DATA WAREHOUSE OPTIMIZATION TECHNIQUES: A SURVEY OF QUERY PROCESSING, STORAGE, AND PERFORMANCE ENHANCEMENT METHODS

Data warehouses today are an essential part of existing Business Intelligence (BI) solutions, that collect data from multiple sources and enable analytical processing for strategic decision making. However, as enterprise data grows, and analytical queries become more complex, faster insights are demanded that present a new set of challenges with query execution and storage, scalability, and overall system performance. This survey aims to give an overview of the data warehouse optimization techniques with emphasis on three important areas: Query Processing Optimizations; Storage Optimizations, and Performance Improvement Techniques. Popular techniques like query transformation, access path selection, join optimizations, data partitioning, indexing, materialized views, memory and buffer management, workload management, resource allocation, parallel processing and load balancing are covered. Furthermore, a comparative study of recent research is given highlighting the research trends, strengths and limitations and gaps in the literature. The survey reveals several areas for improving the scalability, responsiveness, and reliability of modern data warehouse designs, including effective query execution techniques, optimized storage structures, and resource management strategies. The results provide practical recommendations for practitioners and researchers related to the best optimization strategies for analysis settings.