A datum is the smallest indivisible unit of computation within a job. A job can have one, many or no datums. Each datum is processed independently with a single execution of the user code and then the results of all the datums are merged together to create the final output commit.
The number of datums for a job is defined by the glob pattern which you specify for each input. Think of datums as if you were telling Pachyderm how to divide your input data to efficiently distribute computation and only process the new data. You can configure a whole input repository to be one datum, each top-level filesystem object to be a separate datum, specific paths can be datums, and so on. Datums affect how Pachyderm distributes processing workloads and are instrumental in optimizing your configuration for best performance.
Pachyderm takes each datum and processes it in isolation on one of the pipeline worker nodes. You can define datums, workers, and other performance parameters can all be configured through the corresponding fields in the pipeline specification.
To understand how datums affect data processing in Pachyderm, you need to understand the following subconcepts: