A data transformation pipeline takes one or more inputs and produces a structured output for analysis or another system. Steps can normalize formats, combine sources, calculate fields, classify records, remove invalid entries, and check expected constraints.
Recurring pipelines need deterministic handling of known cases and visible treatment of uncertainty. They should preserve source provenance, prevent incomplete reads from appearing complete, and make failed runs safe to retry without losing prior good data.
ELI5
A data transformation pipeline moves data through a repeatable series of changes so another system can use it. The steps may clean errors, join sources, rename fields, calculate values and check the final structure.
For example, a pipeline can combine daily sales files, standardize their dates and currencies and load verified totals into a dashboard. If one source is incomplete, the run should stop safely and keep the last known-good output instead of replacing it with partial data.
