Financial Industry - Data Center Platform | Digital Transformation

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Yinhua Fund · Background

To meet business development needs, the company has continuously built its data warehouse system since 2016, completing the construction of marketing data center and investment research data center. Based on internal and external data statistics, extraction, and regulatory reporting needs, it built a reporting platform focused on marketing and investment research analysis, which met the company's daily business reporting and data analysis needs for a considerable period.

However, with the rapid growth of business data scale and business development, higher requirements were placed on the data warehouse system. The main challenges included low development efficiency and high maintenance costs of the original Oracle data warehouse, single service form, reliance on manpower to meet business needs, unclear data structure for core business marketing, investment research, and anti-money laundering operations, and significant bottlenecks in big data computing and storage. There was an urgent need to meet business requirements for high-performance data processing, efficient data services, complex business calculations, massive data queries, metadata, and data quality management. Under these business circumstances, the company proposed building a fund data middle platform in 2020.

Project Objectives

01
Massive Data Processing

Meet the needs for massive data processing, historical business data storage, and high-performance indicator calculations in the data center.
02
Unified Data Asset Management

Achieve unified convergence of scattered data, standardized processing, unified operation, storage, and management of assets, completing enterprise digital infrastructure.
03
Core Capability Enhancement

The long-term goal is to provide more powerful data storage, processing, computing, and data service platforms, achieving strong support for enterprise marketing, investment research business analysis, and intelligent applications, thereby further enhancing the company's various services and competitive capabilities.

Solution Architecture

The Yinhua Fund data center platform, built based on the data development management platform and data asset catalog, integrates data from various business systems and quickly achieves historical data migration, marketing center and investment research center theme data asset construction. Through distributed computing capabilities, it solves the migration and calculation of YOGA TA data that the original Oracle data warehouse could not achieve. Meanwhile, based on the data asset catalog, it realizes data asset operation and management, with significant improvements in data quality and data security management. Through the data service capabilities of the data middle platform, it supports upper-layer data application construction including marketing theme reports, performance analysis, risk control systems, and anti-money laundering projects.

Customer Benefits

Upgrading from traditional Oracle data warehouse architecture to big data center platform architecture has shown significant improvements in data collection objects, data storage and computing architecture, core data architecture, development management tools, and data service capabilities.

Service Capability
Management Tools
Data Architecture
Processing Architecture
Data Objects

Our data middle platform consolidates fragmented data demands through standardized services, supporting both operational analytics and intelligent applications like risk control and AML.
Replacing manual processes with automated tools enhances Yinhua Fund's data governance, significantly improving data quality and security.
The upgraded layered data warehouse resolves structural coupling by organizing data into clear thematic domains, reducing usage complexity and costs.
Our hybrid compute-storage architecture handles batch/real-time data at TB/hour scale, separating operational and analytical data while boosting processing power.
The new scalable infrastructure supports diverse data sources (mobile/web-crawled) beyond internal systems, overcoming Oracle's limitations in volume and format handling.