Pathway
Pathway offers a robust data processing framework designed for Python and AI developers, featuring real-time machine learning model integration, API connectivity, and high scalability.
Services
Pathway specializes in providing a high-performance data processing framework suited for Python and AI developers. This framework efficiently handles streaming data and supports real-time ML model and API integration, facilitating various AI use cases. Pathway's capabilities extend from batch processing to live data processing, ensuring a robust solution for both SQL and ML/AI applications. The services also encompass real-time request serving, feature store capabilities, and advanced query APIs, all aimed at enhancing the user's ability to manage and analyze data effectively.
Technology Stack
Pathway's engine is powered by Rust, providing an interactive development experience with Python. The native stack relies on the Java Virtual Machine, supporting Java and Scala code. The Python API is constructed around Table objects analogous to Spark RDD or pandas DataFrame, allowing seamless data manipulation. Additionally, the framework includes an SQL API, enabling developers and data analysts to apply their existing SQL skills to real-time streaming data. Deployment processes are streamlined through tests, CI/CD, and containerized deployment with Kubernetes/Docker.
Key Features
Pathway offers a suite of powerful transformations, emphasizing speed and scalability. Its framework is capable of processing millions of data points per second and scaling across multiple workers while maintaining consistency and predictability. Users benefit from tools that support interactive development with Jupyter notebooks and robust data experimentation. The framework ensures streaming consistency, complying with internal consistency protocols, and supporting real-time feature store functionalities for enhanced data management capabilities.
Use Cases
Pathway addresses a broad spectrum of use cases ranging from classical streaming to data indexing for knowledge management. The framework's flexibility supports a variety of scenarios, including real-time ML model deployment, advanced data querying, and real-time API integration. Its architecture is designed to handle both batch and streaming data efficiently, providing a consistent and predictable performance essential for SQL and ML/AI applications.