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This paper tries to reduce the effort of learning, deploying, and integratingseveral frameworks for the development of e-Science applications that combinesimulations with High-Performance Data Analytics (HPDA). We propose a way toextend task-based management systems to support continuous input and outputdata to enable the combination of task-based workflows and dataflows (HybridWorkflows from now on) using a single programming model. Hence, developers canbuild complex Data Science workflows with different approaches depending on therequirements. To illustrate the capabilities of Hybrid Workflows, we have builta Distributed Stream Library and a fully functional prototype extending COMPSs,a mature, general-purpose, task-based, parallel programming model. The librarycan be easily integrated with existing task-based frameworks to provide supportfor dataflows. Also, it provides a homogeneous, generic, and simplerepresentation of object and file streams in both Java and Python; enablingcomplex workflows to handle any data type without dealing directly with thestreaming back-end.

A Programming Model for Hybrid Workflows: combining Task-based Workflows and Dataflows all-in-one