  This paper tries to reduce the effort of learning, deploying, and integrating
several frameworks for the development of e-Science applications that combine
simulations with High-Performance Data Analytics (HPDA). We propose a way to
extend task-based management systems to support continuous input and output
data to enable the combination of task-based workflows and dataflows (Hybrid
Workflows from now on) using a single programming model. Hence, developers can
build complex Data Science workflows with different approaches depending on the
requirements. To illustrate the capabilities of Hybrid Workflows, we have built
a Distributed Stream Library and a fully functional prototype extending COMPSs,
a mature, general-purpose, task-based, parallel programming model. The library
can be easily integrated with existing task-based frameworks to provide support
for dataflows. Also, it provides a homogeneous, generic, and simple
representation of object and file streams in both Java and Python; enabling
complex workflows to handle any data type without dealing directly with the
streaming back-end.
