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.

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Install Pathway using `pip`:

```bash
pip install pathway
```

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Try Our Templates

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Pathway offers ready-to-go templates for RAG and ETL pipelines.
Run Pathway on your data in minutes.
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Key Features:

Easy-to-use Python API: Pathway is fully compatible with Python. Use your favorite Python tools and ML libraries.
Scalable Rust engine: your Python code is run by a powerful Rust engine with multithreading and multiprocessing. No JVM and no GIL!
Stateful operations: use stateful and temporal operations such as groupby and windows.
Incremental computations: using Differential Dataflow, Pathway takes care of out-of-order data points for you, in real time.
Batch and streaming alike: use the same pipeline on static data and live data streams.
In-memory data processing: real-time updates, reduced latency, and higher throughput.
Easy to deploy with Docker or Kubernetes. Pathway comes with an orchestrator and is fully compatible with OpenTelemetry.
Exactly once consistency: obtain the same results in both batch and streaming.
Persistence and backfilling: save the state of the computation to quickly resume after a failure or a pipeline update.
LLM tooling: online ML, RAG pipelines, vector indexes... With Pathway, your ML pipeline works on fresh data.
Connect to any data source: Pathway comes with 350+ connectors, including SharePoint. Or implement your own.

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What's next?

GitHub repository

Pathway sources are available on GitHub. Don't hesitate to clone the repo and contribute!

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License key

Some features of Pathway such as monitoring or advanced connectors (e.g., SharePoint) require a free license key. To obtain a free license key, you need to register here.