Data Warehouse End-to-End Solution
Data Warehouse End-to-End Solution

A comprehensive, scientific, and iterative design and development framework, combined with automated development tools and implementation standards, delivering data services to banking, insurance, securities, funds, government, manufacturing, and other industries.

Industry Background

As enterprises expand, business data accumulates rapidly. Over time, as business systems operate, the volume of stored data grows, increasing database load and degrading system performance. At the same time, traditional data storage formats often fail to meet the diverse analytical requirements of different business units. To improve database performance, support data analysis, and enhance executive decision-making, enterprises need to establish a dedicated data warehouse.


Customer Pain Points
Solution Overview

GienTech’s Data Warehouse End-to-End Solution draws on years of consulting and implementation experience to offer a scientific, complete, and iterative design and development framework, supported by automated development tools and implementation standards. It provides data services across banking, insurance, securities, funds, government, manufacturing, and other sectors.


Based on a hybrid architecture that combines traditional data platform expertise with big data technologies, the solution enables enterprises to build a unified data storage and management center that supports data acquisition and integration, analysis, application, and service delivery. It empowers intelligent data management, enables business analytics and decision support, and accelerates digital transformation.


Depending on data modeling and integration methods, the solution encompasses four key layers: Source Data Area, Integrated Processing Area, Consolidated Data Area, and Application Data Area.


Key Features
Source Data Area

Based on the traditional ODS design concept, this layer keeps the data structure largely consistent with the source systems. Minimal processing or cleansing is performed, and permanent historical data is preserved using a zipper technique.

Integrated Processing Area

Designed according to normalization principles, business data is categorized by subject and standardized to achieve consistency across systems. Historical data is also preserved using a zipper storage method.

Consolidated Data Area

Processes and integrates data according to business interface requirements, with support for visual, user-friendly creation of data interfaces.

Application Data Area

Provides processed result datasets to various analytical applications. Data indicators are tailored to application needs, improving display efficiency and enhancing the user experience.

Our Advantages
Case StudiesView More
Data Warehouse System for a Development Financial Institution
Data Warehouse System for a Development Financial Institution

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