Claude AI Elevates DeWu’s Financial Data Warehouse to Full-Chain Efficiency

Introduction

In e‑commerce data‑warehouse systems the finance domain has the highest complexity and the lowest tolerance for errors because financial data must be accurate and is cross‑linked with almost every other domain. Finance engineers typically perform three tasks: translating business transactions into a unified financial language, building a multi‑layer asset architecture (ODS → DWD → DWS → ADS), and guaranteeing data quality for metrics such as GMV.

Why AI Is Needed

Manual verification of dozens of tables, hundreds of metrics, and intricate cross‑period allocations is error‑prone and labor‑intensive. The article argues that large‑model AI can inject strong reasoning into the entire pipeline—requirement understanding, code generation, quality testing, and documentation—thereby reducing human mistakes and repetitive work.

Three Core AI Capabilities

Massive context window: 200k+ token windows allow the model to ingest full schema definitions, dictionaries, and metric logic in a single “working memory”, enabling whole‑domain reasoning.

Automatic business‑semantic abstraction: The model understands terms such as “daily active users” or “retention” and maps them to concrete SQL, reducing rework caused by mis‑interpreted requirements. Claude is highlighted as superior in this regard.

Regulated execution beyond human limits: When given clear prompts and documentation, the model consistently follows standards, maintaining high compliance even under tight deadlines.

Application Scenario Overview

The article groups use‑cases from “single‑point efficiency gains” to “full‑chain enhancements”. Key scenarios include:

OneData standardised modelling for financial accounting projects.

AI‑assisted SQL coding for UE table iterations.

AI‑driven data testing for complex financial logic.

AI‑powered PRD‑to‑code conversion for intricate fee‑logic updates.

Core Use‑Case 1: OneData Standardised Modelling

Financial accounting involves >100 tables, multiple sub‑domains, and hundreds of metrics, making manual modelling infeasible. The article outlines four difficulty points: complex metric lineage, inconsistent naming conventions, cross‑domain dependencies, and cumbersome documentation. The proposed methodology combines three steps:

Prompt × Iterative Convergence × Massive Document Reading: High‑quality normative documents (naming rules, field‑level dictionaries, full‑stack design principles) are fed as prompts.

Iterative refinement: Each AI output is validated against critical fields (e.g., allocation logic, reversal logic) and the prompt is updated.

Massive context ingestion: The model reads all historical design documents at once, producing architecture diagrams and model suggestions.

After several iterations the model generated a complete OneData solution, yielding significant efficiency improvements, higher compliance with naming standards, strong reusability across sub‑domains, and an automated data‑quality‑control rule set.

Core Use‑Case 2: AI‑Assisted SQL Coding

For a UE‑table iteration, the AI performed:

Requirement整理 → 技术文档 creation.

Field‑source analysis on DWD layer.

Automatic generation of ETL code across DWD → DWS → ADM.

Naming‑standard alignment using the metric dictionary.

Self‑generated test SQL and validation loops.

Results included clearer code structure, reduced development time, and performance optimisation through partition pruning and predicate push‑down suggestions.

Core Use‑Case 3: AI‑Driven Data Testing

Financial data testing faces high complexity: multi‑field impact, strict mathematical formulas (e.g., 正向‑冲销 = 冲销后), cross‑period allocation, and extensive edge‑case coverage. The AI automatically generated test cases covering:

Formula verification (forward‑minus‑reversal).

Aggregation consistency.

Business‑rule translation.

Boundary‑scenario checks.

In a fee‑related UE table project, AI‑generated test SQL reduced the time to produce comprehensive test suites, uncovered hidden bugs, distinguished acceptable precision errors, and improved overall delivery quality.

Core Use‑Case 4: AI‑Powered PRD Conversion

PRD documents often contain ambiguous language. By prompting the model to read the “fee‑logic” document and compare it with existing code, the AI produced a structured change list (new fields, deprecated fields, logic adjustments) and generated the corresponding DDL/DML with explanatory comments, dramatically shortening the alignment cycle between product and engineering.

Benefits and Outlook

Quantitative outcomes after AI adoption include:

Significant reduction in manual effort for metric lineage tracing and documentation.

Higher adherence to naming and lifecycle standards.

Reusable assets (prompts, scripts, SOPs) across accounting and cost‑analysis domains.

Accelerated development cycles and earlier baseline completion.

The article concludes that large‑model AI is not limited to finance; it can be rolled out to other warehouse teams by selecting 1‑2 well‑defined pain points, sharing prompt designs, and establishing a “human‑define‑rules + model‑execute” collaboration model.