| Summary: | 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.
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