In the first part of this series, we explored why so much SAP data remains locked away, despite these systems being a treasure trove of business-critical information. While recent innovations have made it easier than ever to get your hands on SAP data, extracting data is only the first step. The real challenge is coming up with ways to transform all this data into actionable insights.
That’s where Microsoft Fabric comes in—a modern, unified data platform built to break down silos and empower decision-making. Unlike traditional SAP data tools like SAP BW or SAP Analytics Cloud, Fabric isn’t limited to a specific function like warehousing or visualization. Instead, it combines data integration, engineering, and advanced analytics into an all-in-one flexible platform that's open for a broad audience of data consumer types.
In this article, we’ll explore what sets Microsoft Fabric apart in the marketplace and why it’s a game-changer for data management. We’ll take a closer look at its next-level features that go beyond the limitations of traditional data tools. We’ll also compare its capabilities to familiar SAP data products. Finally, we’ll wrap things up by examining why Fabric is the perfect platform for unlocking valuable insights from your SAP data. Let’s dive in!
Microsoft Fabric Overview
Microsoft Fabric is positioned as an all-in-one data platform that's designed to address the diverse needs of modern businesses. As such, it brings everything you need to manage, analyze, and act on your data—all in one place. Compared to the typical "best-of-breeds" style data environments that normally orbit SAP landscape, Fabric provides a one-stop shop for data consumers of all types to unlock the full potential of your SAP data.
In this section, we’ll take a high-level look at the individual services and capabilities that make up Microsoft Fabric. From core services like OneLake, the central data lake that unifies your data storage, to advanced data science environments and real-time intelligence features, Fabric provides the flexibility and scalability to handle a wide range of workloads. Whether you’re integrating data, building AI models, or analyzing streaming data, Fabric delivers the tools and infrastructure to make it happen.
Let’s dive into the core components of Microsoft Fabric and see how each one contributes to creating a seamless, end-to-end data platform.
Figure 1: Looking at the Big Picture with Microsoft Fabric
OneLake: Data Lakes & Data Warehouses Unite
OneLake builds on the power of open standards like Delta Lake and Apache Iceberg to provide a unified and flexible approach to data storage. By storing data in common, widely supported formats, OneLake ensures seamless compatibility with a variety of processing engines, enabling effortless integration across diverse workloads. This separation of concerns—keeping storage and processing engines distinct—gives you the flexibility to manage data warehouses, lakehouses, and data lakes, all under one roof.
Figure 2: OneLake: The OneDrive for Your (Enterprise) Data
As you can see in Figure 2, OneLake can also (virtually) manage data coming from external data sources including Amazon S3 buckets, Google Cloud Storage, and other data lake storage services. Here, it's worth noting that no data replication/mirroring is required to access this data. With OneLake's unified file system concept, these external sources are accessed via shortcuts.
At runtime, Fabric Data Warehouse leverages the massively parallel processing (MPP) capabilities of Azure Synapse Analytics to deliver scalable, high-performance data warehousing. Designed for speed and reliability, it’s the perfect solution for managing complex analytics workloads, even at scale.
With OneLake as the backbone, you're not locked into one way of working. For example, Power BI report developers can seamlessly connect to Fabric Data Warehouse for structured analytics, while data scientists can analyze the same data using tools like Apache Spark, ensuring each team has the tools they need without compromising collaboration or accessibility.
Serving as the central data lake for your organization, OneLake provides reliable, scalable, and highly cost-effective cloud-based storage for all your workloads. Whether you're building data warehouses for structured reporting, leveraging lakehouses for hybrid use cases, or scaling up with traditional data lakes, OneLake ensures your data is always accessible, consistent, and optimized for action—no matter what your use case is.
ETL & Data Engineering
Fabric’s data engineering tools give you everything you need to integrate, transform, and manage your data, starting with Data Factory. Built on Microsoft's Azure Data Factory service, it offers serverless data integration services to create ETL/ELT pipelines and data flows. What really sets it apart is its low-code development tools (built on Power Query), which make building complex workflows faster and easier—even if you’re not a coding expert.
For seasoned data engineers, Fabric takes the experience up a notch by providing access to advanced tools designed for more demanding workloads. With Fabric Data Engineering, you get access to a couple of notable Python-based tools:
PySpark Notebooks: For experienced Spark developers, Fabric notebooks provide you with direct access to a Fabric-managed Apache Spark container pool, enabling you jump right in and start tackling large-scale data processing and analytics tasks.
Data Wrangler: This AI (Copilot)-powered Python code generator is great for data preparation tasks using familiar APIs like DataFrames, etc. See this link for more details.
Figure 3: Working with the Data Wrangler Tool
Collectively, these tools strike the perfect balance between ease of use and advanced functionality. Whether you’re building pipelines, running Spark jobs, or managing lakehouses, Fabric’s data engineering suite is designed to help you handle it all—quickly, efficiently, and at scale.
Data Science & AI Development
Fabric Data Science delivers a versatile workspace that's tailored to meet the needs of both experienced data scientists and business analysts. This comprehensive environment supports every stage of the data science and AI development lifecycle, providing tools for experimentation, AI model training, and advanced solution development.
Fabric Data Science supports a wide range of data science workflows, from simple exploratory data analysis to highly customized machine learning pipelines. With built-in support for open standard tools like PySpark notebooks, Python, and Spark, Fabric enables data scientists to work with their preferred tools while leveraging the platform’s power to process and analyze large datasets efficiently. For SAP data analysis, this is huge as you don't have to go through the headache of spinning up large data appliances to get started. This makes it easier to iterate quickly, test hypotheses, and refine models without being bogged down by infrastructure limitations.
Figure 4: Fabric Data Science and AI Workflow Concepts
Beyond the built-in AI/ML features including with Apache Spark, it's also worth noting that Fabric Data Science integrates deeply with Azure AI Services, extending its capabilities far beyond traditional data science platforms. For example, data scientists can use Fabric to build and train machine learning models and then deploy those models directly into production with Azure AI.
When combined with Fabric data engineering services, this integration supports the development of highly sophisticated AI development pipelines that incorporate advanced features such as natural language processing, computer vision, and predictive analytics—all seamlessly connected to Fabric’s underlying data sources. By bridging the gap between data science and AI deployment, Fabric accelerates the time-to-value for AI initiatives.
Perhaps most importantly, Fabric Data Science is built for collaboration. It allows teams to share notebooks, code, and results in a unified workspace, making it easier for data scientists, analysts, and engineers to work together. This collaborative environment ensures that everyone, from technical experts to business stakeholders, can contribute to and benefit from AI-driven solutions.
Real-Time Intelligence
Fabric Real-Time Intelligence changes the game for streaming and event data by making it easy to process and analyze information as it happens. Unlike traditional batch processing scenarios, Real-Time Intelligence ensures that you’re always working with up-to-the-minute insights, enabling faster, smarter decision-making.
Collectively, these features make it possible to create a digital nerve center within your business. With Real-Time Intelligence, you can define business rules to monitor key performance indicators (KPIs) and trigger specific actions based on those metrics. Whether it’s sending alerts, updating dashboards, or initiating low-code workflows, this functionality ensures your business can respond instantly to changes. See this blog article to see firsthand how this works.
In an SAP setting, Real-Time Intelligence can be used to support some powerful use cases:
Monitoring equipment for predictive maintenance
Tracking customer behavior for personalized engagement
Monitoring production or logistics operations in real-time using real-time dashboards
As is the case with all Fabric services, Real-Time Intelligence works seamlessly with the entire Microsoft Fabric ecosystem. It combines real-time streaming with historical data for a complete picture, breaking down silos and enhancing decision-making. Plus, with its scalability and performance, you can handle even the most demanding workloads. With Fabric Real-Time Intelligence, you’re not just reacting to events—you’re staying ahead of them.
Power BI Integration
Fabric integrates Microsoft's industry-leading Power BI tool to visualize data in reports and dashboards. Since it's been reported that up to 95% of businesses run Power BI, not much explanation is needed here other than to say that Power BI naturally supports deep integration with Fabric data sources.
Figure 5: Building Reports & Dashboards Using Power BI & Fabric
Although the standalone Power BI product has long supported integration with SAP data sources (as I covered here), there are some notable performance benefits to be gained by integrating your SAP data into OneLake and then using the new direct lake mode for Power BI in Fabric to consume/visualize it. This is particularly true in situations where you're dealing with enormous SAP data tables like MARA or VBAK.
Another benefit to Power BI in Fabric is that you have access to more advanced features of Microsoft Copilot. Here, you can into some very powerful generative AI technology to analyze your SAP data and even have it generate reports and other Power BI-related artifacts automagically.
Industry Solutions
As Fabric continues to mature, Microsoft is working with customers and partners to develop pre-built analytics packages that are tailored for specific industries. These template solutions can be easily adapted to work with data from specific data landscapes (specifically SAP). See the short video below for more details.
Purview & Data Governance
Although it's not technically included with Microsoft Fabric, we would be remiss if we didn't at least touch on some of the advanced data governance capabilities of Microsoft Purview. After all, with all the concerns around data privacy and cybersecurity, it's only natural for SAP product owners to have anxiety about managing their SAP data within Fabric.
While Fabric (and the underlying Microsoft Azure platform) provides many built-in security and governance features, Purview takes data governance to a whole other level. Some notable features here include the following:
The management of a robust, dynamically-generated, and searchable data catalog complete with traceable data lineages.
Support for the development of data products that can be leveraged to unlock self-service access scenarios.
Configuration and enforcement of data quality rules and other related health controls.
In order to get the most out of your SAP data, you need to empower multi-disciplinary teams that understand the data at different dimensions to collaborate with one another. With Purview, you can build a robust data culture without having to compromise on security. See the video link below for a deeper dive on this topic.
Comparing Fabric to SAP Data Products
In the highly competitive landscape of data products, both SAP and Microsoft aim to help organizations unlock the value of their data—they just take different approaches to achieving this goal. While SAP’s tools are primarily purpose-built to extend the capabilities of its ERP/business application ecosystem, Microsoft Fabric provides a unified, cloud-native platform that's designed to work equally well with SAP and non-SAP data sources.
Table 1 provides a detailed comparison of prominent SAP data products and their Microsoft Fabric equivalents. While SAP does not have equivalent solutions to match up with everything in Fabric, hopefully this table helps you understand how Fabric can be positioned within both your current and future data landscape.
Table 1: Comparing SAP Data Products to Microsoft/Fabric Products and Services
Why Fabric & SAP Makes Sense
This section could have just as easily been titled "Why Not Just Stick with SAP?". Of course, it’s a valid question—after all, SAP has been a trusted enterprise solution for decades, and its data tools like Datasphere and SAC are solid options for many use cases.
So, if you happen to find yourself at that data platform crossroads, here are a few compelling reasons why Fabric is a smart bet for you to get the most out of your data estate long term.
Fabric's Diversity & Scale
When it comes to modern data analysis, the reality is that SAP alone isn’t enough. Success in today’s data-driven world requires access to a wide variety of data sources: SaaS applications, external industry datasets, streaming/event data, and even unstructured data from data lakes. SAP’s ecosystem, while powerful within its own boundaries, simply wasn’t designed to handle this level of diversity and scale.
SAP Datasphere is a welcome upgrade to legacy tools like SAP BW, providing more flexibility and cloud capabilities. However, its features and scope still pale in comparison to Microsoft Fabric. Fabric offers a unified, cloud-native platform that brings together data integration, warehousing, analytics, and AI, all in one seamless environment.
Collaborative Data Analysis
While tools like SAC (and Lumira before it) have introduced some level of self-service access for business analysts, SAP data has historically been tightly controlled by corporate data teams. This centralized approach once made sense, given the complexity of SAP data models and the security and governance limitations of legacy BW/BI tools. However, in today’s data-driven world, this model is far too restrictive.
These days, the sheer volume and variety of data flowing into modern enterprises make it impossible for any one team to manage—let alone draw insights from. To truly harness the power of your enterprise data, you need a platform that can empower a broader range of users to access, analyze, and act on data without bottlenecks.
Figure 6: Achieving BI Nirvana Through Collaboration
Fabric takes collaborative data analysis to a whole new level, allowing cross-functional teams to work together in a secure and effective manner. With built-in support for Power BI, Azure AI, and advanced data engineering tools, the bottom line is that Fabric empowers you to turn your data into insights faster and more efficiently than SAP’s solutions can.
Fabric's Growth Trajectory
While SAP has been (and continues to be) a significant player in the data space, the reality is that data is not their bread and butter. Rather, SAP data products are primarily positioned as tools/extensions that complement its first-class ERP and business application product suite.
Microsoft, on the other hand, is firmly established in the space and is investing heavily in innovation. With Fabric, Microsoft is creating a comprehensive solution designed to meet the needs of businesses navigating increasingly complex data landscapes. Once you get your SAP data into OneLake, it's truly a choose your own adventure scenario from there forward.
Figure 7: Microsoft R&D Investment vs. SAP
Closing Thoughts
Hopefully, this whirlwind tour through Microsoft Fabric filled in some knowledge gaps in terms of how it's positioned (and gave you a clearer picture of how it stacks up against SAP’s data tools). We’ve explored how Fabric’s unified, cloud-native approach provides a robust alternative to traditional SAP solutions like Datasphere and SAC, breaking down silos and enabling collaborative, real-time, and AI-driven analytics. With features like OneLake for centralized storage, advanced data engineering tools, and seamless integration with Azure AI, Fabric is designed to address the needs of modern enterprises in ways SAP’s tools alone cannot.
By complementing SAP’s operational strengths with Fabric’s expansive capabilities, businesses can build a data strategy with Fabric that’s as flexible as it is powerful. At the end of the day, it’s not about replacing SAP—it’s about driving more value out of your existing SAP investment(s) by unifying diverse data sources, empowering users across teams, and unlocking insights faster than ever. As we continue this series, we’ll dive even deeper into Fabric’s advanced features, exploring practical use cases for real-time intelligence, data science, and AI model development. Stay tuned!
