Pyper is a flexible framework for concurrent and parallel data-processing, based on functional programming patterns. Used for 🔀 ETL Systems, ⚙️ Data Microservices, and 🌐 Data Collection
See the <a href="https://pyper-dev.github.io/pyper/" target="_blank" rel="noopener nofollow">Documentation</a>
Key features:
💡Intuitive API: Easy to learn, easy to think about. Implements clean abstractions to seamlessly unify threaded, multiprocessed, and asynchronous work.
🚀 Functional Paradigm: Python functions are the building blocks of data pipelines. Let’s you write clean, reusable code naturally.
🛡️ Safety: Hides the heavy lifting of underlying task execution and resource clean-up. No more worrying about race conditions, memory leaks, or thread-level error handling.
⚡ Efficiency: Designed from the ground up for lazy execution, using queues, workers, and generators.
✨ Pure Python: Lightweight, with zero sub-dependencies.

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Pyper is a flexible, pure-Python framework designed for concurrent and parallel data processing. It features an intuitive API that unifies threaded, multiprocessed, and asynchronous work using functional programming principles. Pyper ensures safety by managing underlying task execution and resource clean-up, and it is optimized for efficiency with lazy execution through queues, workers, and generators.

Pyper - Concurrent Python Made Simple