Thought-provoking post on refactoring data rather than code https://jreyesr.github.io/posts/data-refactorings/ - does anyone know of a Fowler-style catalog of data refactorings? (Note: not looking for filter, select, group by, and other operations on data, but on equivalent of "extract method" or "introduce base class".)
Refactoring data as if it were code: a case for extending refactoring to static data - jreyesr's blog

In this article we extend the concept of refactorings, as used on source code, to data stored, for example, on YAML or JSON files. As an example, we extend the typical Rename Variable action to renaming a Kubernetes resource. We explore several scenarios that seem like a data-focused variant of code refactorings, why they may be useful. Then, we review some ways in which those data refactorings may be implemented, which tools could support them, and how the user experience may be like.

@gvwilson Interesting post! It seems to be mostly focused on OOP / mapping formats, however.

I'm not aware of a catalog of data refactorings (besides DB de-/normalization), but something I've used in my heterogeneous pipelines is a data +representations approach. Namely, as a given data structure is appropriate for certain types of processing, the aim was to expose different representations of the same underlying data, depending on what's consuming it (e.g. tabular for relational, mappings for non-relation, algebraic (matrices and tensors) for numerical computing, small files for certain specialized workflows, etc.). So basically conversions, but a bit more principled.

Some representations are more redundant than others, so from the post's POV, I suppose a 'base class' would be a repr that is more compressed? Or with semantic structure? From this, other repr methods would generate a given format+structure (e.g. YAML) needed by a consumer. On the semantic side, I suppose one could look at all the operations specific to RDF, OWL, or other semantic web formalisms.