Exploration of Enterprise Big Data Microservice Architecture Based on Domain-Driven Design (DDD)
Yiru Zhang · Procedia Computer Science · 2026
As digitization and intelligence are rapidly changing, the importance of enterprise big data processing platforms in data management has grown over time. But the high level of coupling seen in traditional monolithic architectures cannot meet the challenge of growing maintainability and complexity of demands in the face of business expansion and the increase in the amount of data and so cannot scale to higher demands with respect to platform scalability and reduced data collection efficiency. The article suggests one solution to enterprise big data processing platform implementation on the platform of microservice architecture, grounded in the theory of Domain Driven Design (DDD). In-depth analysis of the business requirements allowed the recognition of the functional and non functional requirements of the platform in different situations and this deformation of the central business logic into independent microservice modules allowed data collection, parsing, cleaning and visualization functions to be developed, deployed and upgraded separately thus enhancing the flexibility and scalability of the system. The given article also develops an automated data collection process and organizes it using microservices and an enhanced dynamic timing algorithm in order to allocate the data collection processes effectively to the Docker nodes and provide the real time monitoring of the collection progress and services to make the data collection correct and efficient. Based on the implementation and trial of the platform, the following results have been confirmed: the enterprise big data processing platform on the basis of microservice architecture has enhanced considerably the criteria of scale, the quality of the data, and the efficiency of collection.