With the continuous growth of business and demand, Guangfa Bank encountered many difficulties in the process of data governance and platform construction of big data:
Business needs are urgent, but the time for software delivery to differ greatly from business expectations.
The logic of the existing system architecture is relatively complicated, and it is difficult to quickly expand to meet the requirements of business peak periods.
New technologies are emerging endlessly, and the requirements for teamwork and personnel skills are getting higher and higher.
In order to solve these problems, Guangfa Bank optimized the system architecture and delivery model.
Split the system architecture into application architecture and data architecture: The data architecture uses data as the mainstay for 7 * 24 hours of processing and data factory mode. After processing, the final data results are directly passed to the application architecture through the middle layer for input; The application architecture is transformed into microservice and containerization to improve the flexibility and delivery efficiency of application services.
the continuous integration and continuous delivery of Delivery Mode Application, and build a delivery pipeline that includes code management, baseline control, automatic build review, automated testing, and environmental deployment.
In the risk control scenario, through big data analysis and data relationship mining, you can find customers who could not be contacted before and increase the customer repayment rate.
The system's operation and maintenance analysis can discover abnormalities such as risky transactions, intrusion detection, and password detection through data analysis. You can also view network packets through big data technology to discover sensitive transactions, sensitive data and then discover risks.