Dynamic Scaling Strategies in Cloud Data Warehousing: Balancing Cost and Performance

EasyChair Preprint 15879

Abstract

Cloud data warehousing has become a crucial component of modern analytics, enabling enterprises to store, manage, and process vast amounts of data. However, achieving a balance between performance and cost remains a challenge due to fluctuating workloads and unpredictable resource demands. Dynamic scaling strategies provide an effective solution by adjusting computational and storage resources in real-time based on workload requirements. This article explores various dynamic scaling strategies such as auto-scaling, workload-aware scaling, predictive scaling, and multi-cluster scaling. It also examines the challenges associated with dynamic scaling and presents best practices for achieving optimal cost-performance balance. The adoption of AI-driven scaling mechanisms and cloud-native tools has further enhanced the ability to optimize cloud data warehouse environments, ensuring high efficiency and cost control.

Keyphrases: Auto-scaling, Cloud Data Warehousing, Dynamic Scaling, Elasticity, Multi-Cluster Architectures, Predictive Scaling, cost optimization, query performance

@booklet{EasyChair:15879,
  author    = {Holmes Walter},
  title     = {Dynamic Scaling Strategies in Cloud Data Warehousing:  Balancing Cost and Performance},
  howpublished = {EasyChair Preprint 15879},
  year      = {EasyChair, 2025}}