Distributed Tools & Datasets for Python Data Engineering

Python API for Apache Spark, enabling scalable and efficient data processing. Particularly useful for ETL processes involving large datasets that need parallel processing across a cluster.

Platform to programmatically author, schedule, and monitor workflows. Allows for complex pipeline construction and efficient task management with robust dependency handling.

Open-source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Designed for large-scale computational tasks with powerful workflow features.

Parallel computing library that scales Pandas workflows to larger-than-memory datasets. Enables parallel processing while maintaining a familiar Pandas-like interface for big data.