Data Warehouse Performance Tuning Using Materialized Views

Abstract

Data warehouse performance tuning using materialized views is important because enterprise reporting systems often process large historical datasets, complex joins, and repeated aggregation queries. Materialized views store precomputed query results, allowing analytical reports and dashboards to retrieve summarized or joined data faster without recalculating results from base tables each time. Existing literature highlights query rewrite, aggregate precomputation, refresh scheduling, indexing, partition alignment, and workload-based view selection as major techniques for improving warehouse performance. However, many organizations still face challenges such as slow dashboard response, high query execution cost, delayed report generation, refresh overhead, storage consumption, and difficulty maintaining consistency between base tables and materialized views. This research is important because decision support systems require fast, reliable, and scalable access to analytical data for daily business monitoring. This article discusses data warehouse performance tuning using materialized views, focusing on view design, query pattern analysis, aggregation strategy, refresh methods, indexing support, storage trade-offs, and performance evaluation. The study concludes that effective use of materialized views improves query response time, reduces processing load, supports faster reporting, and strengthens data warehouse performance for enterprise analytics.