Nowadays there is a huge growth of massive amount of data generated by different sources at real time or near real time. Generally data is heterogeneous by its content and exists at every human sphere such as education, government, finance, medicine and so on. This paper is about the possibility to use the Kolmogorov complexity as a technological innovation in such an area as engineering education. The Introduction section describes fundamental issues (data storage, data security and high speed access to data) from the point of view of software for teaching's choose. The first part of the paper related to infrastructure's development rules. Second part compares two ways of problems' decision: supercomputer and distributed computing systems according to expenses criterion. It is shown that last way applying allows to assign and handle operations on homogeneous or heterogeneous nodes with less expenses compared to supercomputers. However data and operation partition and distribution among nodes could be a challenge by itself - the third part of the paper is about it. Furthermore, nodes interaction can cause significant difficulties as generally distributed systems consist of great number of nodes which could be constant in quantity or constantly growing. In practice, most systems should be scalable due to incessant data growth and tasks for processing that means that any system needs to meet with such circumstances and not to be limited by constant number of nodes. Fours part of this article analyses the possibility of using Kolmogorov complexity for optimal way of data dividing and processing in distributed computing systems without quantity nodes limitations. The last part describes the theory investigation on how this fundamentals of distributed systems could be beneficial to eLearning technology in universities and business companies. In Conclusion the advantages and disadvantages of Kolmogorov complexity were considered applying to eLearning study aims.

Engineering education; Distributed systems; Kolmogorov complexity; Data partition; Data distribution.