SAP Datasphere helps companies to analyze data live from a wide variety of sources without having to carry out extensive preparatory work. This article describes the platform in detail!
SAP Datasphere is a cloud-based data management platform. It supports companies in efficiently managing and evaluating their data. As the successor to SAP Data Warehouse Cloud, the solution has a significantly wider range of functions. This is particularly true with regard to the integration options for SAP and non-SAP systems and governance.
With SAP Datasphere, companies can integrate, model and analyze structured and unstructured data from different sources in real time. The special feature is that no physical data migration is required for this. This enables highly flexible and cost-efficient use of business-relevant data – even in hybrid and multi-cloud environments.
SAP describes Datasphere as the basis for setting up a business data fabric. This is a construct that provides every employee in the company with meaningful data. A business data fabric also makes it possible to provide data records in a very short time (“on demand”) and describe them in such a way that they can be understood and used by specialist users without IT knowledge.
The SAP Datasphere platform combines various data management functions. These are aimed at simplifying and improving the use of data within the company. The following properties and features are particularly noteworthy:
SAP Datasphere has powerful tools that companies can use to integrate data from the cloud and on-premise sources. This includes relational databases, data lakes and ERP systems as well as third-party platforms. All data is logically merged in a semantic layer so that it can be used for analytical and operational applications.
To understand: A semantic layer acts as an intermediary between complex data systems and business users. It translates technical data structures into understandable, business-related terms. This makes it possible for non-IT employees to interpret and analyze them.
The modeling functions of SAP Datasphere can be used to create semantic data models that can be used to map and interpret complex relationships between data. These data models are designed to logically link data from different sources without the need for physical consolidation.
This type of modeling helps users to identify relationships between business data – for example, which customers bought which products and when. Thanks to SAP Datasphere, such relationships become visible even if the corresponding information originates from different systems.
Another important element of the platform is data virtualization. It allows companies to analyze data from different systems without having to physically copy or move it. This means that SAP S/4HANA data can be used in real time, as can data from third-party sources.
The advantage of this approach is that companies do not have to carry out complex data mergers in order to create evaluations. Instead, they can access the existing data in the source systems directly. This saves time and reduces costs. Data quality also increases, as there are no duplicate or outdated data records.
With SAP Datasphere, decisions are not based on historical data, but on real-time information from the data landscape. This is made possible by various approaches such as streaming data processing, data replication with extremely low latency and the aforementioned virtualization. These methods ensure that changes in the data source are available in the database with minimal delay.
SAP Datasphere also provides advanced governance functions. These control access to data sources and ensure that compliance requirements are met. Companies can define detailed authorizations to ensure that only authorized users access certain data records.
Last but not least, SAP Datasphere is fully integrated into the SAP ecosystem – including the SAP Analytics Cloud, SAP BW/4HANA and SAP S/4HANA. At the same time, third-party solutions such as Databricks, Snowflake and Google BigQuery can also be connected.
The most important features of SAP Datasphere can be summarized as follows:
| Feature | Feature Description |
| Data virtualization | Access to distributed data sources without physical replication |
| Data modeling | Creation of semantic models for advanced analyses |
| Real-time data processing | Integrating and analyzing data in real time |
| Data governance | Strict access controls and data protection measures |
| Modern architecture | Cloud-native solution with hybrid integration options |
| Business data fabric | Standardized database for all analysis applications |
SAP Datasphere has many advantages for companies. First and foremost is the uniform view of distributed data, which makes a significant contribution to improving business decisions. It should also be emphasized here that all data is available in real time (or at least in near-real time). This means that decisions are not only based on a broader database, but also on up-to-date information.
Another added value lies in the decoupling of data access and data storage. This means that companies no longer have to go through complex processes such as ETL (Extract, Transform, Load). Redundant data storage is also no longer necessary. This makes data workflows more agile. This in turn enables faster adaptation to new requirements. At the same time, the complexity of data management is reduced, which lowers IT costs.
Last but not least, SAP Datasphere promotes a data-driven corporate culture, as it enables departments to access business-relevant information directly and view it in a consolidated manner. By comparison, this was not possible in previous environments with a business warehouse. Here, it was necessary to first provide data for reports centrally. In addition, new reports usually had to be created by IT experts, which sometimes resulted in long waiting times.
The advantages of SAP Datasphere can be summarized as follows:
SAP Datasphere is technically structured like a modern data warehouse, but is cloud-based and significantly more flexible. The architecture is roughly divided into three main areas or layers:
This layer contains all systems from which data originates. These include SAP S/4HANA, SAP BW or MS SQL Server, for example. These sources are connected to SAP Datasphere via interfaces and connectors such as the SAP Cloud Connector or the Data Provisioning Agent (DP Agent). The data can either be connected live or loaded into the platform in a targeted manner.
The actual data preparation takes place in this layer. SAP distinguishes between two workspaces here:
Technically, this involves a multi-level structure, comparable to classic data warehouse architectures.
In this layer, the previously created data models are queried by tools such as SAP Analytics Cloud, Power BI, Excel or Cubeware. Access is via standard interfaces such as ODATA or JDBC. Integration with the SAP Analytics Cloud is particularly close. The analytics solution can import several models directly from SAP Datasphere without additional data preparation.
SAP is working with several technology partners to provide companies with access to extended analysis, integration and governance functions. Four of these play a particularly important strategic role:
Collibra extends SAP Datasphere with extensive governance functions. These include a central data catalog, lineage tracking (origin of data), data quality guidelines and compliance functions. Collibra thus improves control over the origin and use of data. This is particularly important in regulated industries.
With the help of Apache Kafka, Confluent enables the processing of real-time data streams from sensors, transactions or logistics systems. This allows current events to be used directly in SAP Datasphere – for example for live analyses or immediate process adjustments.
Databricks combines SAP data with modern data lake technologies. This enables companies to merge large volumes of data from different sources. It is also possible to evaluate the data sets quickly and flexibly. This feature is important for sophisticated analyses or the use of artificial intelligence, for example.
DataRobot adds Automated Machine Learning (AutoML) to SAP Datasphere. Even without data science knowledge, business users can use the technology to automatically create forecasting models based on SAP data. These models can be integrated directly into reports or operational applications.
To be able to use SAP Datasphere, a number of requirements must be met. First and foremost, you need access to the SAP Business Technology Platform (SAP BTP). This is because SAP Datasphere is a cloud service within this platform. Accordingly, SAP Datasphere is also licensed via the BTP.
The following requirements should also be met
SAP Datasphere is the technological core of an SAP Business Data Fabric. The aim of the latter is to make data from different systems usable in a standardized, reliable and context-related manner – without having to move or duplicate it.
To this end, SAP Datasphere connects the various systems, standardizes the data, establishes correlations and makes the information available across system boundaries.
SAP Data Warehouse Cloud (SAP DWC) was already based on the idea of providing companies with a cloud-based, semantically oriented data management system. This concept was not only adopted in SAP Datasphere, but also significantly developed further. The following overview provides information about the differences:
| Feature | SAP Data Warehouse Cloud | SAP Datasphere |
| Strategic positioning | Pure cloud data warehouse | Central component of the SAP Business Data Fabric |
| Focus | Data modeling and reporting | Holistic data management (integration, context and governance) |
| Semantics and context | Basic functions available | Extended semantic layer, automatic transfer from SAP systems |
| Governance | Basic functions | Extended data governance (e.g. through Collibra integration) |
| Integration options | SAP-centric, limited options for third-party systems | Open data ecosystem with diverse integration options (SAP and non-SAP) |
| Data access | Data replication in the foreground | Flexible combination of replication and virtualization |
| Data provision | Table-oriented | Business-oriented data products and spaces |
SAP Data Warehouse Cloud:
Data modeling and reporting
SAP Datasphere:
Holistic data management (integration, context and governance)
SAP Data Warehouse Cloud:
Basic functions available
SAP Datasphere:
Extended semantic layer, automatic transfer from SAP systems
SAP Data Warehouse Cloud:
Basic functions
SAP Datasphere:
Extended data governance (e.g. through Collibra integration)
SAP Data Warehouse Cloud:
SAP-centric, limited options for third-party systems
SAP Datasphere:
Open data ecosystem with diverse integration options (SAP and non-SAP)
SAP Data Warehouse Cloud:
Data replication in the foreground
SAP Datasphere:
Flexible combination of replication and virtualization
SAP Data Warehouse Cloud:
Table-oriented
SAP Datasphere:
Business-oriented data products and spaces
With SAP Data Intelligence Cloud, another data management solution is available in parallel to SAP Datasphere. However, the two products differ in terms of their focus and target group.
SAP Datasphere concentrates on business-oriented data management. There is a particular focus on semantic consistency and self-services for data users. The target groups are primarily specialist departments and BI teams that require data for analyses and reports.
SAP Data Intelligence Cloud, on the other hand, is a specialized solution for technically complex data orchestration. It primarily supports the creation and management of data pipelines. SAP Data Intelligence Cloud was primarily designed for data engineers. Accordingly, the solution includes extensive options for processing unstructured data, preparing data for AI applications and managing heterogeneous data landscapes.
In short: SAP Datasphere focuses on business-oriented data provision, while SAP Data Intelligence Cloud focuses on technical data integration and pipeline control.
Note: SAP plans to initially offer Datasphere and Data Intelligence Cloud in parallel. In the long term, however, Datasphere will take over the tasks completely. To this end, the functions of the Data Intelligence Cloud will be gradually integrated into Datasphere.
SAP Datasphere and SAP Analytics Cloud (SAC) work closely together. Roughly speaking, Datasphere is responsible for delivering the data, while SAC is responsible for analyzing it. The integration is designed in such a way that data models from Datasphere can be used directly and live in SAC without the need for physical data movement or duplicate data maintenance.
Switching from SAP BW/4HANA to SAP Datasphere is particularly useful if a cloud-oriented data strategy is being pursued. The same applies if a company wants more flexibility, openness and modern analysis options. Apart from this, Datasphere will become the core platform for analytical data management in the future. This is according to official statements from SAP. BW/4HANA, on the other hand, will not undergo any further functional development.
Companies that have already invested in BW/4HANA can gradually transfer existing models and data flows to SAP Datasphere. This can be realized with the SAP BW Bridge, which enables the use of BW logic within Datasphere.
Yes, SAP Datasphere can be tested as part of a free 30-day trial version. Access is via a preconfigured environment (shared tenant). This makes it possible to get to know the most important functions of the platform – including the creation of data models, working with sample data and initial analysis functions. Access to the test is via the SAP website (see link below).
Are you planning to implement SAP S/4HANA? Would you like professional and efficient advice and support?
