Business Intelligence dashboards and ad-hoc analyst queries frequently drive Databricks compute usage — especially when: * Dashboards are auto-refreshed too frequently * Queries scan full datasets instead of leveraging filtered views or materialized tables * Inefficient joins or large broadcast operations are used * Redundant or exploratory queries are triggered during interactive exploration This often results in clusters staying active for longer than necessary, or being autoscaled up to handle inefficient workloads, leading to unnecessary DBU consumption.