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ANALYSIS OF PROBLEMS AND APPROACHES TO INFORMATION PROCESSING DURING BIG DATA MIGRATION

Badanov Artem Andreevich  (Lead Developer at Amberdata LLC)

This article examines information processing challenges arising during big data migration in the context of heterogeneous and multivariate data warehouses. A one-time data migration scenario, typical for infrastructure modernization, technology platform transitions, and data source consolidation, is examined as a methodological constraint. It is shown that transferring data and associated metadata between systems with different storage models significantly complicates information processing. This paper identifies and analyzes five key issues that have the greatest impact on the correctness and efficiency of big data migration: differences in supported data types and storage models, limited portability of data processing methods, failures and errors when processing large volumes of information, the specifics of data extraction from non-standard sources, and the limited computing resources of the source systems. For each of these challenges, approaches and mitigation methods are considered, based on adapting data processing processes to architectural and resource constraints. The research results can be used in the design and implementation of big data migration processes in modern information systems and also serve as a basis for developing adaptive and hybrid approaches to information processing.

Keywords:big data, big data migration, information processing, data warehouses, ETL, storage models, computing resources, big data migration challenges.
 

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Citation link:
Badanov A. A. ANALYSIS OF PROBLEMS AND APPROACHES TO INFORMATION PROCESSING DURING BIG DATA MIGRATION // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№02/2. -С. 49-57 DOI 10.37882/2223-2966.2026.02-2.06
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