Paraphrasing Data Management and Data Warehousing

Traditional databases provided storage capabilities and offered limited (if any) data processing capabilities. This means that applications would read raw data from the database and, using application code such as ABAP or JAVA, develop complex data processing code to run on the application server.

SAP HANA Cloud database provides sophisticated, multitier data storage and, also, advanced data processing capabilities so that instead of an application requesting raw data from the database, it can request information. Data processing is now pushed down from the application layer to the in-memory database of SAP HANA Cloud. This means, not only is data processing faster, applications are leaner as they do not have to handle data processing logic. Leaner applications means more agility.

Calculation views are defined on top of tables to provide the data processing layer. Calculation views are usually stacked so that the lower calculation views provide opportunities for reuse by defining only the basic data layer. On top of those are more calculation views that add further calculations and semantics until they are ready for consumption by clients. Calculation views do not persist data but calculate results on-the-fly based on live data in source tables. Source tables can be local tables in SAP HANA Cloud database or remote tables in any database.

SAP HANA Cloud includes advanced data processing engines that combine with calculation views to natively store and process spatial, graph, hierarchy, and predictive data:

Develop predictive models using in-built algorithms including machine learning.

Store and query highly networked data models such as supply chains or online communities (for example, LinkedIn contacts).

Query hierarchies to extract valuable semantics such as ancestors, dependents, distance between nodes, or siblings.

Store and query geometric data to add spatial information to analytics such as mapping.

With SAP HANA Cloud, you can combine any type of data: text, geometries, IoT data, and so on, to create multi-models that power high-performance, advanced analytics.

Learn more about modeling in SAP HANA Cloud in our learning journey.

Today we are always searching for new technologies which helps us managing data. But in this endless research we ignored one big problem: We only focus on the technology itself and not on the data in which the real value lies. The transformation into the cloud has additionally increased the problem. Now we are trying to extract context via different cloud and on-premise systems.

So, the best approach to tackle this problem wouldn't be moving data hoping to reconstruct lost data. The best way forward is to have a business data-fabric-architect, a data management architecture that focuses on provisioning an integrated, semantically rich data layer over underlying data landscapes to provide seamless and scalable access to data without duplication.

SAP Datasphere plays a crucial role in SAP Business Data Cloud. While SAP Business Data Cloud can technically function without it, SAP Datasphere is required for out-of-the-box Insight Apps and advanced analytics. It allows users to build analytical custom models by integrating SAP-managed and third-party Data Products, with future potential for zero-copy data sharing. Additionally, it provides secured spaces for SAP-managed models and enables row-level authorization for customized analytics.

SAP Datasphere will help our customers here. It is the next generation and new name of SAP Data Warehouse Cloud. SAP Datasphere is a comprehensive data service, which is built on SAP Business Technology Platform and the foundation for a business data fabric. It enables any organization to provide meaningful data to any data consumer- while maintaining business context and logic.

An important step for our industry as well as for our customers is to work with an open data ecosystem of leading technology providers to enable tailored integration between SAP Datasphere and their solutions, making it easier for companies to simplify, integrate, secure and control their data landscape.

The first group of industry-leading data and AI providers includes Confluent, Databricks and DataRobot. The goal is to work together to promote and strengthen SAP Business Data Fabric as the new industry standard for an interoperable data strategy.  It provides functionality from data entry to self-service data access to SAP and non-SAP data.

With the introduction of SAP Datasphere, we kept all the powerful functions of SAP Data Warehouse Cloud and added new, sophisticated semantic modeling capabilities with the analytical model, a new data catalog and application and data integration.

Note that the illustration shows the three capabilities Data Warehouse Cloud already includes and the 3 new key elements which were added with the first release of SAP Datasphere.

As a summary we can say that SAP Data Warehouse Cloud has evolved into SAP Datasphere, a unified solution for data integration, data cataloging, semantic modeling, data warehousing, data federation, and data virtualization.

Now let's take a brief look at the capabilities of SAP Datasphere.

Learn more about SAP Datasphere on the official product page.

Get product assistance from the official help documentation: SAP_DATASPHERE.

Integrate, prepare, catalog, manage and model your data through data management and data warehousing capabilities from SAP BTP and start utilizing offerings like SAP Datasphere, SAP BW Bridge: Overview and Technical Deep Dive, Modelling with SAP HANA Cloud and SAP Master Data Governance Cloud.

• Datasphere federates heterogeneous data sources.

• Semantic modeling unifies data without replication.

• Governance ensures secure access for analytics.