Many organizations continue running short-lived or low-intensity SQL workloads — such as dashboards, exploratory queries, and BI tool integrations — on traditional clusters. This leads to idle compute, overprovisioning, and high baseline costs, especially when the clusters are always-on. Databricks SQL Serverless is optimized for bursty, interactive use cases with auto-scaling and pay-per-second pricing, making it better suited for this class of workloads. Failing to migrate to serverless for these patterns results in unnecessary cost without performance benefit.

Many Spark and SQL workloads in Databricks suffer from micro-optimization issues — such as unfiltered joins, unnecessary shuffles, missing broadcast joins, and repeated scans of uncached data. These problems increase compute time and resource utilization, especially in exploratory or development environments. Disabling Adaptive Query Execution (AQE) can further exacerbate inefficiencies. Optimizing queries reduces DBU costs, improves cluster performance, and enhances user experience.