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Databricks Certified Data Engineer Professional · Databricks
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For wide transformations (shuffles), you want: More executors = Better shuffle parallelism More network interfaces = Higher aggregate bandwidth Distributed I/O = Parallel shuffle reads/writes. 16x network bandwidth: Shuffle data distributed across 16 network interfaces ✅ True parallelism: 16 nodes simultaneously reading/writing shuffle data ✅ Better fault tolerance: Losing 1 executor = only 6% of work lost vs 100% ✅ Reasonable cores/executor: 10 cores (not ideal, but better than 160!)
suretly B
merge will work if no match