As SAP continues its push toward cloud-based analytics with SAP Datasphere, many organizations are considering or actively moving away from SAP BW (Business Warehouse)—a platform they've trusted for years. While the promise of modern, cloud-native architecture and tighter integration with data lakes and external sources is compelling, the transition from a mature, stable system like BW to Datasphere isn't always smooth. This blog aims to caution users about the critical differences, limitations, and challenges you might face during or after this shift and going forward. If you're considering or planning a move, these insights can help you make informed decisions and avoid disruptions.
- Feature Parity is Still Evolving
SAP BW is a mature, Well-tested solution with rich features such as complex transformations, process chains, BEx queries. Many of these capabilities are either missing, limited, or work differently in SAP Datasphere.
- No full replacement for BEx Queries – Datasphere's modeling focused more on consumption by SAP Analytics Cloud.
- No direct support for ABAP-based logic – Complex custom logic using ABAP routines has no native counterpart in Datasphere.
- Limited process orchestration: Unlike BW’s process chains, Datasphere lacks a mature orchestration framework. The current "Data Integration Monitoring" interface is unintuitive and insufficient. Administrator may face extreme difficulties in tracking data packets transferred from source to target, identifying the next scheduled process, or monitoring delta loads effectively. The job start and end date and time is confusing in the load process .
- Additionally, Replication Flows used for change data capture (e.g., from S/4HANA) often run continuously for 24 hours in a batch, placing significant and sustained load on the source system—slowing it down. This is a recurring issue in real-world implementations.
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- 2) Performance will be Unpredictable
In BW, performance tuning has been refined over decades. Datasphere, being a cloud solution, introduces new variables like virtual data access, latency, and network bottlenecks.
Remote table access (e.g., to S/4HANA or SQL Server ) can be slower, even for relatively smaller datasets or views.
Limited visibility into backend processes makes troubleshooting harder than in BW
3) Security and Authorizations Are Different
SAP Datasphere’s security model, while aligned with modern cloud principles, lacks the flexibility and depth of BW’s traditional RSECADMIN-based authorization framework. This presents practical challenges, especially for medium to large enterprises that require fine-grained access control.
- Lack of model-level access restriction within a space: For example, suppose you have two analytical models in the same space—one exposing Sales Values, and another exposing Sales Margins a. If the goal is to restrict access to the margin model for a specific group of users, this is not achievable within the same space. The only workaround is to create a separate space for the restricted model and assign user access accordingly. In a Real projects, this leads to uncontrolled space proliferation, resulting in increased administrative overhead and confusion—especially problematic when using a single client for both Development and Production environments. the Business will end up creating 100's space matching each Scenarios. We cant even Share thealready Desinged Analytical models between the Spacess.
- Even the Data Access Control (DAC) mechanisms cannot enforce row- or object-level restrictions across models within the same space, making governance a nightmare in simple business scenarios.
- There is currently no way to share complex analytical models across spaces in SAP Datasphere. Each analytical model must be rebuilt from scratch within the relevant space. Any change to an analytical model must be manually repeated across all spaces where it's duplicated, making maintenance tedious, error-prone, and highly inefficient.
4) Data Integration Is Still Maturing
While SAP Datasphere offers broad connectivity to various data sources, its integration capabilities—especially with non-SAP systems—are still developing and may not match the stability of SAP BW.
- Example – MSSQL Extraction Issues: In our experience, data extraction from Microsoft SQL Server, which worked seamlessly and consistently in SAP BW, has been unreliable in Datasphere. Extraordinarily frequent timeout errors occur even when handling moderate data volumes. The same extraction logic, when executed through BW, performs flawlessly. This points to significant performance and stability gaps in Datasphere’s current data integration layer, particularly for external (non-SAP) sources.
5) Data Preview in Analytical View
In real business Environment, where analytical or consumption views deal with hundreds of thousands of records, the Preview function often fails—ending in memory errors .data preview for as little as 140 MB cant be handled by DataSphere (
| | cannot allocate enough memory: [9] Memory allocation failed;exception 1000002: Allocation failed ; $failure_type$=STATEMENT_MEMORY_LIMIT_FROM_GLOBAL_CONFIG; $failure_flag$=; $size$=147550016; $name$=Results; $type$=pool; $inuse_count$=178; $allocated_size$=7366036808; $alignment$=8) |
. Despite having sufficient memory allocated to the space, Datasphere struggles to handle large data previews. This limitation makes it difficult to analyze data validation at the analytical level or even at the Table level. This forces us to build SAC reports just to validate the data and go back to the Analytical view in DataSphere for fixing the Issues, unlike SAP BW where data review in InfoProviders is much simpler and more efficient.
Conclusion :
In our own project, where we fully replaced SAP BW with Datasphere without the BW Bridge (due to cost constraints), we’ve encountered several operational and architectural challenges that go beyond just missing features. From orchestration gaps to integration instability and access control limitations, the shift exposed critical differences that impacted delivery timelines and user trust.
Without SAP BW or BW Bridge in the landscape, organizations must be extremely cautious. Datasphere is not yet a feature-equivalent successor, and assuming parity can lead to misalignment between business expectations and system capabilities.
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