Overview
Seamless Planning allows you to leverage planning data, this is possible due to a tight integration between SAP Analytics Cloud and SAP Datasphere. Seamless Planning allows you to start either with data or completely from scratch.
Introduction
What does Seamless Planning do? As a modeler, when creating a model, you have the possibility to store your model’s master data and fact data in SAP Analytics Cloud or SAP Datasphere. Storing model data in SAP Datasphere reduces the load on SAP Analytics Cloud. By making the data available in SAP Datasphere, you can use it to create views and Analytical Models. You can save any model with Measures as well as Public Dimensions and Currency Rates Tables to an SAP Datasphere space.
SAP Analytics Cloud objects available in SAP Datasphere are in read-only mode and you cannot make structural changes to these objects in SAP Datasphere. SAP Analytics Cloud models expose the underlying data as a local table (Fact), while public dimensions expose the master data as a local table (Dimension). You can then use these SAP Analytics Cloud objects in graphical views, SQL Views, Data Flows, Analytics Models, Transformation Flows, etc.
Prerequisites
The prerequisites below are necessary to connect SAP Analytics Cloud and SAP Datasphere for seamless planning.
- An SAP Analytics Cloud tenant running on HANA Cloud.
- Both SAP Analytics Cloud and SAP Datasphere tenants must be located in the same data center.
- Both SAP Analytics Cloud and SAP Datasphere tenants must use the same identity provider.
- SAP Analytics Cloud and SAP Datasphere tenants must be linked in a 1:1 relationship by the system owner.
Procedure
Go to Modeler and click on Create New Model.
Select Start with data and click Next.
Another popup will appear prompting to select a Data Storage Location, select SAP Datasphere. You can now select a SAP Datasphere space to store your planning data. Select a SAP Datasphere space to store and deploy your SAP Analytics Cloud model using the dropdown menu, and click Next .
In the data source list page, select File (Local File or File Server).
Click Select Source File , and in the browser window, select the file you want to use as a data source.
In the Model Details panel, enable the Planning Capabilities toggle to enable planning features available in SAP Analytics Cloud. Once done, we need to expose this data to SAP Datasphere. To do this, in the Model Details panel, under Exposure in SAP Datasphere, click on the Edit button. Once done, save the model.
Results
You can now prepare data and follow your usual modeling workflow, just like you would for any model. SAP Analytics Cloud models expose the underlying data as a local table (Fact), while public dimensions expose the master data as a local table (Dimension). You can then use these SAP Analytics Cloud objects in Graphical Views, SQL Views, Data Flows, Analytics Models, Transformation Flows, etc.
Create a Model Structure for SAP Datasphere
Context
You can also create an empty model without data and reuse its structure as a basis for an analytical model in SAP Datasphere.
Procedure
Go to Modeler and click on Create New Model.
In the popup, select Start with an empty model and click Next .
Another popup will appear prompting to select a Data Storage Location, select SAP Datasphere. You can now select a SAP Datasphere space to store your planning data. Select a SAP Datasphere space to store and deploy your SAP Analytics Cloud model using the dropdown menu, and click Next .
In the Model Details panel, under Preferences, enable Planning Capabilities toggle to enable planning features available in SAP Analytics Cloud by default.
Create Dimensions, Measures, and any properties or hierarchies as required.
Once done, we need to expose this data to SAP Datasphere. To do this, in the Model Details panel, under Exposure in SAP Datasphere, click on the Edit button.
This will open a popup where you can enter a Technical Name and Business Name for the model. Click on Ok.
Now save the Model.
Result
You can now prepare data and follow your usual modeling workflow, just like you would for any model. SAP Analytics Cloud models expose the underlying data as a local table (Fact), while public dimensions expose the master data as a local table (Dimension). You can then use these SAP Analytics Cloud objects in Graphical Views, SQL Views, Data Flows, Analytics Models, Transformation Flows, etc.
