Distributed Databases: A Structural Constraint in the AI Era

Structural Limitations from Function-Based Database Combinations

The pattern of starting with OLTP, adding Elasticsearch when search requirements emerge, introducing an OLAP engine for analytics, and later adopting Vector DB for AI capabilities is extremely common. The problem is that over time, this structure solidifies into a complex architecture mixing different storage, indexing, and computing models—not just functional expansion. While each engine performs its role, the overall structure gradually loses consistency and management difficulty increases exponentially.

Increasing Complexity in Data Flow, Consistency, and Latency

As engines become separated, data is replicated and transformed through multiple pipelines, creating snapshots from different points in time. Eventually, data consistency relies not on the system itself but on ETL and synchronization jobs. When failures occur, tracking which point became a bottleneck or which index is stale becomes a high-cost operation. Observability decreases, latency accumulates between engines, and transparency of the entire data flow continuously degrades.

The Inevitable Database Combination

This data-separated database structure was not a wrong choice but rather a result of technical context. OLTP (row-based storage), OLAP (columnar storage), FTS (inverted index), and Vector Search (HNSW/IVF/PQ-based indexes) have been optimized for different data structures, and technology to integrate them into a single engine did not exist. Therefore, combining different databases was the only option, and this approach became the de facto standard for a long time. However, as services become more sophisticated and AI-based workloads expand, this combinatorial structure begins to reveal its structural limitations.

Continuous high burdens exist in per-engine cluster operations, index duplication, pipeline maintenance, schema duplication, and data movement costs. Failure points increase, knowledge concentrates in specific engine specialists, and new feature development necessarily involves per-engine coordination work. As workloads increase, complexity grows exponentially rather than linearly, ultimately degrading both business velocity and stability.

Cognica's Technical Turning Point

Cognica is an engine that solves these structural limitations. An architecture capable of handling OLTP, OLAP, text search, and vector search on a single storage layer with unified indexing is not a summation of existing DB functionalities but an approach that redefines the data model itself. Cognica processes diverse workloads without data replication, enabling different query patterns to execute based on the same data copy. The area that the industry had previously assumed technically impossible—engine-level integration of multiple workloads—has been implemented as an actual product for the first time. This is precisely where Cognica fundamentally differs from existing databases.

We must examine whether current data stacks can maintain long-term scalability and stability, whether pipelines can handle additional workloads, and whether the structure persists even when key personnel change. In the past, combinatorial architecture was considered natural because single-engine options did not exist. But the situation has changed. Cognica is the first engine to technically realize a previously unavailable option, providing a practical alternative that reduces complexity, ensures data consistency, and processes diverse workloads in a unified structure. There is no longer a need to accept 'combining multiple engines' as an inevitable premise. For the first time, a different path exists.