Deep Learning Techniques for Predicting Data Warehouse Performance Bottlenecks

Abstract

Modern organizations rely heavily on data warehouses to support business intelligence, reporting, and advanced analytics. As data volumes, user concurrency, and query complexity grow, maintaining consistent performance becomes increasingly difficult. Traditional performance monitoring approaches—based on threshold rules, manual tuning, and periodic reports—are often reactive, identifying bottlenecks only after system degradation has already impacted users. This paper explores deep learning–based techniques for proactively predicting performance bottlenecks in data warehouses, such as slow-running queries, resource saturation (CPU, memory, I/O), and contention on key tables or indexes. We propose a framework that collects rich operational telemetry (query logs, execution plans, resource metrics, workload characteristics), transforms it into feature representations, and trains deep learning models (LSTM, CNN, hybrid models, and autoencoders) to forecast performance anomalies before they occur. A detailed methodology is presented, including data preprocessing, feature engineering, model architectures, training strategies, and evaluation metrics. A case study on a simulated enterprise data warehouse workload demonstrates how the proposed deep learning models can predict potential bottlenecks with high accuracy, enabling proactive scaling, workload reshaping, or query optimization. The results highlight that deep learning techniques significantly outperform traditional rule-based and simple statistical approaches, especially under complex, highly concurrent workloads.