Resumen
The article presents an analysis of approaches to the development of enterprise information systems that are in use today. One of the major trends that predetermines the agenda of information technology is the focus on parallel computing of large volumes of data using supercomputing technologies. The article considers the resulting ubiquitous move to distributed patterns of building enterprise information systems and avoiding monolithic architectures. The emphasis is placed on the importance of such fundamental characteristics of enterprise information systems as reliability, scalability, and maintainability. The article justifies the importance of machine learning in the context of effective big data analysis and competitive gain for business, vital for both maintaining a leading position in the market and surviving in conditions of global instability and digitalization of economy. Transition from storing the current state of a enterprise information system to storing a full log and history of all changes in the event stream is proposed as an instrument of achieving linearization of the data stream for subsequent parallel computing. There is a new view that is being shaped of specialists at the intersection of engineering and analytical disciplines, who would be able to effectively develop scalable systems and algorithms for data processing and integration of its results into company business processes.