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 Pathway Live Data Framework

Pathway Live Data Framework is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Pathway Live Data Framework comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries.
Pathway Live Data Framework code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively.
The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams.

Pathway Live Data Framework is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation.
Your Pathway Live Data Framework code, despite being written in Python, is run by the Rust 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 Live Data Framework with pip:

pip install -U pathway


For any questions, you will find the community and team behind the project on Discord.

 Use-cases and templates

Ready to see what Pathway Live Data Framework can do?

Try one of our easy-to-run examples!

Available in both notebook and docker formats, these ready-to-launch examples can be launched in just a few clicks. Pick one and start your hands-on experience with Pathway Live Data Framework today!

 Event processing and real-time analytics pipelines
With its unified engine for batch and streaming and its full Python compatibility, Pathway Live Data Framework  makes data processing as easy as possible. It's the ideal solution for a wide range of data processing pipelines, including:

- Showcase: Real-time ETL.
- Showcase: Event-driven pipelines with alerting.
- Showcase: Realtime analytics.
- Docs: Switch from batch to streaming.



 AI Pipelines

Pathway Live Data Framework provides dedicated LLM tooling to build live LLM and RAG pipelines. Wrappers for most common LLM services and utilities are included, making working with LLMs and RAGs pipelines incredibly easy. Check out our LLM xpack documentation.

Don't hesitate to try one of our runnable examples featuring LLM tooling.
You can find such examples here.

  - Template: Unstructured data to SQL on-the-fly.
  - Template: Private RAG with Ollama and Mistral AI
  - Template: Adaptive RAG
  - Template: Multimodal RAG with gpt-4o

 Features

- A wide range of connectors: Pathway Live Data Framework comes with connectors that connect to external data sources such as Kafka, GDrive, PostgreSQL, or SharePoint. Its Airbyte connector allows you to connect to more than 300 different data sources. If the connector you want is not available, you can build your own custom connector using Pathway Live Data Framework Python connector.
- Stateless and s

Top issues

Latest release

Added
- pw.io.chroma.write writes a Pathway table to a Chroma collection, keeping the collection in sync with the table as rows are added, changed, and removed. The columns are mapped onto Chroma's record fields explicitly: the optional primary_key becomes the record id (when omitted, the row's internal Pathway key is used instead), embedding the vector, the optional document column the stored text, and metadata_columns the record metadata. The collection must already exist. The server is addressed with host/port (plus ssl, headers, tenant, and database for authenticated deployments such as C