Semantic Extract, Transform, and Load-er (SETLr) is a flexible, scalable tool for providing semantic interpretations to tabular, XML, and JSON-based data from local or web files. It has been used by diverse projects and has shown to be scalable and flexible, allowing for the simplified creation of arbitrary RDF, including ontologies and nanopublications, from many different data formats. Semantic ETL scripts use best practice standards for provenance (PROV-O) and support streaming conversion for RDF transformation using the JSON-LD based templating language, JSLDT. SETLr also supports custom Python functions for remote APIs, entity resolution, external data lookup, or other tasks. We provide case studies for dynamic SETL scripts, ontology generation, and scaling to gigabytes of input and discuss the value and impact of this approach.