Running SAP data analysis on MS SQL Server
The previous use cases focused on direct integration of SAP data to BI tools. Opening an SAP report in Tableau or SAP Lumira with one click is always fun, no databases or IT support is required. Is it already the ultimate SAP data freedom? What if there are ton of SQL experts in a company who would like to load data from SAP to SQL databases?
Well, many prospects and clients want to use SAP data not only in BI tools like Tableau, but also in complex analytical data processing workflows, corporate reporting, or just to make available SAP data in SQL databases (or other platforms) for ad-hoc queries. And often they want to do it with the same agility as BI: business users selecting the exposed SAP data in a free, self-service way.
One of our new features makes this possible, enabling self-service SAP to SQL exports. The first DBMS platform fully integrated by VirtDB Data Unfolder is the popular MS SQL or Azure SQL.
If you look at the short video of this use-case, the SAP to MS SQL extract process is fairly simple (as everything VirtDB), it starts with selecting your dataset in the SAP client (standard or custom reports, queries, tables, views, etc.) and clicking on the “Schedule extraction” item from Data Unfolder menu. You have to define the target table name (where your data will be loaded and stored) and Data Unfolder will bring you to the standard SAP background job scheduler transaction where you can set up a schedule for your data load.
SAP background job engine will execute your load at the scheduled frequency, with the conditions / selection criteria you have set – and the data will be loaded to the target SQL server.
If you look at your extracted SAP data in SQL Server’s management studio, you will see that not only a new table was created but a database view as well (with the “table_name”+”_view” postfix).
The MS SQL table contains SAP’s technical column names, not easy to understand by humans, but helping data migration between SQL Server and SAP. The view is defined over the table, selecting all the extracted SAP data, but more understandable / business friendly column names override those coming from SAP.
In the next use-case we will show how to make incremental loads of SAP data to SQL databases.