With the advancement of information technologies, systems such as financial information systems, MRP, and ERP have been further developed within enterprises. However, the lack of a unified platform to connect, integrate, and relate data across these systems results in poor coordination between key processes such as production, sales, and inventory. This makes it difficult to fully unlock the true value of enterprise data.

Although enterprises have accumulated a large amount of historical data, the absence of proper data management planning in earlier stages has led to inconsistent data quality. Issues such as scattered, non-standardized, and inconsistent foundational data make it difficult to support advanced data analytics and decision-making applications.

Traditional data integration tools often rely on centralized architectures, which limit scalability and flexibility. As a result, they cannot effectively adapt resource allocation to evolving business needs, leading to lower resource utilization and reduced overall system efficiency.

Integrates data from various business information systems and channels, enabling the fusion of heterogeneous data sources and breaking down data barriers between different industrial systems.

An agile data quality platform that integrates both streaming and static data, enabling timely detection of data quality issues.

The product offers flexible scalability, allowing horizontal scaling of nodes based on resource consumption. This architecture meets the demands for both high performance and high scalability.