LeverX tested the top DWH platforms and found major advantages and some surprising flaws. Find out which one offers the best value for your investment.
If you've been navigating the abbreviation pool of enterprise data — BW, HANA, DWH, SAC — you might have stumbled across SAP Datasphere and wondered if it’s just another rebranded acronym to memorize.
Spoiler: it’s not. SAP Datasphere (formerly known as SAP Data Warehouse Cloud) is SAP’s answer to the modern, flexible, cloud-based data warehouse designed to make enterprise data accessible, understandable, and usable across your entire business.
But is it really better than alternatives like Snowflake, SAP BW, Google BigQuery, or Amazon Redshift? Let’s take a closer look.
Under the hood, SAP Datasphere is built to be modular and scalable like Lego bricks, but for your data. It combines database services, data integration, modeling, and visualization (through tight integration with SAP Analytics Cloud) in one tidy package. It’s designed for both IT teams and business users.
Here are its core components:
Spaces are isolated, virtual work environments that allow different teams or departments to independently manage their own datasets and data models. Each space has role-based access control, making it easy to enforce security while allowing autonomy. Spaces are ideal for decentralizing data ownership, without sacrificing central governance.
This is where raw data is integrated, transformed, and modeled. The Data Builder supports multiple ways to connect to data: live connections, federated access, or physical replication. You can work with SQL views, graphical modeling tools, or scripted logic, allowing both technical and non-technical users to participate in data engineering tasks.
Key capabilities:
The Business Builder allows organizations to create a semantic layer over technical data. Here, users define business entities (e.g., Customer, Product, Region), measures (Revenue, Margin), and hierarchies that align with the organization's internal language and KPIs. This promotes consistency across reporting and analytics applications.
Why it matters:
Datasphere supports a wide range of data source connections, both SAP and non-SAP. These include:
Live connectivity and data federation reduce the need for bulk data transfers and enable near real-time data access.
Governance is built into every layer of the Datasphere architecture. Role-based access controls, activity logging, and data lineage tracking help organizations maintain compliance and transparency. This is particularly critical in regulated industries like finance, pharma, and manufacturing.
At its core, SAP Datasphere is powered by SAP HANA Cloud, which provides high-performance in-memory data processing and advanced analytical capabilities. This allows for:
Together, these components form a robust and flexible architecture that supports hybrid data management strategies, empowering both central IT and decentralized business users to collaborate effectively.
Datasphere is not an island; it’s an integration-first platform that enhances the overall SAP data and analytics ecosystem by bringing performance, semantic alignment, and real-time accessibility into one flexible environment.
Since SAP Datasphere is built on SAP HANA Cloud, it comes with high-performance, in-memory database power right out of the gate. It also works well with SAP Analytics Cloud, which means your data stories, dashboards, and predictive analytics don’t need to run the extra mile to get fresh, real-time data.
Unlike traditional setups, where getting data from one place to another feels like shipping a piano cross-country, Datasphere integrates natively with SAP S/4HANA, BW/4HANA, and other sources, without duplicating everything.
Let’s break down how SAP Datasphere interacts with each of the solutions.
Since SAP Datasphere is built on SAP HANA Cloud, it leverages one of the most powerful in-memory database engines available. This allows it to execute complex queries with extremely low latency, support real-time data processing, and scale across enterprise-grade workloads. HANA Cloud is the computational backbone of Datasphere, enabling:
Datasphere inherits HANA’s columnar storage, advanced compression, and parallel processing capabilities, making it suitable for demanding analytical use cases such as financial consolidation, operational analytics, and forecasting.
Additionally, for companies already using SAP HANA Cloud outside of Datasphere (e.g., in SAP Business Technology Platform scenarios), integration is seamless via native connectors and shared metadata. Datasphere can extend the reach of existing HANA models into broader reporting contexts, without duplicating or reshaping them.
SAP Datasphere and SAP Analytics Cloud are designed as a unified analytics solution. Datasphere provides governed, semantically rich data models, while SAP Analytics Cloud adds powerful data visualization, planning, and augmented analytics capabilities. Key integration highlights:
This creates an end-to-end stack where data is modeled, governed, and visualized in a way that minimizes friction and duplication, while increasing transparency and collaboration between IT and business users.
Datasphere supports native integration with SAP S/4HANA and SAP BW/4HANA, both via live connections and data replication, depending on the business's performance and architectural needs.
This bidirectional compatibility allows enterprises to move at their own pace, leveraging existing investments in BW, while gradually modernizing or consolidating data models within Datasphere.
Let’s start with the obvious rivalry — SAP BW/4HANA.
SAP BW/4HANA is a robust, mature enterprise data warehousing solution that excels in structured reporting, governed data environments, and well-established SAP-centric workflows. It is particularly effective for organizations with tightly integrated SAP systems and complex reporting requirements, where control, validation, and standardized processes are top priorities.
SAP Datasphere, on the other hand, is a cloud-native solution designed to offer greater agility, real-time data access, and flexibility across both SAP and non-SAP sources. It allows for more decentralized data ownership and enables business users to explore, model, and work with data directly, without extensive dependency on IT or predefined structures.
In short:
Importantly, these platforms are not mutually exclusive. Many organizations run them in parallel, using BW/4HANA for established reporting processes and Datasphere for new, more exploratory, or cross-functional analytics initiatives. SAP supports and encourages such hybrid strategies.
Snowflake is a well-established cloud-native platform known for its scalability, separation of storage and computing, and support for semi-structured data formats. It works in a data lake-style architecture, where massive volumes of structured and semi-structured data can be stored and queried independently of computing. It is cloud-native to the core and platform-agnostic, which makes it very attractive for companies juggling 20+ data sources and a lot of unknowns.
Where SAP Datasphere pulls ahead is in native integration with SAP ecosystems and business context modeling that aligns with business processes and terminology. While Snowflake offers powerful tools for raw data processing and custom modeling, it does not natively understand SAP application metadata or business context without manual configuration.
Therefore:
Snowflake provides a flexible, open environment but requires building your own structure and business logic from scratch. SAP Datasphere, by contrast, comes with built-in business context and semantic models tailored for enterprise use.
BigQuery (Google) and Redshift (AWS) are heavy hitters in the data warehouse space, especially for organizations already deep in their respective cloud ecosystems.
Datasphere differs from these in business logic and semantics. BigQuery and Redshift treat your data as raw material. Datasphere treats it as something that should already make sense to your business.
So, if your life revolves around SAP applications, Datasphere saves you from reinventing the wheel whenever you want to report something.
Summary:
| Category | SAP Datasphere | SAP BW/4HANA | Snowflake | Google BigQuery | Amazon Redshift |
| Cloud-native architecture | Fully cloud-native with modern data federation | Primarily on-premise; limited cloud enablement | Cloud-native with decoupled storage and compute | Cloud-native and serverless | Сloud-optimized |
| SAP ecosystem integration | Deep, seamless integration with SAP systems and semantics | Native SAP compatibility | Requires connectors or third-party tools | Requires third-party tools; less seamless with SAP | Needs third-party tools; less seamless integration with SAP |
| Business modeling and semantics | Strong focus on business context and semantic layers | Rigid data modeling with heavy IT governance | Minimal native support; more technical configuration | Raw data focus; limited out-of-the-box semantic support | Emphasis on raw data processing with minimal built-in semantic modeling |
| Real-time data access | Supports real-time access and data federation | Possible with an advanced setup | Primarily batch; real-time requires custom architecture | Primarily batch processing; near real-time with tuning | Primarily designed for batch processing; near real-time capabilities require optimization |
| Usability for business users | Designed for collaborative, business-driven use | Requires technical expertise and IT support | Developer-focused; limited business-facing tools | Strong for analysts, less intuitive for non-technical users | Powerful for technical analysts; may pose a usability barrier for business users without a data background |
| Data lake compatibility | Basic capabilities; not core-focused | Not supported | Strong support for structured and semi-structured data | Strong support | Moderate support |
| Flexibility and agility | High–modular workspaces, low-code/no-code options | Low–predefined structures and processes | High flexibility for modern data teams | Highly flexible | Flexible within the AWS stack |
| Best fit for | Enterprises with SAP-centric data landscapes and hybrid needs | Existing SAP environments needing structured reporting | Organizations seeking a scalable, cloud-first architecture | Companies prioritizing speed, scalability, and serverless architecture over deep business semantics | Enterprises with structured data and traditional BI/reporting needs |
Data doesn’t drive value — understood data does.
One of SAP Datasphere’s most powerful features is its unified semantic layer, which bridges the traditional gap between IT and business teams. In most data warehouse environments, there's a disconnect: technical teams work with source tables and cryptic field names, while business teams just want to know “What were our top-selling products last quarter?”
Datasphere addresses this by allowing data owners to create reusable business entities with familiar names, hierarchies, KPIs, and dimensions within its Business Builder. These entities abstract the complexity of underlying data models and expose a consistent, governed view to users across the organization.
This means:
It’s not just about better dashboards — it's about data trust, transparency, and alignment across the organization.
According to industry benchmarks, organizations that lean on data-driven decision-making are 23 times more likely to acquire new customers, 6 times more likely to retain them, and 19 times more likely to operate profitably. Unified, consistent data models make that kind of performance possible.
Traditional data warehouses rely on batch processes. Data is extracted, transformed, loaded (ETL), and often refreshed once a day, in the best case. By the time it's available for analysis, it may already be outdated.
Built-in support for data federation and live connectivity enables real-time access to distributed data sources without duplicating or moving all your data into one central location.
Key benefits:
Why it matters:
Organizations that leverage analytics are more likely to make faster decisions and outperform peers in profitability. Whether you're monitoring supply chains, managing financials, or reacting to customer behavior, real-time access gives you a strategic edge with greater operational agility, proactive financial control, and more relevant customer engagement.
SAP Datasphere was designed to work seamlessly with the SAP ecosystem — not as a workaround but as a native part.
While third-party data warehouses often require custom connectors, third-party ETL tools, or significant transformation logic to understand SAP data structures, Datasphere natively understands the metadata, hierarchies, and semantics of SAP applications like S/4HANA, SAP ECC, and BW/4HANA.
Here’s why that matters:
On the analytics side, integration with SAP Analytics Cloud means users can build visualizations and predictive dashboards directly on top of Datasphere data models — again, without moving or duplicating anything.
This creates a unified analytics stack:
Businesses have reported a 20–30% boost in productivity by streamlining data retrieval and reporting processes, exactly the kind of workflow SAP Datasphere is built to enable.
Together, they deliver an enterprise-grade solution that supports real-time decision-making without compromising on data governance or trust.
While SAP Datasphere presents a compelling solution for modern data management, particularly for SAP-centric organizations, it has its challenges. Understanding these limitations is critical for setting realistic expectations, ensuring successful adoption, and aligning the platform with broader data strategy goals.
Despite SAP Datasphere’s positioning as a business-friendly platform, effective use still requires a foundational understanding of data modeling, semantic layering, and governance concepts.
Organizations may face:
Successful adoption demands not just tooling, but internal capability-building and strategic enablement programs.
One of SAP Datasphere’s core differentiators is its ability to federate data across systems in real time, minimizing the need for duplication.
Performance is inherently dependent on:
Companies should implement performance governance frameworks to ensure queries remain efficient, especially in distributed data environments. Without disciplined modeling, federated access can become a bottleneck rather than an accelerator.
Unlike Snowflake or Google BigQuery, SAP Datasphere operates within a more controlled and SAP-oriented ecosystem.
This can limit:
For companies leveraging broad data stacks, including Salesforce, AWS-native services, or open-source tooling, this may result in higher integration overhead or slower time to value.
While SAP Datasphere adopts a cloud-based consumption model, licensing remains non-trivial.
Pricing is influenced by a mix of:
Without active usage monitoring and architectural planning, organizations may face unexpected cost escalations. A clear governance model for provisioning, user access, and workload scaling is essential for sustainable cost control.
Datasphere handles data integration and modeling, but advanced analytics and dashboarding are primarily delivered through SAP Analytics Cloud. This creates a layered architecture that may be a strength or a limitation, depending on your analytics strategy.
Considerations include:
While SAP Analytics Cloud offers native synergy, organizations with existing BI investments should evaluate whether this dependency aligns with long-term goals.
As a relatively new and evolving platform, SAP Datasphere is undergoing continuous development. Some features, such as API extensibility, third-party data catalog integration, or advanced ML capabilities, are still maturing.
This presents potential limitations for:
SAP has committed to a robust roadmap, but organizations requiring advanced extensibility should validate current-state capabilities vs. roadmap timelines before making strategic bets.
SAP Datasphere is a sophisticated platform with clear strengths and equally clear contexts, where it shines brightest. It is a next-generation enterprise data fabric designed to connect, model, and govern data across complex SAP and non-SAP ecosystems. Its strengths lie not just in data storage but also in semantic consistency, real-time access, and cross-functional usability.
However, like any enterprise platform, its value depends heavily on context. Below is a deeper analysis of the types of organizations and specific business situations where SAP Datasphere provides measurable advantages.
Primary characteristics:
Why Datasphere fits: Datasphere is natively aware of SAP data structures. That means no reverse-engineering of hierarchies, key figure definitions, time dependencies, or currency conversions. It recognizes your business logic because it was built for it.
For companies looking to modernize their on-premise BW or ECC reporting environments, Datasphere provides:
This results in reduced development cycles, improved trust in reporting, and less friction between IT and business stakeholders.
Primary characteristics:
Why Datasphere fits: With support for federated data access, Datasphere can unify data from multiple systems without physically moving or duplicating everything. This is critical for:
Datasphere’s Spaces concept also allows for controlled decentralization; each line of business can manage its own datasets and models within guardrails set by IT.
Primary characteristics:
Why Datasphere fits: Thanks to real-time federation and live query support, Datasphere enables analytics on live operational data, not yesterday’s snapshot. This is a major advantage for industries where timing is everything:
Combined with SAP Analytics Cloud, users can build dashboards and alerts that reflect current business conditions, without nightly batch jobs or scheduled data loads.
Primary characteristics:
Why Datasphere fits: SAP Datasphere provides an environment where business users can explore and model data without deep technical skills, but within a governed framework defined by IT. This balance is made possible through:
This empowers departments to create their own dashboards and data views, while IT retains visibility and oversight.
Primary characteristics:
Why Datasphere fits: Datasphere is SAP’s strategic successor to legacy BW systems. While it’s not a one-to-one migration path, it offers:
It is particularly appealing for organizations undergoing S/4HANA transformation, as it aligns with SAP’s long-term product direction and deprecates older toolsets.
SAP Datasphere is not designed to be a generic lakehouse or a low-cost warehouse for every scenario. It may not be the right fit for:
In these cases, platforms like Snowflake, Databricks, or Google BigQuery may offer better tooling, community support, and ecosystem alignment.
Building a modern data platform is about connecting systems, aligning business logic, maintaining governance, and ensuring that your people don’t immediately revert to exporting everything into Excel.
That’s where LeverX comes in.
We’ve been in the SAP niche long enough to know where the real work begins and how to do it right. Rolling out SAP Datasphere for the first time, integrating it with your S/4HANA landscape, or migrating away from BW — we help you move faster, avoid common traps, and achieve meaningful results.
As enterprises accelerate their digital transformation efforts, the ability to integrate, govern, and analyze data across complex landscapes has become a strategic priority. SAP Datasphere offers a compelling solution for organizations looking to bridge the gap between IT and business, unify SAP and non-SAP data, and enable real-time, context-aware analytics without compromising governance.
Unlike traditional data warehouses, Datasphere isn’t just about storage but delivering trusted, business-aligned data at speed and scale. Its semantic modeling, native integration with SAP systems, and flexible architecture make it an ideal choice for enterprises navigating hybrid cloud environments.
That said, no platform is one-size-fits-all. While SAP Datasphere is a strong choice for SAP-centric organizations and those investing in business-user enablement, platforms like Snowflake, BigQuery, and Redshift may be more suitable for open, developer-led ecosystems and large-scale raw data processing.
Ultimately, the best solution depends on your strategic goals, your existing infrastructure, and how your organization defines success in the data space. For companies that prioritize semantic consistency, real-time insight, and SAP alignment, SAP Datasphere provides both a solid foundation and a practical path forward.
If you're not sure whether SAP Datasphere suits your business, contact LeverX for expert guidance on getting the most value from your SAP ecosystem.