SAP Datasphere Reviews & Product Details

What I like most about SAP Datasphere as a data engineer in the NHS is its strong governance and seamless integration with core SAP systems like SAP S/4HANA. It allows me to build trusted, well-structured data models while maintaining strict security and compliance standards. That reliability is crucial in a healthcare environment where accurate reporting directly supports operational and financial decision-making. Review collected by and hosted on G2.com.

One thing I dislike about SAP Datasphere is that it can feel rigid and less flexible compared to more modern data platforms. Development and modelling can be slower than expected, especially for complex transformations. It can also be frustrating when performance tuning and troubleshooting aren’t as transparent or straightforward as you’d like. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is its ability to seamlessly integrate data from multiple sources into a unified, business-ready data model. It makes it much easier to connect SAP and non-SAP systems without heavy data replication, which improves efficiency and reduces complexity.

I especially appreciate its strong integration with SAP Analytics Cloud, enabling real-time insights and consistent reporting across the organization. The data modeling capabilities are intuitive, allowing both technical and business users to collaborate effectively.

Additionally, the governance and security features help maintain data integrity while ensuring controlled access, which is critical for enterprise environments. Overall, SAP Datasphere provides a scalable, cloud-based foundation for modern data management and analytics. Review collected by and hosted on G2.com.

While SAP Datasphere offers strong integration and modeling capabilities, there are a few areas that could be improved. The learning curve can be steep, especially for users who are new to SAP’s data ecosystem. Some features and configurations require deep technical knowledge, which may limit accessibility for less technical users.

Performance optimization can also be challenging when working with large or complex data models. In certain scenarios, troubleshooting and fine-tuning require additional effort and expertise.

Additionally, licensing and overall cost can be a concern for smaller organizations, particularly when scaling usage. Clearer documentation and more hands-on examples for advanced use cases would further enhance the user experience. Review collected by and hosted on G2.com.

I like using SAP Datasphere to integrate, process, and analyze data from multiple sources in real-time. It provides real-time insights that improve decision-making and project efficiency. I appreciate how the real-time processing and reliable data delivery allow me to access and analyze up-to-date data as soon as it's available, helping me make faster and more informed decisions. This also reduces errors and delays that used to happen with manual data handling, making my workflows much smoother and more efficient. Additionally, I found the setup was easy, which was a big plus. Review collected by and hosted on G2.com.

Advanced configuration is limited and the learning curve is steep. More flexible config for complex pipelines and better tutorials for advanced workflows would help. Review collected by and hosted on G2.com.

Spaces = clean isolation. I can spin a sandbox space per branch, wire connections, and run pipeline tests without stepping on prod models. It’s the right level of multi-tenant for QA.

Semantic layer with teeth. Business entities/measures live in one place (dimensions, associations, units/currencies). We write “contract tests” against the model, not brittle SQL, if someone drops a KPI or changes a type, CI fails loudly.

Virtualize first, replicate when needed. Being able to federate queries to S/4HANA/BW or stage extracts into HANA Cloud keeps performance decent while avoiding data sprawl. Our tests cover both paths using the same artifacts.

Decent lineage & impact analysis. When a column rename lands upstream, lineage shows which views/calculations break. I can link an impact report to a failing test and the owner knows exactly where to patch.

Transportable configs. Moving artifacts from dev -> test -> prod is predictable; we version transports next to our test code, so rollbacks are boring.

BW Bridge for the real world. Legacy BW content doesn’t block us; we can validate migrated models side-by-side with new ones until parity tests pass.

Tight with SAC. For UAT, we point a SAC story at the test space and stakeholders validate numbers without bespoke extracts. Review collected by and hosted on G2.com.

UI latency at scale. Big models make the modeling UI feel heavy; complex Data Flows are sluggish to edit.

Error messages are terse. Federation/privilege errors often read like riddles, you end up checking remote system logs to learn what actually failed.

Transform limits. Graphical transforms cover 80%; the last 20% still needs SQLScript or external prep.

Transport gotchas. Dependency ordering can bite, miss one shared artifact and your pipeline deploy looks “green” but doesn’t run. Review collected by and hosted on G2.com.

I really like the ease of usage with SAP Datasphere. Coming from a different field, it was really easy and handy to pick up, especially with the support from my colleagues. The transfer of knowledge in terms of this software is quite smooth, and I can easily learn from others. I also appreciate how we can save interfaces or views that we've built, which makes learning and usage more straightforward. Review collected by and hosted on G2.com.

I think sometimes when we have very, very large lists, scrolling down and selecting the right stuff is pretty hard. But then, there's a source dump interface which came in handy. Review collected by and hosted on G2.com.

Easy to use no need for prior specific education! Thanks for the SAP provideers and for my company to choose SAP.

In addition, SAP Datasphere is a unified, cloud-based data service built on the SAP Business Technology Platform (BTP) that provides a comprehensive business data fabric for data integration, cataloging, semantic modeling, data warehousing, and virtualization across SAP and non-SAP data sources. Review collected by and hosted on G2.com.

Dislikes about SAP Datasphere include high costs and lack of scalability for small businesses, performance issues with large datasets, complex integration with non-SAP tools, a steep learning curve, intricate data modeling and setup processes, and frequent, sometimes disruptive UI changes. Review collected by and hosted on G2.com.

I appreciate SAP Datasphere for balancing technical flexibility with enterprise governance, which is especially important for our work. I like that it preserves SAP business semantics when integrating with systems like SAP S/4HANA. The data virtualization capabilities are great for accessing certain datasets while keeping sensitive clinical data protected. I also value the cloud scalability, which is beneficial when new grants come in. Review collected by and hosted on G2.com.

The learning curve is steep - it could be more simplified and there could be improved performance for mixed workloads. Review collected by and hosted on G2.com.

I appreciate the ease of use of SAP Datasphere and the time savings it provides. The integration, preparation, and availability of data are done more quickly, with fewer manual manipulations. I like being able to quickly integrate multiple sources, model the data simply, and share it easily. This reduces manual tasks, limits errors, and speeds up the production of reliable analyses. Review collected by and hosted on G2.com.

The tracking of flows and the diagnosis of errors could be more detailed: more explicit logs, more comprehensive alerts and monitoring. Review collected by and hosted on G2.com.

I like that SAP Datasphere keeps the business meaning of SAP data when you bring it into your models. This makes the work easier because you do not need to rebuild logic that already exists in the source systems. I also like that you can join SAP data with non SAP data in one place. The visual modeling helps both technical and business users work together. It feels like a modern and trusted layer for analytics. Review collected by and hosted on G2.com.

I dislike that some features still feel slow or not fully mature. Complex modeling can require many steps, and performance is not always consistent with large data sets. The user interface can also feel confusing at times for new users. Review collected by and hosted on G2.com.

What I appreciate most is the ease of integration. Since I work with automation frameworks such as Python and Jenkins, I need tools that can interact seamlessly with my scripts, and Datasphere does this very effectively through its CLI. I don't have to manually navigate the UI to deploy changes or set up a test space. I simply run a command and it's taken care of. This also contributes to the ease of implementation. Moving a new data model from development to a QA environment is straightforward because we can automate the entire upload process. It saves us a significant amount of time. I use Datasphere constantly, pretty much every time a developer commits code, and it manages the frequent updates without any issues. Honestly, what I value most is being able to skip the manual steps and manage the infrastructure as code. Review collected by and hosted on G2.com.

Honestly, the documentation for specific CLI commands can sometimes be a bit sparse. For example, when I encounter an unusual edge case while scripting a deployment, it often takes considerable effort to track down the exact flag I need. Additionally, when automation fails, the error messages returned by the CLI are often vague. Sometimes, it simply reports 'failed' without specifying which part of the JSON model was incorrect, which means I end up spending extra time debugging or combing through logs. Once everything is set up, it works very well, but troubleshooting during the setup phase can be frustrating. Review collected by and hosted on G2.com.