A large number of information-based and automated systems have generated massive and multi-source heterogeneous data. The data standards of these system data are inconsistent, resulting in numerous data silos that prevent the realization of data integration and correlation analysis.

To support the implementation of intelligent decision-making and intelligent applications in production, distribution, customer service and marketing, as well as comprehensive management, and to provide timely data support for these operations, the demand for real-time data fusion management is strong.

The self-developed code approach results in low development and delivery efficiency, lacks intuitive monitoring of task status and other operation and maintenance functions, and the management functions related to user permissions and security are not complete.

Provides a one-stop data integration platform with data pipeline building, task management, user management, and metadata management. It supports real-time incremental data capture from various databases with minimal impact on both source production systems and target databases.

Supports high availability and containerized deployment with smooth and flexible dynamic scaling. Different computing tasks can be redistributed across machines without disrupting other operations. The platform offers stability-focused configuration strategies across node management, pipeline management, and task management layers.

Provides data engineers and operations personnel with an intuitive data task map, enabling real-time insights into data flows and significantly improving operational efficiency and effectiveness.