Implementation of Modeling and Distribution of a Big Data Warehouse

The exponential growth of data generated by modern applications and devices poses a substantial challenge for organizations in efficiently storing, processing, and accessing large datasets. Traditional data warehousing solutions struggle to handle the scale and complexity of Big Data, leading to performance bottlenecks, increased latency, and limited data availability. Designing an effective Big Data Warehouse can cope with these challenges while providing real-time access to valuable insights remains a pressing concern. The implementation of the system presents significant challenges in managing and processing vast volumes of data while ensuring efficient data distribution and accessibility. This paper created four different types of databases on the data containing information about sales in bakeries. The creation of a database is directly specified by its type. Subsequent processing of data and reports is also different for individual types. In the practical part, it will then implement individual types of databases and their options for storage, analytical processing and subsequent reports. I will then compare the individual prototypes.