Showcase: GBASE Outstanding Financial IT Innovation Solutions — Data Warehouse System Solution

To promote efficient and orderly implementation of financial IT application innovation and continuously provide referenceable, replicable guidelines and demonstrations for the financial industry, the Financial IT Application Innovation Ecosystem Laboratory (hereinafter referred to as the "Lab") has successively organized the first and second rounds of financial IT innovation solution evaluation since November 2021. Multiple solutions from GBASE (General Data Technology) stood out and were honored as "Outstanding Financial IT Innovation Solutions," garnering widespread attention and recognition.

To this end, the Lab has launched a special column to showcase outstanding financial IT innovation solutions. Now let's explore GBASE's excellent solutions together!

Data Warehouse System Solution

Solution Overview

This solution adopts GBase 8a MPP Cluster database to build an active-active cluster (business-active) architecture. Both centers can perform read and write operations simultaneously, with data synchronization between them. The cluster uses a distributed flat architecture that can scale out dynamically, supporting massive data storage and large-scale parallel computing. The system data model includes: data source layer, data exchange layer, data processing layer, data mart layer, data service interface layer, data application layer, and unified service layer. It can provide unified data services for multiple marts, branch marts, analysis and mining, etc.

Pain Points and Challenges Addressed

(1) Information silos. In traditional data warehouses, the closed architecture results in separate storage for various business systems such as core banking, personal loan, general ledger, credit, online banking, and risk management, thus creating information silos.

(2) Insufficient computing power to meet business demands. The original database appliance equipment was not only costly but also ill-suited for large-scale horizontal scaling, failing to meet the performance requirements for ultra-large-scale data computation and making it difficult to complete data processing tasks within tight time windows.

(3) Resource contention between ad-hoc queries and batch jobs. Conflicts between ad-hoc queries and batch jobs, coupled with insufficient platform computing capacity, limited the resources allocated to ad-hoc queries during peak batch operations. This resulted in low concurrency and poor user experience for business departments.

Typical Case: Data Warehouse Project at a State-Owned Bank's Head Office

This case built a new-generation data warehouse system based on Phytium chips, Kylin operating system, and General Data Technology's GBase 8a MPP Cluster database. The new system fully supports applications in external regulatory reporting, asset-liability management, business management, and risk management, covering 8 major business areas, 33 business lines, and over 120 business scenarios, delivering deep big data analysis and comprehensive applications that provide an innovation engine for accelerated business growth.

Typical Case: Audit System Project at a Provincial Rural Credit Cooperative

This case built a data warehouse platform based on Kunpeng chips, Kylin operating system, and General Data Technology's GBase 8a MPP Cluster database, achieving large-scale parallel processing of massive data. With features such as high performance, high scalability, and high availability, it addressed issues like system performance degradation and the inability to linearly scale the database, supporting the rapid development of auditing and data analytics.

Financial IT Application Innovation Ecosystem Laboratory

The Financial IT Application Innovation Ecosystem Laboratory (the "Lab") was established to implement the national innovation-driven development strategy. Led by the People's Bank of China and organized by China Financial Electronicization Group Co., Ltd., it involves collaboration with major financial institutions and industry organizations, with participation from industry, academia, research, and application entities. Adhering to the principles of "joint discussion, joint development, mutual benefit, and sharing," the Lab is dedicated to serving as a key infrastructure and professional experimentation platform for financial IT application innovation.

The Lab adheres to a goal-oriented and problem-oriented approach, pooling multi-party resources to address common challenges in financial IT innovation and establish best practices. Since launching its solution collection initiative, the Lab has gathered over a thousand solutions, building a case library that provides referenceable, replicable, and scalable guidelines and demonstrations for financial institutions.

The Lab will soon launch the third round of solution collection. We welcome more organizations to actively participate, presenting and disseminating more and better solutions. Together, we will empower financial IT innovation and digital transformation, and contribute wisdom and strength to the high-quality development of the financial industry and the self-reliance and self-strengthening of science and technology.

General Data Technology Co., Ltd.

Founded in 2004, General Data Technology Co., Ltd. (GBASE) has been dedicated to the R&D and promotion of self-controlled databases, providing products and services to tens of thousands of users in sectors including government, finance, telecommunications, public services, energy, transportation, and national defense. Its financial clients include policy banks, large state-owned banks, joint-stock banks, city commercial banks, rural credit cooperatives, insurance companies, and securities firms.

The GBASE database series includes: centralized transactional databases, distributed transactional databases, distributed analytical databases, cloud data warehouses, and more. GBASE databases have been deployed in over 110 banks, spanning core banking, core transaction systems, general transaction systems, office systems, data warehouses, big data platforms, data middle platforms, data lakehouse integration, and other applications. Specifically, the centralized transactional database GBase 8s has been adopted in core banking systems at city commercial banks, and the distributed analytical database GBase 8a has been used in data warehouse projects at several large state-owned banks and state-owned insurance companies.