Apache Airflow
Data workflow orchestration platform
Python
Versatile language for web, data and AI with clear syntax
Typing: dynamic Created: 1991
Paradigms
object-orientedfunctionalprocedural
Pros and Cons
Ventajas
- + Clear and easy to learn syntax
- + Huge library ecosystem
- + Dominant in Data Science and ML
- + Excellent for rapid prototyping
- + Very active community
Desventajas
- - Slower than compiled languages
- - GIL limits real multithreading
- - Dependency management can be complex
- - Not ideal for native mobile
Casos de Uso
- APIs and web backends
- Machine Learning and AI
- Data analysis
- Automation and scripting
- Scientific applications
Frameworks and Libraries (26)
BentoML
Simplified model serving framework
Dagster
Modern data orchestrator with assets-first approach
Dash
Python framework for analytical web applications
Django
Python 'batteries included' web framework
fast.ai
High-level deep learning framework
FastAPI
Modern, fast Python framework with types
Flask
Simple and extensible Python microframework
Gradio
Framework to create user interfaces for ML models
Kedro
ML pipeline framework with engineering best practices
LangChain
Framework for applications with LLMs
Litestar
Modern high-performance ASGI framework (formerly Starlite)
MLflow
Open-source platform for ML lifecycle
NVIDIA Omniverse
3D simulation and collaboration platform
OpenCV
Computer vision and image processing library
Playwright
Cross-browser E2E framework by Microsoft
Prefect
Modern workflow automation platform
Pygame
Python library for 2D game development
PyTorch
Deep learning framework preferred in research
Quart
Async framework compatible with Flask API
Ray
Distributed computing framework for ML and Python
Sanic
Ultra-fast async web framework for Python
Streamlit
Python framework to quickly create data science apps
TensorFlow
Google's ML framework for production
gRPC
High-performance RPC framework from Google
Temporal
Durable execution platform for workflows