## 2 Related Works  
Studying the techniques of data storing is one of the most important challenges, not only for commercial software development, but also in the field of scientific research. There are many studies aimed at finding the most optimal tools for storing data, as well as methods of storing data, depending on the software architectures used [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. Depending on the approach to data storage methods, several groups of studies can be distinguished.  
Most of the research in the field of data storage is related to the analysis of NoSQL solutions [16], as well as the choice between relational and NoSQL databases [17]. Of the greatest interest in the preparation of this article is the research related to the use of relational and NoSQL databases to create data marts. The study in [18] describes an approach to creating a data mart based on a MongoDB document database, which implies the use of the JSON format for the final selection. In turn, relational databases have wider functionality, both for creating data marts and as tools for OLAP technologies [19].  
Exploring different storage file formats represents a different area of research. In [20, 21, 22, 23, 24], studies of big data storage formats, such as Apache Avro, Apache Parquet, ORC, and others are presented. These studies present the results of studying various formats in terms of performance or format selection for a specific purpose. Thus, the research in [20] is related to the study of file formats for storing data in web systems. The most popular Apache Parquet and Apache Avro formats are explored here. The research in [21] aims to study data storage formats for problems in the field of bioinformatics and examines formats, such as Apache Parquet, ORC, Apache Avro, and other popular formats. In [22], similar problems of resource efficiency are considered, but only the storage formats of Apache Avro and Apache Parquet are investigated. The research in [23] is a comprehensive study of the ORC and Apache Parquet file formats, including load testing using various tools for working with these formats. These formats are very similar in structure and data storage methods, making this research the most valuable. However, this study is more theoretical in nature as it aims to study the formats themselves without being tied to a specific task.  
Despite the detailed study of data storage technologies, the study of data marts using the Apache Hadoop platform is a novel direction. Research aimed at exploring these technologies is often either conceptual-level research or considers Apache Hadoop only as a source of data for creating data marts in other systems. For example, [24] describes a conceptual model for creating data marts in MPP Greenplum using data from a data lake based on Apache Hadoop.  
This study supplements the analyzed studies and considers a number of the most popular formats in relation to the tasks of developing data marts within the Apache Hadoop platform without using additional data storage tools.  
In addition, there are a number of studies related to developments in the field of software integration and the choice of the optimal tool for various purposes using different decision-making methods [25; 26; 27; 28]. The choice of system components is an important condition for the correct operation of the software complex. As data storage files are part of the analytical platform, studying the possibilities of various formats for developing data marts is an important task for the correct operation of the system as a whole.