SAP Datasphere was extensively used by me in data integration and reporting processes in which multiple datasets were required to be combined in a unified analytical view. Much of my usage involved dealing with datasets, connecting models, and checking that reporting output aligned with the rules of the source system.

The biggest advantage provided by the application, in my view, was that it decreased the need for manual extraction and transformation of datasets. Rather than extracting the data in separate sets and then transforming them into a usable format, I could deal with the connected models directly. Review collected by and hosted on G2.com.

The learning curve is very steep, especially if you come from SQL-based or traditional independent data warehousing systems. It takes some time for you to be able to operate effectively once you know how models, spaces, and connections work.

Another difficulty lies in the fact that debugging problems in the data may involve navigating many levels of abstraction. If the result obtained does not correspond to what was expected, determining at which level there is a problem may take some additional time. Review collected by and hosted on G2.com.

I used SAP Datasphere on a trial basis and I am currently learning it. From my experience, it is a very powerful tool for data integration and analytics. It helps bring data from different sources into one place and makes it easier to understand and use for business insights. Even during the learning phase, I can see how useful it is for companies because it simplifies complex data management. For buyers, it is a good investment if they want better data control, real-time insights, and a modern analytics solution. Overall, I am still exploring it, but I can already see strong benefits for business use. Review collected by and hosted on G2.com.

Since I am currently using SAP Datasphere on a trial basis and still learning, I find that there is a bit of a learning curve, especially for someone new to data modeling and SAP ecosystem. Some advanced features are not immediately easy to understand without proper guidance or tutorials. It takes time to explore and fully understand the workflow. However, this is expected for a powerful enterprise-level tool, and I am still in the learning phase. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is how effectively it helps unify fragmented enterprise data without forcing organizations to completely rebuild existing data landscapes.

In our case, we were dealing with operational and analytics data spread across ERP systems, finance platforms, reporting databases, cloud applications, and custom operational tools supporting fintech workflows. One of the biggest challenges was maintaining a consistent view of business data across teams because every department was working from slightly different datasets and reporting logic. SAP Datasphere helped create a more centralized and governed data layer without disrupting existing operational systems.

What stood out immediately was the balance between integration flexibility and enterprise governance. The platform made it easier to connect SAP and non-SAP environments while maintaining better control over data consistency, lineage, and business context.

From a UI/UX perspective, the platform felt more business-oriented compared to traditional data engineering-heavy environments. Analysts and operational stakeholders could collaborate more effectively with data teams because the data modeling and access workflows were easier to understand.

Another strong point was performance for enterprise-scale analytics workloads. Even with large operational datasets and cross-system reporting requirements, query handling and data accessibility remained reliable for most business intelligence workflows.

Integrations were also a major advantage since the platform connected well with analytics ecosystems and reporting tools already being used internally. That reduced migration friction and improved adoption across teams. Review collected by and hosted on G2.com.

One thing I disliked about SAP Datasphere is that while it is extremely capable for enterprise-scale data unification, the implementation and operational setup can become complex very quickly in real production environments like ours.

At work, we use multiple operational systems across finance workflows, reporting platforms, customer analytics, and internally developed fintech applications. Bringing all of that data into SAP Datasphere required much more planning and governance alignment than we initially anticipated. A large part of the effort was not just technical integration, but also standardizing business definitions, reconciling conflicting datasets, and ensuring reporting consistency across teams.

For example, transaction reporting, reconciliation dashboards, and operational KPIs were originally being calculated differently by finance, operations, and analytics teams. While SAP Datasphere ultimately helped centralize and govern those datasets effectively, building clean semantic models and optimized reporting layers took significant collaboration between data engineering and business stakeholders.

Another challenge we experienced directly was performance tuning for complex analytics workloads. Standard dashboards and operational reporting worked well, but as teams started running cross-system analytics queries combining ERP data, operational metrics, and customer activity datasets, maintaining fast and consistent query performance required additional optimization work. Review collected by and hosted on G2.com.

What I like best about SAP Datasphere is how it brings together data from multiple sources into a single, unified environment without losing business context. It makes it easier to access and manage data across systems, especially when working with large enterprise datasets. The integration with other SAP tools is smooth, and it helps maintain consistency across data models, which is useful for reporting and analytics. I also like the way it supports real time data access, which improves decision making speed. Review collected by and hosted on G2.com.

What I dislike about SAP Datasphere is that the initial setup and configuration can be complex, especially for teams that are not already familiar with SAP environments. There is also a learning curve when it comes to understanding the data modeling approach, and performance can sometimes vary depending on how data is structured and queried.

SAP Datasphere mainly solves the problem of fragmented data across different systems. It creates a centralized data layer where data can be accessed, governed, and analyzed more efficiently. For me, this helps reduce the time spent on data preparation and improves data reliability. It also makes collaboration easier between teams working on analytics and reporting.

Overall, it is a powerful solution for organizations that deal with large scale data and want better control, visibility, and integration across their data landscape. Review collected by and hosted on G2.com.

Zero copy of data - We virtualize the source data for consumption and curate it in Datasphere without copying. The data comes from multiple sources and it all gets harmonized into one layer. Review collected by and hosted on G2.com.

We use SAC for planning, and we rely on Datasphere as the backend to load data into SACP. What we don’t like about Datasphere is that whenever we make model changes, it breaks the real-time replication, and we then have to reload the data. Review collected by and hosted on G2.com.

I like that SAP Datasphere has an intuitive and modern web design, which makes it easy to work with. It offers multiple paths to ingestion, making it valuable for my work in the Data and AI world, where I need to process massive amounts of data. Additionally, the initial setup of SAP Datasphere was easy, which was a great relief. Review collected by and hosted on G2.com.

A lot of things: 1. Datasphere CLI doesn't cover HDLFS 2. It's super expensive 3. The small files make it very expensive from a compute PoV and so you need a lot of optimization (run optimize) 4. The HDLFS part overall is still not mature at all 5. Integration with SAP BDC is still very special 6. Unclear roadmap especially with Dremio acquisition Review collected by and hosted on G2.com.

I use SAP Datasphere as a central platform to manage, structure, and leverage company data. I particularly like the ability to unify data from multiple systems while maintaining a clear and coherent structure. Its semantic layer makes the data easier to understand for business teams. I appreciate the features that facilitate daily work, such as graphical modeling that allows building business models visually without having to write complex code. The Data Flow feature is valuable because it allows creating transformation pipelines intuitively, ensuring good performance and clear governance. Review collected by and hosted on G2.com.

Even though SAP Datasphere brings a lot of value, some things could be improved. The interface sometimes lacks fluidity, especially when working on complex models or when multiple objects are open at the same time. Some actions take longer than necessary, which can slow down daily work. The interface could be faster; sometimes pages take a while to load, especially when opening multiple models. Some actions require too many clicks, which slows down the work. Review collected by and hosted on G2.com.

I primarily appreciate how SAP Datasphere simplifies working with data while preserving business context. The business layer (semantic modeling) is a significant advantage, allowing me to build models that reflect business meanings, making reports easier to understand and ensuring consistency across teams. I really like the data virtualization feature because it lets me access data directly from source systems without the need for heavy data duplication, saving time and reducing storage overhead. Its tight integration with SAP tools, especially SAP Analytics Cloud, enhances the ease and speed of reporting once data models are ready. The mix of low-code and SQL-based modeling is quite practical for me as a data analyst, allowing me to quickly build or modify datasets without heavily relying on engineering teams. Lastly, I appreciate the governance and structure it enforces, as it ensures cleaner, more reliable data, even though it involves a bit of a learning curve initially. Review collected by and hosted on G2.com.

Learning curve – It takes some time to understand spaces, modeling layers, and how everything connects, especially if you’re new to SAP. Performance tuning – When working with large datasets or complex models, performance can slow down and needs careful optimization. Cost considerations – Since it’s a cloud-based platform, usage and storage costs can increase if not managed properly. Limited flexibility compared to pure coding tools – For very custom or advanced transformations, sometimes traditional SQL/Python-based tools feel more flexible. Review collected by and hosted on G2.com.

I like that I don’t have to move everything into one warehouse. It lets me connect live data from different places, which saves a ton of time. The modeling is pretty straightforward once you click around a few times. Review collected by and hosted on G2.com.

Same honest vibe: I don’t like how finicky the connection setup can be—it feels like every small error takes forever to track down and debug. The UI also sometimes lags or hides things I just used, which gets frustrating fast. And while it handles SAP data well, integrating non-SAP sources can be pretty clunky. The pricing model worries me too; I’m constantly second-guessing whether a query will unexpectedly spike costs. It works great when it works, but overall it’s not as smooth as I’d hoped. Review collected by and hosted on G2.com.

The best part for me is the “Spaces” setup. It finally solves that old headache where Finance or Marketing wants their own data playground, but without the risk of them accidentally breaking our core IT models. It’s a huge relief to give them real autonomy while I still keep the “governance” keys.

I also really like that it doesn’t strip away the meaning of my SAP data. If you’ve ever tried moving SAP data into a non-SAP warehouse, you know what a nightmare it can be to rebuild all the logic. Datasphere just “gets” the metadata right out of the box, and that has saved us a ton of manual mapping work. Review collected by and hosted on G2.com.

The interface can feel really laggy. There’s a noticeable delay when saving models or switching between the Data Builder and the Business Builder, and it gets frustrating when you’re trying to move quickly.

Pricing is also pretty confusing. It’s hard to predict how many “Capacity Units” you’ll actually burn through, so you end up constantly monitoring usage to avoid an unexpectedly huge bill. On top of that, the error messages are often far too vague—half the time I have to dig through forums just to understand what a basic validation error is even trying to say. Review collected by and hosted on G2.com.