Python ETL framework for stream processing, real-time analytics, and RAG.
Python ETL framework for real-time analytics, LLM pipelines, and RAG.
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.
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.
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.
Executes Python code via a Differential Dataflow Rust engine for multithreading and distributed computations.
Processes batch jobs, stream replays, and data streams using the exact same codebase.
Provides wrappers, parsers, embedders, and an in-memory real-time Vector Index for live document RAG.
Connects to Kafka, GDrive, PostgreSQL, SharePoint, and over 300 data sources via Airbyte.
The repository does not state a licence name in its metadata, and the README mentions free and enterprise versions with differing consistency guarantees.
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.
Pathway is officially available on MacOS and Linux. Users of other systems must run it inside a Virtual Machine.
Yes, the framework uses a unified engine allowing the same code to handle batch jobs, stream replays, and real-time streams.
It includes built-in connectors for Kafka, GDrive, PostgreSQL, and SharePoint, plus an Airbyte connector for over 300 additional sources.
See Zekai first in Google