A data pipeline smoke test is a technique used to speed up data pipeline development by automatically running all data transformations on empty or synthetic data, exercising every transformation inside the pipeline. This approach catches bugs in just a few seconds and can significantly reduce development time. It is particularly useful for pipelines with heavy business logic and can be integrated with various frameworks such as Pandas, SQL, Spark, or Dask. The test verifies that code in each transformation follows the rules of the data processing language and that each transformation can handle the type of data produced by upstream transformations. Data pipeline smoke tests can also help avoid accidentally breaking pipelines in production and provide broad test coverage.