From DIKW to Distributed Data Warebase: Letting Data Emerge as Intelligence

The presentation begins with the DIKW (Data‑Information‑Knowledge‑Wisdom) model, illustrating how raw data (bits) gains meaning through context, becomes information via data models, transforms into knowledge through abstraction and embedding vectors, and finally reaches wisdom through complex reasoning.

It then details the role of data models: relational tables provide structured information, while document models handle semi‑structured data, and embedding vectors represent knowledge in high‑dimensional space.

Using a homestay platform as a case study, the article shows how multiple data products—relational databases (PostgreSQL), NoSQL stores (MongoDB), search engines (Elasticsearch), and data warehouses (Snowflake, Hive)—are combined to support storage, retrieval, and analytics, but this multi‑system approach introduces development, operations, and business challenges such as high complexity, synchronization overhead, and data latency.

To address these drawbacks, a new architecture called the distributed Data Warebase is proposed. It merges the capabilities of traditional databases and data warehouses, storing structured, semi‑structured, and vectorized knowledge within a single system, leveraging distributed transactions, sharding, inverted and vector indexes, columnar storage, vectorized execution, and materialized views for performance.

The distributed Data Warebase aims to simplify data architectures, reduce engineering effort, and enable seamless extraction of information and knowledge, ultimately fulfilling the emerging mission of data systems: letting data emerge as intelligence.

Finally, the article discusses how this unified data foundation supports AI agents through in‑context learning, vector search, retrieval‑augmented generation, and model fine‑tuning, illustrating a path toward intelligent, data‑driven applications.