In many Databricks environments, large Delta tables are created without enabling standard optimization features like partitioning and Z-Ordering. Without these, queries scanning large datasets may read far more data than necessary, increasing execution time and compute usage. * **Partitioning** organizes data by a specified column to reduce scan scope. * **Z-Ordering** optimizes file sorting to minimize I/O during range queries or filters. * **Delta Format** enables additional optimizations like data skipping and compaction. Failing to use these features in high-volume tables often results in avoidable performance overhead and elevated spend, especially in environments with frequent exploratory queries or BI workloads.
Databricks charges are based on DBUs (Databricks Units) per hour, which correlate directly with compute resource use. Query performance heavily impacts DBU consumption. Inefficient data layout leads to longer scan times, increased cluster runtime, and higher costs.
