SAP Datasphere Reviews & Product Details

Simplicity to use the different features. Short curve of learning how to use the different solutions. Performance ok and requirements easy to satisfy. Review collected by and hosted on G2.com.

The customization part is not so easy to learn and lacks of some powerful mechanisms to impleement complex tasks. The integration with other tools can be improved. Review collected by and hosted on G2.com.

I like SAP Datasphere's ability to unify data from multiple sources into a single, trusted layer while preserving business context. It simplifies data modeling, improves governance, and makes it much faster to deliver reliable insights. Its seamless integration with both SAP and non-SAP systems is especially valuable for building scalable and flexible analytics solutions. Review collected by and hosted on G2.com.

It has a steep learning curve, and performance can vary with complex models and large datasets. The user interface and documentation could also be more intuitive and easier to use. A more intuitive interface with clearer navigation and fewer steps for common tasks would help. For documentation, more practical examples, step-by-step guides, and better troubleshooting resources would make it easier to use. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is the way it simplifies access to trusted, business‑ready data across the entire organization. The semantic modeling layer, strong integration with SAP and non‑SAP sources, and the ability to virtualize or replicate data depending on the use case make it very flexible. It also provides a unified environment for data modeling, governance, and analytics, which helps accelerate projects and reduces dependency on IT. Review collected by and hosted on G2.com.

Certain tasks require switching between multiple interfaces, and performance can vary depending on the data virtualization approach. Documentation and examples could also be more detailed, especially for complex modeling scenarios. While the platform is evolving quickly, these areas can slow down adoption. Review collected by and hosted on G2.com.

What I like most about SAP Datasphere is how it simplifies data integration across different sources while preserving business context, which makes analytics much more reliable and meaningful. Review collected by and hosted on G2.com.

While SAP Datasphere is very powerful, it can feel complex at times, and simplifying certain processes would make it even more efficient to use.It’s a powerful tool, but it can be a bit complex and not always the most intuitive at first, especially for new users without SAP background. Review collected by and hosted on G2.com.

I appreciate how SAP Datasphere brings together data from multiple SAP and non‑SAP systems in a unified, well‑structured environment. The UI is clean and intuitive, which makes modeling and exploring data far easier than in traditional warehouse tools. Its integration capabilities are a major strength—connections to SAP S/4HANA, BW, and cloud sources feel seamless and reduce the time spent on manual data preparation. Review collected by and hosted on G2.com.

While SAP Datasphere is powerful, it can feel overly complex for new users. The UI, though modern, sometimes hides key functions behind multiple layers of menus, which slows down navigation and data modeling. Integration with non‑SAP sources occasionally requires extra configuration or connectors that aren’t as seamless as advertised, adding setup time and maintenance overhead. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is how easily it connects data from multiple sources without requiring complex integration work. The semantic layer and clear data modeling tools make it simple to maintain consistency across reports. Changes propagate quickly, which saves time and reduces errors. Overall, it streamlines data preparation and helps create reliable, unified analytics much faster. Review collected by and hosted on G2.com.

One of the main downsides of SAP Datasphere is that some integrations require additional configuration or rely on external tools, which slows down onboarding. Performance can also be inconsistent when working with larger datasets, especially during complex transformations. These issues sometimes interrupt the workflow and make it harder to maintain a smooth data pipeline. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is how it brings data from SAP and non‑SAP systems together in a unified, well‑governed environment. The interface is clean and intuitive, which makes modeling and exploring data straightforward even for mixed technical teams. Performance has been consistently strong, especially when virtualizing data without unnecessary replication. The onboarding experience is smooth thanks to clear workflows and strong integration with existing SAP landscapes. With the growing set of AI‑assisted features, it’s becoming even easier to build reliable, analytics‑ready datasets that accelerate business insights. Review collected by and hosted on G2.com.

The main downside of SAP Datasphere is that the UI can feel unintuitive at times, especially when navigating between modeling, connections, and spaces. Performance is generally good, but certain virtualized queries can be slow depending on the source system. Pricing can also be difficult to estimate, which makes ROI planning harder. Some onboarding steps require prior SAP knowledge, and the AI‑assisted features are still early in maturity compared to other platforms. Review collected by and hosted on G2.com.

I like how SAP Datasphere helped solve the problem of fragmented data and made analyzing pricing, cost, and reporting metrics easier by centralizing data from multiple sources. It reduced the manual work for me and made it easier to reuse data models across different analyses, keeping the data consistent across teams. The centralization of data saved time and prevented our reporting from feeling scattered. Once we had the data modeled properly, the reuse across different analyses helped us to work more efficiently. Review collected by and hosted on G2.com.

It can feel a bit complex at first, especially when working with modeling, permissions, and setting things up. Performance sometimes hits a bottleneck with large or complex data. The platform sometimes lacks intuition, indicating a definite learning curve. Usability and onboarding could be improved, as some parts are not intuitive at first, so a clearer guide (maybe a wizard) and better documentation could ease adoption, especially for newer users. Review collected by and hosted on G2.com.

SAP Datasphere is best known for its ability to unify SAP and non-SAP data while preserving business context. It offers a modern, cloud-native foundation that reduces the need for heavy on-premise infrastructure and provides strong flexibility through both real-time data federation and adaptable replication options. Review collected by and hosted on G2.com.

SAP Datasphere is criticized for its high licensing costs, slow performance with large datasets, and complex, non-intuitive UI. Users frequently struggle with sluggish interface response times and, in some cases, inadequate data integration with non-SAP sources, leading to a steep learning curve. Review collected by and hosted on G2.com.

I use SAP Datasphere as a centralized data platform to bring together data from different sources, making it easier to analyze and use for business decisions. It helps integrate data from both SAP systems and non-SAP sources, so everything can be accessed from one place without creating multiple disks. I appreciate that it addresses duplication, inconsistency, and confusion by providing a single platform where data can be integrated, modeled, and accessed in a governed way. I mainly use features related to data integration, modeling, and governance. The key tool I rely on is the data builder, which helps in creating data models, use, and transformation in a structured way. I prefer using it because it allows both rule-based transformations and graphical modeling, making it flexible depending on the use case. I also use the connections and data integration features to bring data from different sources, reducing the effort of building separate pipelines and keeping everything centralized. Review collected by and hosted on G2.com.

While SAP Datasphere is very powerful, there are a few areas where it could be improved. One challenge is flexibility compared to more open data platforms. For complex transformation or custom workflow, it can sometimes feel restrictive, especially if you are used to tools like Spark or Databricks, where you have more control over the logic and executions. Performance can also be a concern in certain scenarios, particularly when working with large datasets or complex models. Some queries or transformations may take longer than expected, and optimization options are not always task fair. Review collected by and hosted on G2.com.