Denormalization techniques for IOT data warehouses: balancing query performance and data redundancy

This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially increases storage needs by 16% (120 GB vs. 103.5 GB), but columnar compression reduces the final storage size by 50.4% (17.1 GB vs. 34.5 GB). The study provides practical insights into balancing query performance and data redundancy in high-speed IoT environments.