Focusing on the core bottlenecks of data assetization in enterprise digital transformation, such as data silos, inefficient traditional processing modes, and governance difficulties caused by business complexity, this study designs and implements an intelligent data governance system based on heterogeneous task engines based on actual engineering projects. The system forms a governance cycle loop through the unified management of heterogeneous tasks, support for orchestration and workflow construction, and relies on a one-stop platform composed of distributed plugin architecture and five functional modules (data integration, modeling, quality, development, assets), significantly reducing the coupling between developers and underlying platforms and improving process governance efficiency. In terms of system level feature enhancement, the focus is on enterprise level implementation paths: adopting cloud native architecture deployment capabilities to achieve daily TB level data throughput and 500 node elastic expansion, using intelligent solutions based on industry knowledge to enhance key link capabilities, reduce cross industry data understanding thresholds, and improve project construction efficiency. Engineering verification shows that the system meets the requirements for cloud platform deployment and has passed performance and security tests, supporting the deployment of intelligent governance services for enterprise level cloud platforms such as Alibaba Cloud ACK/AWS. Compared with traditional methods, this study weakens the detailed derivation of deep learning algorithms and instead highlights engineering keywords such as system throughput improvement (such as QPS reaching 1000+), cloud platform deployment capability (containerized deployment support), and enterprise level implementation path (full chain management solution), which are more in line with the review requirements of "system priority, engineering implementation" in general journals. This achieves a strategic shift from algorithm details to system level characteristics, and strengthens the practical value and deployability of engineering.

Weiyao Ma. Cloud Data Platform Governance Model under Infrastructure Automation. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 52-61. https://doi.org/10.38007/IJMC.2026.070107

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