Application workflow¶

We need to picture the application workflow as a Data Pipeline that helps you process and move data between different processing steps.

The processing steps are the nodes of a Directed Acyclic Graph (DAG).

A directed graph may be used to represent a network of processing elements; in this formulation, data enters a processing element through its incoming nodes and leaves the element through its outgoing nodes.

Below an example of a Directed Acyclic Graph depicting a classical workflow where:

  • Node A will process the data coming from the workflow sources
  • Node B and Node C will process the data generated by Node A
  • Node D will process the data generated by Node B and Node C

 !define DIAG_NAME Workflow example

 !include includes/skins.iuml

 skinparam backgroundColor #FFFFFF
 skinparam componentStyle uml2


:Node A;

  :Node B;
fork again
  :Node C;
end fork
  :Node D;




Take the time to carefully think how to structure the workflow by answering the questions:
  • How many nodes do I need?
  • Can the node execution be split in several tasks?
  • What will each node read as inputs?
  • What will each node write as outputs?
  • What parameters does each node need?
  • Is my workflow cost-effective in terms of I/O?
Below a few examples of workflows: