clean_pipeline
Apply a sequence of cleaning operations in order to create a new clean dataset, with optional materialization for faster downstream queries.
Instructions
Apply a sequence of cleaning ops in order. Returns one new source.
`ops` is a list of `{"kind": str, "args": dict}` (the same shape that
`auto_clean` produces). Each op runs sequentially against the previous
stage's output. Final stage gets `alias` (default `{source}_clean`).
`materialize=True` (default) collapses the final result into a real
DuckDB TABLE so downstream queries (run_sql, auto_modeling_audit,
suggest_plots, etc.) don't have to re-evaluate the whole filter/
replace chain. Turn off for short-lived exploratory cleanups where
you won't query the output much.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ops | Yes | ||
| alias | No | ||
| source_id | Yes | ||
| materialize | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||