Pathway: Live Data Framework
What is the project about?
Pathway is a Python ETL framework designed for stream processing, real-time analytics, LLM pipelines, and Retrieval-Augmented Generation (RAG). It simplifies building data-intensive applications that react to live data changes.
What problem does it solve?
Pathway addresses the complexity of building and deploying real-time data pipelines. It unifies batch and stream processing, allowing developers to use the same code for both, and simplifies the integration of machine learning and LLMs into data workflows. It eliminates the need to switch between different tools for batch and streaming, and handles the complexities of state management, consistency, and scalability.
What are the features of the project?
- Wide range of connectors: Connects to various data sources (Kafka, GDrive, PostgreSQL, SharePoint, Airbyte, and custom connectors).
- Stateless and stateful transformations: Supports operations like joins, windowing, sorting, and custom Python functions.
- Persistence: Saves the state of computations for pipeline restarts after updates or crashes.
- Consistency: Manages time and ensures consistent computations, handling late and out-of-order data. Offers "at least once" consistency (free version) and "exactly once" (enterprise version).
- Scalable Rust engine: Enables multithreading, multiprocessing, and distributed computations, overcoming Python's limitations.
- LLM helpers: Provides tools for integrating LLMs (wrappers, parsers, embedders, splitters, real-time Vector Index, LlamaIndex/LangChain integrations).
- Unified Batch and Stream Processing: The same code works for both batch and streaming data.
What are the technologies used in the project?
- Python: Primary API for defining data pipelines.
- Rust: Underlying engine for scalable and efficient computation (based on Differential Dataflow).
- Docker: For containerization and deployment.
- Kubernetes: Supported for cloud deployment and scaling (especially in the Enterprise version).
- LLM Tooling: Wrappers for common LLM services.
What are the benefits of the project?
- Simplified Development: Easy-to-use Python API.
- Unified Processing: Handles both batch and streaming data with the same code.
- Scalability: Rust engine allows for high performance and scaling.
- Real-time Capabilities: Enables building applications that react to live data.
- Easy LLM Integration: Streamlines the development of LLM and RAG pipelines.
- Easy Deployment: Docker and Kubernetes support.
- Consistency and Reliability: Handles time, late data, and provides persistence.
What are the use cases of the project?
- Real-time ETL: Extract, transform, and load data from various sources in real-time.
- Event-driven pipelines with alerting: Process events and trigger alerts based on specific conditions.
- Real-time analytics: Perform analytics on live data streams.
- LLM Pipelines: Build live LLM and RAG applications.
- Unstructured data to SQL on-the-fly: Convert unstructured data into structured SQL queries.
- Private RAG: Create RAG applications with private data.
- Adaptive RAG: Build RAG systems that adapt to changing data.
- Multimodal RAG: Combine different data types (text, images, etc.) in RAG applications.
- Any data processing pipeline requiring real-time updates and/or integration with machine learning models.
