Interactive notebooks, such as provided by the Jupyter platform [2], are gaining traction in scientific computing, data science, and machine learning. Developing a Jupyter kernel machinery for a new language, however, requires considerable effort. In this extended abstract, we present Bacatá, a language-parametric bridge between Jupyter and the Rascal language workbench [3]. Reusing existing language components, such as a parsers, interpreters, Read-Eval-Print Loop (REPLs) and autocomplete, Bacatá generates a Jupyter kernel machinery so that the DSL can be used in notebook form. We sketch the architecture of Bacatá and demonstrate it in action using a DSL for image processing, called Amalga.