Design and Implementation of a Financial Data Warehouse: Architecture, Modeling, Quality Control, and Metadata Management

Background : Since 2018, the rapid growth of business lines and data analysis requirements have made the construction of a financial data warehouse increasingly urgent.

Key challenges addressed : disorganized data storage and integration, undefined data quality standards, lack of unified metadata management, and high maintenance costs.

Modeling overview : Explains why data warehouse modeling is essential, compares ER, dimensional, and Data Vault models, and selects dimensional modeling for its business‑oriented flexibility.

Warehouse architecture : Introduces a layered architecture consisting of I (Inbound), C (Consolidation core), S (Subject), and R (Report) layers, each with specific functions and modeling principles.

Data quality monitoring : Describes a monitoring system that provides automatic validation, real‑time alerts, scoring, and weekly quality reports, illustrated with architecture diagrams.

Metadata management : Details naming conventions, review and accountability mechanisms, and consistency checks, all supported by a dedicated metadata management platform.

Construction standards : Provides field and table naming rules, as well as SQL coding standards to ensure readability, maintainability, and alignment across the warehouse.

Conclusion and outlook : Summarizes the achieved architectural framework, business knowledge base, and metadata management system, and outlines future work focusing on usability and timeliness improvements.

References : 1) "Big Data Journey – Alibaba’s Big Data Practice"; 2) "The Data Warehouse Toolkit – The Definitive Guide to Dimensional Modeling".