Optimizing Databricks Spark Pipelines Using Declarative Patterns (opens in new tab)

If you've ever inherited a Spark job that runs in 35 minutes and someone asks you to make it faster, you know the routine. You start by checking partition counts, then file sizes, then shuffle stages, then broadcast hints. You find a handwritten OPTIMIZE schedule from 2022, a Z-ORDER on the wrong column, and a cluster sized for last year's data volume. By the time you've made the job fast, you've absorbed three new things to maintain. The next person to inherit it will absorb four. This patte...