Tools for Big Data transformation and analysis

Abstract

This article examines modern tools for the transformation and analysis of Big Data, which is due to the sharp increase in the volume of data coming from various sources in the digital age. In the context of a highly dynamic information flow, traditional data processing methods are giving way to distributed and parallel technologies, which significantly speed up the analysis process and increase the accuracy of the extracted knowledge. The methodological basis of the research is based on a comparative analysis of existing big data processing and analysis platforms such as Apache Hadoop, Apache Spark, as well as specialized frameworks for machine learning. Analytical processing, modeling, and experimental testing methods were used, which made it possible to evaluate the performance and stability of each tool when performing complex computational tasks. The experiments were conducted using standard loads typical of real business scenarios, which ensured the reliability of the results obtained. The experimental results have shown that hybrid approaches combining the capabilities of distributed storage, parallel processing, and machine learning algorithms can significantly reduce data processing time and improve the quality of analytical conclusions. It was found that systems optimized for specific types of data and tasks show better results compared to universal solutions. The experimental model demonstrated that the integration of various tools within a single platform contributes to more effective detection of hidden patterns and anomalies, which is an important aspect in making managerial decisions. The discussion of the results confirms the prospects for further research in the direction of the development of Big Data technologies. The authors note the need to create adaptive systems capable of responding quickly to changes in the volume and structure of data, as well as integrating new analysis algorithms. The work is of practical importance for the development of solutions in the field of business intelligence, medicine, finance and other areas where big data processing is becoming a key element of innovative development. Further research in this area may lead to the emergence of new techniques and technologies that contribute to a deeper understanding of the processes occurring in large information flows.