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pathway

Python ETL framework for stream processing, real-time analytics, and RAG.

Pathway Live Data Framework

Python ETL framework for real-time analytics, LLM pipelines, and RAG.

Free forever · source-available · Licence not stated in the repository
✓ Needs IT✓ Browser-based✓ Real-time collaboration
7.8
ZEKAI
Rust-powered Python ETL for RAG
Free & open source · Marketing
Ease of setup4
Usefulness8
Maintenance9.5
Community9.8
★ 62,013
GITHUB
Nov 2026
UPDATED
Licence not stated in the repository
LICENSE
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⚡ Quick answer

Pathway is a Python ETL framework powered by a scalable Rust engine for stream processing and real-time analytics. It suits professionals building live LLM applications and RAG pipelines using Python.

Runs on
Cross-platform
Hosting
Self-host
Languages
Python
License
Licence not stated in the repository

What it is

It executes Python data pipelines over batch and streaming data using a Differential Dataflow Rust engine. It includes connectors for Kafka, PostgreSQL, and GDrive, alongside specialized LLM helpers for real-time Vector Indexes.

Who it's for

It suits developers building live data feeds and RAG systems who want to code in Python while retaining Rust performance. It does not suit teams looking for zero-code UI pipeline builders.

Key features

Scalable Rust Engine

Executes Python code via a Differential Dataflow Rust engine for multithreading and distributed computations.

Unified Batch and Stream

Processes batch jobs, stream replays, and data streams using the exact same codebase.

LLM and RAG Tooling

Provides wrappers, parsers, embedders, and an in-memory real-time Vector Index for live document RAG.

Extensive Connectors

Connects to Kafka, GDrive, PostgreSQL, SharePoint, and over 300 data sources via Airbyte.

How to start

Up and running in 3 steps

1
Install the framework using pip install -U pathway on a supported MacOS or Linux system.
2
Write your data pipeline script importing pathway as pw, defining inputs and operations.
3
Run your script locally with python or deploy it inside a Docker container.
Real talk

Our honest verdict

What's great

  • Same code base handles both local development, batch jobs, and real-time streams
  • Built-in LLM extension includes embedders, splitters, and real-time vector indexes
  • Handles out-of-order and late data points with automatic consistency updates
  • Supports Airbyte integration for connecting to over 300 external data sources

Where it falls short

  • Only available on MacOS and Linux natively, requiring a Virtual Machine for other operating systems
  • Free version provides 'at least once' consistency while 'exactly once' requires an enterprise version
  • Requires technical skill to set up Python environments, dependencies, and container deployment
Cost

Pricing & license

The repository does not state a licence name in its metadata, and the README mentions free and enterprise versions with differing consistency guarantees.

Paid work

Open remote AI-training roles

879 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.

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Questions

FAQ

What operating systems are supported?

Pathway is officially available on MacOS and Linux. Users of other systems must run it inside a Virtual Machine.

Does it support both batch and streaming?

Yes, the framework uses a unified engine allowing the same code to handle batch jobs, stream replays, and real-time streams.

How does it connect to external sources?

It includes built-in connectors for Kafka, GDrive, PostgreSQL, and SharePoint, plus an Airbyte connector for over 300 additional sources.

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