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We present Pathway, a new unified data processing framework that can runworkloads on both bounded and unbounded data streams. The framework was createdwith the original motivation of resolving challenges faced when analyzing andprocessing data from the physical economy, including streams of data generatedby IoT and enterprise systems. These required rapid reaction while calling forthe application of advanced computation paradigms (machinelearning-poweredanalytics, contextual analysis, and other elements of complex eventprocessing). Pathway is equipped with a Table API tailored for Python andPython/SQL workflows, and is powered by a distributed incremental dataflow inRust. We describe the system and present benchmarking results which demonstrateits capabilities in both batch and streaming contexts, where it is able tosurpass state-of-the-art industry frameworks in both scenarios. We also discussstreaming use cases handled by Pathway which cannot be easily resolved withstate-of-the-art industry frameworks, such as streaming iterative graphalgorithms (PageRank, etc.).

Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications