Predictive Capacity Management in Teradata: AI-Driven Forecasting and Performance Optimization for Enterprise Data Warehouses

Abstract

As more and more businesses rely on their Teradata for in-depth analytics, keeping an eye on the capacity of their data warehouses has become a big worry. Traditional static methods don't always function well with the changing nature of data growth, workload changes & changing their business needs. This article talks about a way to use AI to manage predictive capacity in Teradata. It focuses on their anticipatory forecasting & how they use their resources strategically. The suggested method uses ML models to look at how much storage, workload & query their performance have changed over time to make accurate guesses about how much capacity they will need in the future. It lets companies detect infrastructure problems before they happen, change how much storage & computing power they need on the go & keep the system running at its best without providing too much. The research uses anomaly detection to find many sudden spikes in workload & these idle resources. This ensures the system works well & expenses the least amount of money. Experimental results from enterprise-scale datasets demonstrate these significant enhancements in workload predictability, query response times & system throughput, yielding up to a 25% increase in their resource utilization and a 30% reduction in operating expenses compared to conventional threshold-based methodologies. The design makes sure that IT infrastructure is in line with actual business needs while also making sure that it can grow & be more reliable. This AI-powered strategy for managing these predictive capacity converts Teradata settings into these self-optimizing ecosystems. These ecosystems maintain their performance & efficiency high even as data expands and business analytics demands change