In HPE Machine Learning Data Management, a Directed Acyclic Graph (DAG) is a collection of pipelines connected by data dependencies. The DAG defines the order in which pipelines are executed and how data flows between them.

Each pipeline in a DAG processes data from its input repositories and produces output data that can be used as input by downstream pipelines. The input repositories of a pipeline can be the output repositories of other pipelines, allowing data to flow through the DAG.

To create a DAG in HPE Machine Learning Data Management, you create multiple pipeline specifications and define the dependencies between them. You can define dependencies between pipelines using the input parameter in the pipeline specification. For example, if you have two pipelines named A and B, and B depends on the output of A, you would set the input parameter of B to the name of the output repository of A.