Description
Scale Python data and computation workflows across parallel tasks. It helps data science and scientific projects process larger datasets or heavier computations by scheduling work across threads, processes, or clusters.
This is a computing framework, not a spreadsheet or visual app. Parallel execution can increase memory, CPU, and network use, so workloads should be monitored and tested before running on shared systems or expensive clusters.