Case Study: Cost-Aware Auto-Scaling for Real-Time ETL on Kubernetes
Abstract
A media platform deployed predictive HPA with queue depth signals for real-time ETL, cutting compute cost 18% without breaching freshness SLOs.
Cite this article
Robinson, A. (2024). Case Study: Cost-Aware Auto-Scaling for Real-Time ETL on Kubernetes. Research Explorations in Global Knowledge & Technology (REGKT), 3 (1). Retrieved from https://regkt.com/article.php?id=234&slug=case-study-cost-aware-autoscaling-realtime-etl-kubernetes