This article walks us through the process of how to migrate traditional workloads using Classic Compute to Serverless Compute for efficient cluster management, cost effectiveness, better scalability and optimized performance.
Overview
As data engineering evolves, so do the infrastructure needs of enterprise workloads. With growing demands for agility, scalability, and cost-efficiency, Databricks Serverless Compute provides a compelling alternative to classic clusters. In this article, we explore a practical roadmap to migrate your pipelines and analytics workloads from classic compute (manual clusters or job clusters) to Databricks Serverless Compute, with specific attention to data security, scheduling, costs, and operational resilience.