From Zero to One: Building a Next‑Gen Distributed Data Architecture for the AI Era

01 Digital World DIKW Model

The DIKW hierarchy (Data → Information → Knowledge → Wisdom) introduced by Russell Ackoff in 1989 is used as a lens to explain how raw numbers become actionable insight, and how this chain underpins modern digital systems.

02 Evolution of Enterprise Data Systems

Using a homestay platform as a running example, the article shows the progression from a single‑machine relational database (MySQL/PostgreSQL) during the MVP stage to sharding, NoSQL (MongoDB), and finally Elasticsearch for keyword search as traffic spikes during holidays expose the limits of a monolithic setup.

Horizontal scaling by adding servers.

Distributed architectures and optimized storage for large‑scale data.

Flexible data models to accommodate evolving business needs.

Data synchronization is handled either by periodic full loads or by incremental pipelines using Kafka and Flink to keep the search index up‑to‑date.

03 Problem‑Solution Space

Three perspectives are examined:

Operations: Consistency failures, CPU spikes, and data‑sync bottlenecks increase operational complexity.

Development: Learning multiple data products raises the development barrier and slows iteration.

Business: Data latency harms decision accuracy and slows time‑to‑market for innovative services.

The core non‑functional requirements identified are performance, correctness, and real‑time responsiveness.

04 Core Technology Decomposition

To solve the consistency problem of distributed queries, two approaches are considered:

Implement distributed transactions on top of a relational database.

Adopt a document‑oriented NoSQL store that co‑locates related records.

The article chooses the first approach, extending the mature ACID guarantees of relational databases while adding horizontal scalability.

The prototype, codenamed “巨鲸座” (Data Warebase), integrates:

Distributed transaction support for strong consistency.

In‑database inverted index and global secondary index for real‑time keyword search, achieving millisecond‑level sync between the business table and the index.

Unified API (SQL) that abstracts away the underlying heterogeneous components.

05 Data Warebase – New‑Generation System

Data Warebase merges the capabilities of a traditional database and a data warehouse, supporting:

Structured data via relational tables.

Semi‑structured JSON documents.

High‑dimensional vectors for semantic search (e.g., PostgreSQL Vector plugin).

Key technical pillars are columnar storage for analytical workloads, vectorized execution for massive parallelism, and materialized views to avoid repeated computation.

The system is designed to push the limits of performance, correctness, and real‑time processing while offering a minimal learning curve through a single SQL interface.

06 Real‑World Validation

Cross‑border e‑commerce : After migrating to ProtonBase (the product built on Data Warebase), the slowest query dropped from 147 s to 14 s (10.4× speedup), average latency improved fivefold, and storage cost fell by 50%.

Advertising industry : The platform achieved an 8× increase in query throughput, enabling 10 K rps write peaks, and a 20% uplift in revenue due to faster, more accurate real‑time bidding and user‑profile analysis.

Both cases demonstrate that the unified architecture eliminates data‑sync delays, simplifies the stack, and delivers the performance, correctness, and real‑time guarantees demanded by AI‑driven applications.