Transforms

Between the source and the destination, a flow can reshape what it read. Every stage is optional, and every stage is shown to you before it runs.

Typed columns, proposed for you

SnoutData reads a sample and proposes the columns and their types, with a reason for each. You review the proposal and change anything you disagree with: a column's name, its type, whether it is included at all.

Where a record contains a repeating group (a list of line items inside an order, for example) and the parent has a key, that group can become a child table with a foreign key back to the parent, instead of being flattened or stringified.

Map, cast, derive

Rename a column, change its type, split one column into several or combine several into one, and derive new columns from the ones you already have.

Filter

Keep only the rows that matter. Rows that do not match are not written.

Dedupe

Drop duplicate rows, on the columns you choose rather than on the whole record, so "the same customer twice" works even when a timestamp differs.

Redact

Personal data redaction is a stage of its own, which means the sensitive columns are removed before the data reaches the destination, not after it arrives.

This matters most when the destination is somewhere you would rather not put personal data at all, such as a file you are going to share or a fine-tuning set.