The following blog post refers to a series of blogs around SAP’s Business Technology Platform, which shows an end-to-end scenario across the SAP HANA Database & Analytics Portfolio. This is part three out of three, and shows how to visualize data coming from SAP Data Warehouse Cloud. The data is coming from the Tankerkoenig API, processed in SAP Data Intelligence Platform, SAP Data Warehouse Cloud and exposed as an Analytical View. This view is consumed in SAP Analytics Cloud. For further information regarding the scenario, I recommend to read the overview blog.

In this scenario we are using SAP Analytics Cloud Enterprise Edition.

Step 1: Create a connection from SAP Analytics Cloud to SAP Data Warehouse Cloud

There are two ways to work with SAP Analytics Cloud on top of SAP Data Warehouse Cloud:

  • Remote live connection
  • Space-aware connection

The space-aware connectivity is only recommended for testing purpose and not for a productive use. That is the reason for using the remote live connection in this example. In the remote live connection scenario SAP Analytics Cloud and SAP Data Warehouse Cloud can run on different tenants, and even in different data centers. However, SAP Analytics Cloud in combination with live connection to SAP Data Warehouse Cloud has some unsupported features. For further information, I recommend this SAP Note.

Before setting up the live connection in SAP Analytics Cloud, you need to add the URL as trusted origin in SAP Data Warehouse Cloud system.

Login to your SAP Data Warehouse Cloud tenant and switch to your embedded Analytics application (you have to login as an administrator).


Figure 1: Switch to embedded Analytics application


Navigate to System – Administration and click on the tab App integration


Figure 2: Navigate to Administration


Enter the URL of your SAP Analytics Cloud tenant as a trusted origin. If there are multiple SAP Analytics Cloud tenants on one data center, I would recommend using a wildcard by adding * for subdomain matching.


Figure 3: Add a Trusted Origin


After adding your SAP Analytics Cloud URL as a trusted origin, you can add SAP Data Warehouse Cloud as a connection in your SAP Analytics Cloud tenant.

In SAP Analytics Cloud navigate to Connections and select Connect to Live Data – SAP Data Warehouse Cloud


Figure 4: Create connection to SAP Data Warehouse Cloud


A Pop-Up will show up. There, enter the Connection Details, Host and HTTPS Port (for HANA and Data Warehouse Cloud always use port 443).Figure%205%3A%20Connection%20Details

Figure 5: Connection Details


Step 2: Create SAP Analytics Cloud Story with Geo Map

Now, you can directly create a new SAP Analytics Cloud Story. By clicking on the Data tab select Data From a Data source and Connect to Live Data – SAP Data Warehouse Cloud.Figure%206%3A%20Select%20data%20source

Figure 6: Select data source

Select the connection (created in step 1), the space and the analytical data setFigure%207%3A%20Select%20connection%2C%20space%2C%20analytical%20view

Figure 7: Select connection, space, analytical view


In the previous blog about “How to prepare geospatial data in SAP Data Warehouse Cloud”, we explained how to prepare geospatial data in Data Warehouse cloud. When creating an Analytical Dataset activate Expose for Consumption you can directly consume the view in SAP Analytics Cloud and visualize the data in a geo map.Figure%208%3A%20Geo%20Map%20in%20SAP%20Analytics%20Cloud%20based%20on%20SAP%20Data%20Warehouse%20CloudYou can use the created HANA ST_POINT Dimension from SAP Data Warehouse Cloud as the location dimension in SAP Analytics Cloud.

Figure 8: Geo Map in SAP Analytics Cloud based on SAP Data Warehouse Cloud


Figure 9: SAP Analytics Cloud Story


In this blog post we have showed you how to connect SAP Analytics Cloud via remote live connection to SAP Data Warehouse Cloud as well as to create a geo map in SAP Analytics Cloud. Additionally, you got an impression how an end-to-end scenario with SAP Software-as-a-Service solutions could look like, including information how the solutions are integrated.

In the future, we aim to to predict the gas prices and we have many more exciting ideas to expand this blog, so stay tuned.

Finally, I would like to say thank you to my colleagues Wei Han,  Axel Meier and Jonas Mittenbuehler, who helped to make this end-to-end demo story happen together! Thank you!


Randa Khaled

Randa Khaled

Author Since: November 19, 2020

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