it is not designed to address every problem effectively, as evidenced by the complex data stacks in organizations utilizing it.