Databricks benchmark finds early pipeline checks reduce wasted compute

Databricks says its benchmark of agent-written data pipelines used 17 executor-seconds with Spark Declarative Pipelines, compared with 596 for procedural PySpark. The test covered 22 tasks and 264 builds per approach, all using Opus 4.8. Declarative pipelines describe the datasets to produce, allowing Spark to check structural errors before running the work. Completed builds had a median of 64 lines of code, compared with 131 for procedural pipelines. The approach used 2.3 times more model tokens. It also offered no meaningful advantage on silent logic errors: a pipeline could pass structural checks and still return the wrong answer, leaving tests and review necessary.