Pathway is a high-performance live data processing framework built on top of timely dataflow and differential dataflow, providing a Python API for defining streaming and batch data pipelines. It is designed for real-time ETL, event processing, AI/ML pipelines (including RAG), and incremental computation with strong consistency guarantees. The architecture consists of a Rust core engine that handles dataflow execution, scheduling, persistence, and connectors, while the Python layer provides a declarative API for table operations, UDFs, and integrations with external systems like Kafka, databases, vector stores, and cloud storage. Key entry points include the `pathway` CLI, the Python `pw` module, and the Rust engine library. Data flows through connectors (sources/sinks), transformation operators (joins, groupby, windowing, etc.), and optional persistence for fault tolerance. External dependencies include Kafka, S3, PostgreSQL, various vector databases, and LLM services.
Apache Beam is a unified programming model for defining both batch and streaming data-parallel processing pipelines. The codebase is the primary implementation of this model, providing SDKs in Java, Python, Go, TypeScript, and Kotlin, along with multiple runner backends (Direct, Flink, Spark, Dataflow, Jet, Twister2, Kafka Streams, Prism) and a vast ecosystem of I/O connectors for nearly every major data system (GCP, AWS, Azure, Kafka, JDBC, MongoDB, Snowflake, etc.). The repository also includes SQL extensions, ML inference transforms, cross-language execution support, a performance benchmark suite (Nexmark, TPC-DS), and extensive integration test infrastructure. Beyond the core framework, it hosts examples, learning katas, a web-based playground, and CI/CD automation for self-hosted runners. While primarily an application framework, it includes data/research elements such as benchmarking queries and synthetic data generators, but the dominant character is that of a production-grade distributed data processing library.