Welcome to Pathway Developer Documentation!

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It’s the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data.

Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway syntax is simple and intuitive, and you can use the same code for both batch and streaming processing.

Pathway is powered by a scalable Rust engine based on Differential Dataflow and performing incremental computation. Your Pathway code, despite being written in Python, is run by the engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes.

You can install Pathway with a simple pip command:

            pip install -U pathway
Pathway code example.

Use cases

Learn more

Key concepts

Learn more about how Pathway's engine and what makes it powerful.

Read more
API docs

Not sure how to use a specific feature of Pathway? The answer to your question is likely in the API docs.

See the API docs

Learn how to use Pathway with our tutorials. For beginners and veterans alike, they cover most of Pathway's features.

See the tutorials
See the repo

Curious about how Pathway works? Don't hesitate to take a look at the sources and clone the repo.

Go to Github

Self-host your Pathway service with Docker, Kubernetes, or quickly launch a hosted container.

Deploy in one click
What's new?

See the latest available features in the Changelog.

See the Changelog