I.O. Shkokov1

1 AVIV Group Gmbh (Paris, France)

Modern relational databases (RDBs) serve as the backbone for storing and processing vast amounts of information used for business decision-making in large corporations. However, data quality in these systems can be compromised for various reasons, including input errors, failures in ETL (Extract, Transform, Load) processes, or data loss during transformation. In environments where there is a shortage of skilled data engineers, the responsibility for monitoring and maintaining data quality often falls on data analysts, highlighting the need for fast and effective methods to track and improve data quality.

Objective – the objective of this paper is to develop and present practical methods for monitoring data quality in relational databases using SQL. The methods described in the article are aimed at ensuring the reliability of decision-making based on data and can be applied in both private and public organizations.

The paper proposes three methods for monitoring data quality: 1. Creating BI dashboards to visualize data volume, which helps in promptly identifying data gaps or issues in the data loading process.2. Analyzing the completeness of columns in SQL tables, which assists in monitoring the presence of NULL or empty values that serve as indicators of data transformation issues. 3. Setting up alerts for poor data quality, which automatically notify data professionals about emerging data issues. Applying these methods in real-world commercial projects has demonstrated their effectiveness in quickly detecting errors and restoring data quality.

The proposed methods can significantly enhance data management processes in organizations by enabling prompt detection and resolution of data quality issues. This will improve the reliability and accuracy of business decisions and optimize the work of data professionals, particularly in environments with a shortage of skilled data engineers.

Shkokov I.O. Methods for error detection in streaming data of modern relational databases using Structured Query Language technology in large organizations. Science Intensive Technologies. 2025. V. 26. № 5. P. 17−25. DOI: https://doi.org/ 10.18127/j19998465-202505-02 (in Russian)

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