Understanding Parallelism and Performance in Databricks PySpark

Efficient PySpark performance in Databricks depends on correctly balancing executors, cores, and partitions. This guide walks through calculating parallel tasks, tuning partitions for optimal utilization, and shows a 10-node real-world example where balanced partitioning cut runtime from 25 to 10 minutes. By aligning partitions to available cores and monitoring Spark UI, teams can drastically boost throughput and cost efficiency without over-provisioning resources.