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Abstract <p>The article proposes the concept of creating a distributed industry platform (DIP) to improve the quality and accelerate developments in the field of IT technologies. The role of database management systems (DBMS) for building a system of data collection, storage and processing in the construction of ROP, where a large amount of heterogeneous data is collected within a single platform, is considered. By creating a local development environment within the platform and deploying the necessary software, developers have the opportunity to develop in the data ecosystem (EDS). In addition, distributed datasets can be created from EDS, and then data analysis systems integrated into the platform can be used. This is especially important when using artificial intelligence tools, where large amounts of raw and prepared data are required for training and testing neural network-based models.</p>

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About the Concept of Building a Distributed Ecosystem for Storing and Processing Data within the Framework of an it Solutions Development Platform

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • G. D. Dik,
  • A. G. Dik,
  • A. V. Bogdanov,
  • T. V. Avtomonova,
  • V. I. Yyurov

摘要

Abstract

The article proposes the concept of creating a distributed industry platform (DIP) to improve the quality and accelerate developments in the field of IT technologies. The role of database management systems (DBMS) for building a system of data collection, storage and processing in the construction of ROP, where a large amount of heterogeneous data is collected within a single platform, is considered. By creating a local development environment within the platform and deploying the necessary software, developers have the opportunity to develop in the data ecosystem (EDS). In addition, distributed datasets can be created from EDS, and then data analysis systems integrated into the platform can be used. This is especially important when using artificial intelligence tools, where large amounts of raw and prepared data are required for training and testing neural network-based models.