Methodological basis for forecasting the development of the agrobusiness within the concept of big data using Hadoop technology

Abstract

Background. Currently, the volume of data generated in the agro-industrial sector is extremely large and is growing faster than the speed of computing. Thus, using traditional methods such as SQL or a single machine to store or process data can be both useless and time consuming. The effective management of modern agribusiness relies heavily on digital technologies, which, in particular, involves the implementation of forecasting technologies through the analysis of a large amount of various complex data. The practical implementation of this approach involves the development of methodological foundations for forecasting using big data technologies, a way to integrate into global digital markets in the field of agribusiness. Materials and methods. Practical options for applying the methodological foundations of analysis within the concept of big data using Hadoop technology, including HDFS, MapReduce and Hive, as well as solutions based on Python, are considered. Results. Plotly is a Python library that can be used in the field of agribusiness data visualization, has made it possible to integrate the plotting of statistical results. Conclusions. Some aspects of the Hive tool application of the Hadoop ecosystem, combined with flexible programming techniques in the Python language, made it possible to identify additional technological possibilities for creating a BigData project.

Keywords

• methodological foundations
• agribusiness development
• big data
• hadoop
• hive