ArticleOpen Access http://dx.doi.org/10.26855/acc.2026.09.003
Enterprise Data Automation Systems Driven by Cloud-native Data Platforms
Weiyao Ma
Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, USA.
*Corresponding author: Weiyao Ma
Published: July 17, 2026
Abstract
In the context of the continuous deepening of enterprise digital operations, the scale of data and the complexity of business are expanding simultaneously. The traditional data processing system gradually shows structural bottlenecks in terms of real-time performance and scalability. The cloud-native data platform, relying on a containerized runtime environment, a distributed storage structure, and service-oriented data components, provides a stable technical foundation for enterprises to build an automated data processing system. Data collection, data processing, and task scheduling thus form a continuous-running data pipeline. Under the support of the platform-based data architecture, enterprises can integrate multi-source business data into the automated processing flow. Data access, data cleaning, transformation, and storage management are organized into an orchestrable data pipeline system, and data resources shift from decentralized man-agement to platform collaboration. Under the condition of concurrent development of high-concurrency data environments and complex business scenarios, such cloud-native data automation systems gradually become an important form of enterprise data infrastructure and provide a stable data operation framework for real-time data analysis, business decision-making, and platform-based operations.
Keyword
Cloud-native data platform; enterprise data automation; data pipeline system; distributed data architecture
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Copyright
© 2026 by the author(s).
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How to cite this paper
Enterprise Data Automation Systems Driven by Cloud-native Data Platforms
How to cite this paper: Weiyao Ma. (2026) Enterprise Data Automation Systems Driven by Cloud-native Data Platforms. Advances in Computer and Communication, 7(3), 130-134.
DOI: http://dx.doi.org/10.26855/acc.2026.09.003