4 documented Databricks SQL cost inefficiencies, each with how to detect it and how to fix it.
Databricks SQLCER-0225
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...
Databricks SQLCER-0220
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...
Databricks SQLCER-0521
A Databricks SQL warehouse's cluster size sets the driver instance and the number of workers in each of its clusters, from 2X-Small with one worker up to 5X-Large with 512, doubling at each step. The warehouse bills for that size for every...
Databricks SQLCER-0522
A Databricks SQL warehouse keeps running after its last query until its Auto Stop timer expires, and Databricks states that idle SQL warehouses continue to accumulate DBU and cloud instance charges until they are stopped. For typical use...