Modern Design Paradigms for Decision Support Systems: From Integrating Data Warehouses and Data Lakes to the Data Lakehouse Architecture

<ns0:p>The article examines modern approaches to the design of decision support systems in the conditions of rapid growth of information volumes. Data warehousing information technology was born in the bowels of IBM and was finally formulated by B. Inmona and R. Kimball in the 90s of the last century as a method of solving information and analytical tasks in the field of decision-making and support. Arising at the intersection of database technology, decision support systems (DSS) and computer data analysis, the concept of data storage later evolved as it proved suitable for a wide range of applications in business, science and technology. Systems built on the basis of data warehouses have a number of characteristic features that distinguish them as a special class of information systems. Such features include the subject orientation of the system, the integration of data stored in it, collected from various sources, the invariance of this data over time, the relatively high stability of the data, the need to find a compromise in the redundancy of the data. The article examines the evolutionary transition from the use of isolated Data Warehouses and Data Lakes to a conceptually new hybrid Data Lakehouse architecture. The technological foundation of open table formats (Delta Lake, Apache Iceberg, Apache Hudi) and their role in ensuring the transactional reliability (ACID) of file systems are analyzed. The feasibility of applying the "medallion architecture" pattern (bronze, silver, and gold layers) for effective data quality management, collaborative work of machine learning algorithms, and management BI reporting preparation is substantiated.Received 2026-02-27Accepted 2026-04-08</ns0:p>