The data warehouse is the foundation of the bank management system, which bears the data sharing and exchange at the database level between the banking system and the management application system. According to different data modeling methods and data integration methods, the basic data platform can be divided into two parts: the post source data platform and the integrated computing platform. The integrated computing platform focuses on the platform with high degree of data integration, and the post source data platform focuses on the post source data integration platform.
Data warehouse carries the function of one-time storage and multiple reuses of business data in enterprises. It integrates business data from a business perspective and stores long-term business historical data. It is an important data source for subsequent enterprises to carry out data analysis and business decision-making.
The overall solution of GienTech Data Warehouse is based on the hybrid architecture, and combines with big data technology on the basis of making full use of the experience of traditional data platform construction, which can create one-stop data collection and access, data integration and analysis, data application and services, and assist in digital transformation for enterprises.
Pain point 1: data warehouse specifications and processes are inconsistent, and cross-departmental cooperation is difficult (modeling specifications and development specifications).
Pain point 2: Inconsistent indicator caliber leads to the decrease of data reliability (data dictionary)
Pain point 3: data expansion leads to the shortage of computing resources, and the timing cannot be guaranteed (modeling specifications and development specifications, data products and services).
Pain point 4: Long troubleshooting and repair time (metadata and data quality management)
Pain point 5: untimely response to business requirements (self-service data retrieval +OLAP system, data products and services).
1. ODS platform is responsible for supporting the data storage of post source storage layer and providing external data services;
2. The data warehouse platform is responsible for supporting the model storage and data service of theme model layer, common processing layer and business capability layer. Meet the data storage and services required by active data exploration and model laboratory;
3. The real-time decision-making area meets the function construction of acquiring data based on bus and making real-time decision analysis based on memory database.
4. The big data processing area meets the construction of semi-structured and unstructured data processing functions based on batch and real-time streaming data;
5. The historical data storage area satisfies data archiving and supports historical data access;
6. The report query platform satisfies the construction of unified report platform, management cockpit and other application functions mainly based on report query.
7: The application service platform meets the application tool product deployment platform of proprietary computing and display engines;
8. The active exploration platform meets the functional construction of active data exploration service for business users based on data warehouse;
9. The data mining platform meets the requirements of mining model construction, verification, monitoring and model management to support in-depth decision-making;
10: The unified acquisition/exchange/scheduling platform meets the unified non-real-time data acquisition and data exchange functions of the whole bank; meets the unified job scheduling processing;
11. Data management of data warehouse relies on the data management platform to deploy data management functions related to data warehouse;
12. The security management of the data warehouse relies on the unified operation and maintenance monitoring platform within the bank and the software and hardware functions related to the data warehouse to realize the warehouse security management;
13. The data warehouse portal meets the integration of multiple application modes and functions for various users based on the integrated data area;
1. Perfect enterprise-level data warehouse solution and mature products: quickly build enterprise-level data platform, and realize unified data model, unified data governance, unified application platform, unified information release, unified scheduling and monitoring, and unified data exchange. Provide complete and sustainable solutions to enterprises and financial institutions.
2. The irreplaceable leader in China's financial IT market: Since 2018, CEC GienTech Technology Co., Ltd. has ranked first in the market share of data warehouse solutions in China's banking IT industry for three consecutive years.
3. Flexible delivery capability covering the whole country: GienTech has flexible delivery capability and professional talent management system covering the whole country, and has a professional service team of tens of thousands of people to escort enterprises and financial institutions; There are 15 R&D/design service centers in China, namely:
l North China &: East China: Beijing, Tianjin, Dalian, Changchun and Hohhot;
l Southwest &: Northwest: Chengdu, Guiyang, Chongqing, Xi 'an;
l Central China: Wuhan, Changsha and Zhengzhou
l East China: Shanghai, Guizhou, Wuxi, Qingdao, Weifang, Hefei, Nanjing, Suzhou, Jinan, Xiamen, Fuzhou and Hefei
l South China: Guangzhou, Foshan, Zhuhai, Nanning and Shenzhen.
4. Abundant successful cases provide reference: for many years, CEC GienTech Technology Co., Ltd. has accumulated a large number of successful cases, helping financial institutions to build enterprise-level data warehouses and business intelligence systems step by step.