Two branch archives workspaces both run near-identical Apache Airflow DAGs for similar workloads, but one branch's DAG runs consistently finish much faster than the other's. An engineer suspects the difference comes from how each workspace's Apache Airflow settings are configured. What should be compared?
Select an answer to reveal the explanation.
Short Explanation
Airflow's environment size setting isn't a network-wide dial -- it's set separately for each individual workspace, so two branches running nearly identical DAGs can still end up on very different engine sizes. Comparing that one setting side by side is the fastest way to spot the mismatch.
Full Explanation
The Apache Airflow environment size is a per-workspace setting that determines the compute backing that workspace's DAG runs, so two workspaces running similar DAGs can have meaningfully different performance simply because one is configured with a larger environment size than the other -- that's the setting to compare first. Domain assignment groups workspaces for governance and policy purposes; it has no effect on the compute resources backing Airflow DAG execution. Spark pool autoscale settings govern Spark session compute for notebooks and Spark jobs, an entirely separate compute path from the dedicated environment that runs Airflow DAGs. OneLake shortcut caching affects data-read latency for shortcut-linked data, not the compute available to execute DAG tasks. A caveat: even after matching environment sizes, other factors like DAG complexity or external system latency could still cause differences, so environment size is a first check, not a guaranteed full explanation. Confirm the diagnosis by checking each workspace's Apache Airflow settings and comparing the selected environment sizes directly.