SAP Data Warehouse Cloud is based on the concept of cross-application warehousing. This allows customers to gain the flexibility of bringing different data sources in different formats from different applications, in the same data warehouse.
All the power of SAP Data Warehouse Cloud — including business semantics, data transformation, and powerful analytics — takes data analytics to a new level. Our customers can consume, access, and bring in data from so many different places, then work on this data and get powerful insights.
This is how the magic happens: in SAP Data Warehouse Cloud, you can connect different types of data sources and then virtually connect them to use for your data modeling efforts. It's easy and fast to incorporate data from many different sources directly in the Data Builder. On top of that, you can connect different data views into complex data models, mixing and joining as needed to get as many different perspectives as needed.
On top of all that, you can use SAP Analytics Cloud – or soon other analytics tools of your choice – to create powerful and visually appealing data stories.
Currently, you can use the following source systems: SAP HANA on premise, ODATA (using SDA), SAP S/4 HANA on premise, SAP BW on premise, other ABAP systems on premise, and local .csv files.
The way you then model your data and add business semantics to it is the same, no matter where the data comes from. This means your whole organization can quickly adapt to working in SAP Data Warehouse Cloud, instead of having to tackle, extract and integrate data from multiple systems, formats and ways of working.
This empowers users from all lines of business to work with and get insights from data that previously might have been difficult to reach. It also enables all data stories to offer a complete view of the data, as there's no need to keep parts of your data insights separated from the main message.
The goal of cross-application warehousing is to allow organizations to get a consistent view of their data. This is crucial to be able to handle the high volume and high complexity of data sets today.