As data challenges become more defined and difficult, purpose-built databases have emerged to optimize specific problems, leading to a proliferation of specialized solutions. This has resulted in complex and messy data infrastructures, patched together by third-party data pipelines and event streaming products.
Companies rarely rely on a single primary data storage system, often deploying multiple databases to handle different needs, which creates an intricate web of interconnected data systems.
But, the idea of a generalist, all-in-one database that is scalable, performant across contexts, and commercially appealing remains elusive.
Different database types, such as relational OLTP, non-relational document, and memory-based cache, are optimized for specific use cases and face unique challenges that prevent effective consolidation. While tools like Object-Relational Mappers (ORMs) attempt to simplify interactions across databases, they fall short in managing non-relational types, underscoring the complexity of creating a unified solution.
Postgres, with its extensibility through plugins, comes close but still falls short of being a one-stop-shop. Ultimately, the technical and practical hurdles make an all-in-one database unfeasible, leaving the modern data stack as a complex but necessary reality.
In theory, an all-in-one database would struggle with optimization, data model overhead, and latency. Each database type is built for specific use cases, making a universal solution inefficient.
Closest Solution Today: Postgres
While not a full one-stop-shop, Postgres can be extended with plugins like pg_vector for vector search. However, it's not intended to solve every problem efficiently, evidenced by complex data stacks in companies using Postgres.
