Construction and optimization of OLAP cubes for analytical processing

The article is devoted to the construction and optimization of OLAP cubes (Online Analytical Processing) for analytical data processing. OLAP cubes are a powerful tool for multidimensional analysis, allowing you to efficiently process large amounts of data and obtain analytical information for making management decisions. The key stages of creating OLAP cubes are considered, including defining requirements, designing data schemas, the ETL process (extraction, transformation, loading), as well as configuration and testing. Special attention is paid to optimization methods such as indexing, partitioning, caching and pre-aggregation of data. Modern technologies and tools for implementing OLAP solutions such as PostgreSQL, Apache Kylin and ClickHouse are discussed. The article highlights the importance of OLAP cubes in various industries, including finance, logistics and healthcare, where data analytics plays a key role in improving the efficiency and competitiveness of organizations.