## 1 Introduction  
When developing analytical systems, solving the issue of storing the loaded data becomes an important task. One of the most popular methods for storing information involves the use of relational databases [1]. However, the growth in the volume of data, the increase in the number of data acquisition channels, as well as the diversity of the structures of the data obtained have led to the emergence of several directions of research in the field of data storage [2]. Among such areas, the concept of big data stands out [3]. The concept of big data is based on six aspects: value, volume, speed, variety, reliability, and variability [4]. This means that the term \"big data\" refers not only to the volume of these data, but also their ability to act as sources for generating valuable information and ideas [4]. Among the modern methods of storing big data, the architectural concept of building data lakes stands out [5]. The data lake is a scalable system for storing and analyzing data stored in their original format and used to extract knowledge [5]. It is clear from the definition that information enters such a system with an original structure, which requires the creation of a tool for reducing data to a single structure that is understandable to the end user. In addition, when analyzing information it is often necessary to select only highly specialized data intended for solving a specific problem [6]. To solve the described problems, the technology of creating data marts is used [7]. Data marts are a slice of the stored information with a highly specialized focus on the requests of the employees of a particular department or the specifics of the tasks assigned. One of the software tools that allows the building of both data lakes and data marts is the Apache Hadoop platform [8]. Data storage in this system is supported by writing information to files in the HDFS (Hadoop Distributed File System) [9]. As the HDFS does not have specialized file formats, it becomes necessary to choose a storage format from a variety of developed formats. Among the most widely known formats used in Apache Hadoop and the HDFS are JSON [10], CSV [11], Apache Parquet [12], Apache Avro [13], and Apache ORC [14]. Each of these file formats has its own characteristics in the file structure, which determines its characteristics in terms of such criteria as write speed, read speed, occupied space, and others. This paper presents a study of data storage formats in relation to the creation of data marts for the tasks of creating reports during mass testing. The data source for the study was the Digital Psychological Platform for Mass Web-Surveys [15], developed for conducting psychological research in educational institutions using the methods of mass online testing. The aim of this paper is to study the use of different HDFS file formats applied to data mart development within the Apache Hadoop platform and to assess the effectiveness of the most popular file formats used in the HDFS. The study analyzed the effectiveness of choosing a format for building reports based on the data obtained. To analyze the formats, a number of criteria were selected, and experiments were carried out on the Hadoop clusters. This paper consists of six sections. The second section provides an analysis of works related to the topic of this research. It provides an overview of the main research studies aimed at exploring various storage media in large systems and the main trends, and it justifies the need for ongoing research. The third section provides an overview of the studied data storage formats in the HDFS, the internal structure of data formats, supported data types, and data storage methods. The fourth section describes the experiment carried out and gives information about the configuration of the experimental stand, information about the data used for the experiment, and the experimental scheme.