Guide To Supporting On-Premise Spark Deployments With A Cloud-Scale Data Platform
This guide explores how enterprises can deploy Apache Spark on-premise with a cloud-scale data platform to avoid the cost and control limitations of public cloud infrastructure. It outlines challenges in traditional Hadoop-based storage and presents architectural recommendations for better performance, scalability, and data management. The paper advocates for flexible, efficient storage systems that can support Spark’s in-memory processing demands, enabling IT teams to deliver high-performance analytics at scale while maintaining cost-efficiency and governance.