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Create Workflow (part 3), Build a Task Workflow in SAP Data Hub, Trial Edition 2.5
07/09/2019
15 min.
Build a task Workflow using SAP Data Hub, trial edition 2.5.
You will learn
- How to build a task workflow using the Pipeline operator
This workflow combines both tasks which you created during the previous tutorials into a process which you execute at once.
Please note here in this tutorial GCP refers to Google Cloud platform and AWS refers to Amazon Web Services and Azure refers to Microsoft Azure.
Step 1: Create Workflow
Open the modelling environment for building pipelines via SAP Data Hub Modeler. To access the SAP Data Hub Launchpad in AWS or GCP or Azure you need go to the chapters 3.3 and 3.4 as described in the Getting Started with SAP Data Hub, trial edition guide. From SAP Data Hub Launchpad you could access the SAP Data Hub Modeler.
As the above URL is a local URL, it will be accessible only if you are doing the tutorials and have already configured the hosts file. If not, please refer to Getting Started with SAP Data Hub, trial edition 2.5 guide.
Enter DEFAULT as the Tenant, DATAHUB as Username and the password which you have selected during system setup as Password to logon.
Create a new graph and add Workflow Trigger operator, 2 X Pipeline operators and a Workflow Terminator to the graph by drag and drop.
Connect the output out port of the Workflow Trigger to the input in port of the first Pipeline operator. Connect the output out port of the first Pipeline operator to the input in port of the second Pipeline operator. Connect the output out port of the second Pipeline operator to the stop in port of the Workflow Terminator operator.
Right click on the first Pipeline operator and go to Open Configuration. Under the parameter Graph name select the graph that we have created in the tutorial Create Workflow (part 1), Enrich data with Data Transform. In our case, we have named it as Workflow 1.
Similarly, for the second Pipeline operator, select the graph that we have created in the tutorial Create Workflow (part 2), Aggregate data with Data Transform under the parameter Graph Name. In our case, we have named it as Workflow 2. Also increase the parameter Retry interval from 20 to 200 for both the Pipelines.
Step 2: Execute Workflow
Once done, save the graph. In our case, we have named it as Workflow 3. Now execute the graph using the button at the top of the page. The execution status is displayed in the bottom part of the screen and it changes from Running to Completed once the execution completes.
We have now executed the Workflow 3 but you will notice in the above screenshot that, Workflow 3 calls the linked pipeline, Workflow 1. Once the execution of Workflow 1 completes, it starts the execution of Workflow 2.
Remark: When you look at the EnrichDevices or STATISTICS_DATA data set, you will not see any changes compared to the previous two tutorials.