Important notes on sampling
- Sampling jobs may incur costs. These costs may vary between
and your clustered running environments, depending on type of sample and cost of job execution.
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- When sampling from compressed data, the data is uncompressed and then expanded. As a result, the sample size reflects the uncompressed data.
- Changes to preceding steps that alter the number of rows or columns in your dataset can invalidate the current sample, which means that the sample is no longer a valid representation of the state of the dataset in the recipe. In this case,
automatically switches you back to the most recently collected sample that is currently valid. Details are below.
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