Creating an Analytic Application based on SAP Data Warehouse Cloud
Analytics Designer is a powerful capacity to create analytic applications based on SAP Analytics Cloud data models. However, in some cases SAC’s data modeling is not sufficient to meet the needs of the business. It is then interesting to use SAP Data Warehouse Cloud for the modeling part.
In this blog post I’m going to show you how you can create a SAC Analytic Application on an SAP Data Warehouse Cloud Analytical Dataset.
First, I would like to remind some concepts related to SAP Data Warehouse Cloud:
SAP Data Warehouse Cloud is an end-to-end warehouse in the cloud that combines data management processes with advanced analytics. It is built on the powerful SAP HANA and is, together with SAP Analytics Cloud, part of SAP HANA Cloud Services. In our case it is good to recall the definition of 2 artifacts present in DWC:
- Spaces are virtual workspaces for an individual or a group of users for data modeling, data integration, and story dashboard building. They are isolated and can be assigned quotas for available disc space, CPU usage, runtime hours, and memory usage. To model your data and create stories, you need to start off by creating a space in your data Warehouse Cloud Tenant
- Models are where you do all your data modeling in preparation for analysis. Models are stored in spaces.
For more information on space management you can look at this blog
With Analytics Designer you can create an application based on an SAP Datawarehouse Cloud data model.
In this blog post I will create a live connection to an SAP Data Warehouse Cloud system. I will use a dataset named “Beach Sales” which is in the space called named “sandbox”.
Before setting up your live connection in SAP Analytics Cloud, you first need to add the URL of your SAP Analytics Cloud as a trusted origin in your SAP Data Warehouse Cloud system.
Click the menu ”My Products“ in the top bar and select Analytics to switch to your embedded Analytics application.
In SAP Analytics Cloud go to Main Menu>System>Administration>App Integration. In the Trusted Origins section, add the URL of your separated SAP Analytics Cloud System.
Important: You can’t create an analytics application in the SAP Analytics Cloud embedded in an SAP Data Warehouse Cloud system. You need to use a standalone SAP Analytics Cloud. Live connections between SAP Analytics Cloud and SAP Data Warehouse Cloud can be established across tenants and data centers, but also when running both products in the same tenant.
Create the live connection
In your standalone SAP Analytics Cloud go to Main Menu>Connection and click on “+” symbol. Select the “SAP Data Warehouse Cloud” option.
In the dialog, enter a name and a description for your connection. The connection name cannot be changed later. Add your SAP Data Warehouse Cloud host name and enter 443 as the HTTPS port.
Note that SAML Single Sign On is preselected as the authentication method allowed. A valid SAP Data Warehouse Cloud user account in that tenant’s Identity Provider is required to log on to the data source.
Click OK and you’ll be prompted to logon to SAP Data Warehouse Cloud.
Enter a valid username and password for SAP Data Warehouse Cloud and click OK to setup the connection.
Create the analytic application
Create a new application and add a table in your application. Choose the last option “SAP Data Warehouse Cloud Analytic Dataset”
Analytics Designer will automatically show you all available connections, spaces and datasets in SAP Data Warehouse Cloud. By selecting the connection, you will be automatically logged on into SAP Data Warehouse Cloud by using the SAML authentication
Finally, you get a table displayed in your application based on an SAP Data Warehouse dataset. Then it’s up to you to design the application that that will satisfy the demands of your users
In this blog post we learned how to configure the backend SAP Data Warehouse Cloud in order to create an analytical application in SAP Analytics Cloud. This will help you to develop powerful analytic applications based on sophisticated dataset in order to meet your business users’ requirements.