Why a Single Database?

Cognica's Vision for the Future of Databases

Over the past two decades, database technology has evolved through a trend of functional specialization. As web and mobile services grew, the types and processing purposes of data diversified, leading to the emergence of databases specialized for each role. OLTP (OnLine Transaction Processing) for handling core service data, OLAP (OnLine Analytical Processing) for large-scale analytics, cache systems for fast response times, FTS (Full-Text Search) for document-based searching, and more recently, Vector search, a core component of the AI era, are prime examples. At the time, such segmentation and specialization seemed like the direction of technological advancement.

This specialization was a natural response to the needs of an era. Each function became rapidly sophisticated, and companies could solve problems by selecting and combining systems suited to their specific purposes. However, the situation is changing today. As technology becomes more sophisticated, the burden of connecting and operating separate systems is actually increasing. A structure where data is distributed across multiple repositories is no longer efficient and is acting as an obstacle that cannot keep pace with the speed of data-driven innovation.

The reason databases are moving back toward converging into a unified structure is not simply about convenience or cost reduction. To secure competitiveness in data and AI environments, data must be organically processed and utilized within a single environment. This is a technological trend already being detected within the industry, and a clear directional shift.

Limitations of the Functional Database Era

Today, many companies' data infrastructure has what is commonly called a "multi-database architecture." For example, a typical service environment consists of the following configuration:

• OLTP: Storing core transactional data for services (e.g., MySQL, PostgreSQL)
• OLAP: Large-scale analysis and aggregation processing (e.g., Snowflake, BigQuery, Redshift)
• Cache: Temporary storage to minimize response latency (e.g., Redis)
• FTS: Providing text-based search functionality (e.g., Elasticsearch, OpenSearch)
• Vector DB: Storage for semantic search and AI utilization (e.g., Pinecone, Weaviate, Milvus)

On the surface, this may appear to be an ideal structure utilizing technology optimized for each function. However, in such environments, tasks to move and process data occur constantly. The same data is duplicated across multiple repositories, each system must be synchronized with every change, and format conversions must be considered during data movement. As a result, data pipelines become complex, operations and maintenance require high expertise, and the technology itself is transforming into a factor that limits a company's growth speed.

While this structure may have been effective in the past, it is no longer suitable for the AI era. As the number of tools handling data increases, the cost and time required to overview and utilize all data increases further.

Data Integration is the Inevitable Evolution of Technology

Technology does not always evolve toward greater complexity. When it reaches a certain point, it evolves toward absorbing and integrating complexity. Just as computing environments evolved from PCs to servers and then to the cloud, becoming more abstracted and simplified, database technology has also entered the flow of integration.

When data exists separately in multiple locations, significant cost and time are invested to ensure accuracy and consistency. Conversely, when data is processed within a single engine, storage, analysis, search, and AI utilization are naturally connected. This dramatically improves data utilization speed and productivity.

The unified database model is not a strategy for reducing the technology stack, but a process to restore the fundamental value of data utilization. As technology becomes more complex, its core value must become simpler. Databases are moving back toward simplicity and consistency.

Cognica's Direction: Complete Data Utilization in a Single Engine

Cognica is a database designed based on this technological transformation trend. Cognica provides OLTP, OLAP, cache, FTS, and vector search capabilities in a single engine. It offers a structure where storage, processing, analysis, search, and AI utilization occur naturally in one place without connecting different systems.

The value of this integration is not simply in reducing the number of systems. It lies in the fundamental benefits that arise from data being created and consumed within a single structure.

• Data integrity and quality are inherently ensured.
• Analytics and service functions are connected in real-time.
• AI models operate stably and accurately based on the latest data.
• Companies can focus on creating the inherent value of products and services, rather than building and managing data infrastructure.

If the main concern in the past was "what database combination is best," the central challenge going forward will be "how to create value by utilizing data." Cognica aims to reduce the complexity of technology choices and provide an environment that focuses on the essence of data utilization.

Changes That Unified Databases Will Bring

When data is processed within a single engine, fundamental changes appear across a company's service operations and decision-making processes.

• Immediacy and agility are enhanced through the elimination of data movement and processing steps.
• The integrated structure reduces bottlenecks during scaling, enabling stable growth.
• The complexity of operations and maintenance decreases, allowing organizational capabilities to focus on core value areas.
• The speed of data experimentation and service improvement increases, accelerating the pace of innovation.

A unified database is not simply an improvement in technical structure, but a transformation that redefines a company's data capabilities and competitiveness.

Databases in the AI Era

In the future, data will be created more rapidly and expand into more diverse forms. AI is evolving from an entity that consumes data to one that directly creates and connects data. In such an environment, databases cannot respond as a combination of functional tools.

An integrated structure where a single engine can absorb and scale various data processing requirements—this is the next new agenda for database technology. Technology does not become more complex, but evolves in a direction that organizes complexity and restores simplicity.

Cognica stands at this turning point of change. When data comes together in one place, the depth of analysis changes, more sophisticated services become possible, and ultimately, superior AI can be built. Database integration is not a choice but an inevitability demanded by the times, and an essential process for the technology ecosystem to move to the next stage.