Overview of Microsoft Fabric: Key Features, Limitations, and Use Cases

Released in 2023, Microsoft Fabric has quickly established itself as a strong contender in the market of data platforms. Specifically, as of mid-2025, it is the fastest-growing data platform in Microsoft’s history, with over 25,000 paying customers worldwide and a 55% year-over-year revenue growth. Moreover, Microsoft Fabric is already used by 70% of the Fortune 500 companies.

What drives Microsoft Fabric’s popularity? When is this platform the right choice? And what sets it apart from platforms for data storage, analytics, and business intelligence (BI)?

In this article, we provide a comprehensive Microsoft Fabric overview with a focus on its strengths, weaknesses, and preferred use cases.

Microsoft Fabric is a cloud-based data platform that provides data management and analytics features for both business-oriented specialists and data scientists. It supports diverse workflows, including data science, real-time analytics, and business intelligence.

A key advantage of Fabric is its ability to unify Microsoft Azure’s data storage, processing, and management services within a single platform.

The image below illustrates Microsoft Fabric’s core functionality.

The key concept behind Fabric’s design is simplicity. The platform provides a convenient interface that allows businesses to combine data from several sources into a single environment. By using a unified platform rather than multiple disconnected tools, companies can reduce complexity, eliminate data silos, and save the time otherwise spent integrating separate services.

The most important strengths of Microsoft Fabric are tied to its core functionality, such as unified management for data storage, analytics, and BI services, AI/ML development capabilities, and the platform’s lake-centric and open design. Below is a detailed overview of the key advantages of the platform.

To make data analytics and other data-related processes truly convenient, Microsoft Fabric provides a unified solution that combines several supporting systems, namely:

• OneLake. Centralized data lake for unified storage, management, and governance.
• Azure Data Factory for Microsoft Fabric. Built-in data integration solution for managing ETL (extract, load, transform) and ELT (extract, transform, load) processes and workflow orchestration
• Azure Synapse for Microsoft Fabric. Service applied for Microsoft Fabric data warehousing and analytics
• Power BI for Microsoft Fabric. An embedded tool for business intelligence and data visualization
• Apache Spark for Microsoft Fabric. Scalable engine for processing large datasets and machine learning workflows
• Copilot in Microsoft Fabric. Built-in AI assistant that helps with querying, insights, and automated analytics

Additionally, Microsoft Fabric seamlessly integrates with a wide range of tools from the Microsoft ecosystem, as well as external platforms. These include services such as Dataverse, Azure Machine Learning, GitHub, and Azure DevOps, among others.

With its unified interface for managing diverse services, Microsoft Fabric simplifies the integration of multiple products and enhances data management convenience. In addition, the platform’s UI constantly evolves, providing for a better user experience. For example, the platform has recently introduced a flat list view over deployment pipelines, which enables greater clarity and transparency while managing Fabric’s workspace items.

Gartner predicts that by 2027, 50% of business decisions will be augmented or automated by AI agents for decision intelligence. Therefore, strong support for AI development and management capabilities is a must-have feature for a modern data platform.

This is precisely what Microsoft Fabric delivers. The platform integrates with Azure OpenAI, which allows developers to apply the power of generative AI to data. You can configure custom AI algorithms for data discovery, organization, and processing. The integration with Microsoft Copilot allows Microsoft Fabric users to build custom machine learning models, develop data flows and data pipelines, generate code, and visualize results by using simple conversational language.

Traditionally, services such as Synapse Analytics, Data Factory, Power BI, and Data Lake are priced and managed separately in Azure. Microsoft Fabric bundles many of these capabilities under a single license, which allows you to track a unified price for most services.

Prices in Microsoft Fabric are based on Capacity Units (CUs), the basic units of compute and resource allocation for the platform. You can choose between two billing options:

• Pay-as-you-go model. Here you pay only for the resources you actually use, like compute or storage, without long-term commitments. With this model, two Capacity Units will cost you $262.80/month.
• The reserved capacity model. You commit to a certain amount of resources for a set period (typically one or three years) in exchange for more predictable pricing and discounts that ensure ~41% savings. With a reserved capacity, two Capacity Units will cost you $156.334/month.

With Microsoft Fabric, you simplify purchasing because you pay for a single license that covers compute resources for most workloads, instead of subscribing to multiple separate cloud services.The platform is convenient to manage, which allows you to apply the same Capacity Units wherever needed without pre-allocation, which ensures that resources are not locked in idle workloads. Microsoft Fabric also allows you to automatically scale compute capacity up or down based on demand and offers you full transparency into usage and costs. Such functionality for enhanced cost allocation and waste optimization allows you to reduce compute usage, making a data platform more cost-efficient.

Meanwhile, certain tools within the Fabric platform are billed separately. For instance, Microsoft recently introduced Autoscale Billing for Apache Spark, enabling users to manage Spark workloads and their costs independently of the general Fabric capacity. From now on, data engineers using Aparch Spark within the Microsoft Fabric platform can temporarily disable specific jobs or prioritize other jobs. This helps them tailor Spark’s resource consumption to their organization’s workload patterns. This update ensures greater flexibility, transparency, and cost efficiency for Spark workloads, which are often resource-intensive and difficult to monitor in terms of costs.

Microsoft Fabric stores data in the data lake, which is a centralized repository for structured, semi-structured, and unstructured data. Such a design comes with both advantages and disadvantages. On the one hand, a data lake allows organizations to store diverse types of data in a single, scalable repository, which brings great flexibility and allows companies to ensure that no critical data is lost. On the other hand, due to the great data diversity, a data lake needs proper governance and organization in order not to become a “data swamp” with poorly-organized and poorly-searchable information.

Microsoft Fabric has a solution to this problem, as the platform introduces a built-in software as a service SaaS, multi-cloud data lake, namely OneLake. With OneLake, the entire team gets a single and unified storage for data, which allows companies to eliminate data silos. In addition, OneLake provides its users with a unified interface for data governance, metadata management, and lineage. This helps teams prevent the chaos often seen in unmanaged data lakes.

While Microsoft Fabric offers businesses a solid list of benefits, the platform has several weaknesses that you should consider. Here we list some of them.

Built to cover a wide range of database management features, Microsoft Fabric has limited or no support for some data workflows. These are Fabric’s most significant functionality limitations:

• Lack of support for temporary tables. This issue can limit the flexibility for analysts and data engineers who may need to create temporary tables for quick and ad hoc data manipulations that don’t alter production datasets.
• Lack of support for clustered and non-clustered indexes. This issue can slow down the query performance on large datasets, leading to delays in analytics.
• Lack of support for primary or foreign keys. Without these features, Microsoft Fabric cannot automatically enforce data integrity, which can result in inconsistent or duplicate records.
• Limited transaction support. This issue can lead to inconsistent data updates, potentially compromising data quality.

The above-mentioned problems may be impactful, but most of the aforementioned limitations can be addressed with the right approach. In particular, data scientists can optimize queries and data updates, as well as enforce data integrity with manual practices.

Although Fabric shows strong overall performance, it does have certain limitations, largely due to its reliance on Capacity Units. Every subscription comes with a set limit on the number of CUs that can be allocated. Once those Capacity Units are fully consumed, Fabric performance may slow down sufficiently. This may cause bottlenecks during peak performance periods. If you are using a pay-as-you-go model, you will need to purchase extra units during spikes.

Meanwhile, if resource consumption exceeds the reserved amount in the reserved capacity model, you will need to upgrade to a higher reservation tier. In both cases, the problem can cause you to exceed your budget. To address this issue, it is important to plan expenses thoroughly, anticipating your needs and demand spikes.

Microsoft Fabric is designed for simplicity, control, and efficient integration. However, this design also introduces certain limitations. Advanced data engineers and architects may find Fabric’s approach somewhat restrictive, as it provides limited capabilities for directly configuring infrastructure, fine-tuning resource allocation, or optimizing runtime performance.

These limitations mainly affect scenarios that require highly specialized performance tuning, complex query optimization, or custom workflows that push the limits of compute and storage capabilities. In most scenarios, such issues won’t have a sufficient business impact. In addition, Fabric’s limitations can often be mitigated by integrating it with other tools for advanced use cases. For example, data engineers can use Azure Databricks for powering highly customized ML pipelines while using Microsoft Fabric as the central platform for analytics and reporting.

Let’s compare Microsoft Fabric’s critical features and properties against other major contenders in the market of data platforms.

Overall, compared to other major data platforms, Microsoft Fabric stands out as a universal solution offering a comprehensive suite of data processing and management capabilities, tightly integrated with the Microsoft Azure stack.

With a variety of benefits and several weaknesses that can be mitigated with the right approach, Microsoft Fabric is an efficient solution for a wide range of use cases. The data platform can become an ideal choice for companies that need:

• A lake-centric data platform to replace fragmented on-premises or legacy data warehouses with a cloud-native, scalable platform
• Real-time analytics that can be efficiently organized through native integration with Power BI
• Azure AI services for building and managing ML models
• Centralized governance and security management for diverse data, which can be achieved with OneLake

Fabric is designed with consideration of the data market’s main challenges and demands. The image below highlights the key trends in data, business intelligence, and analytics, based on their perceived importance by data users, vendors, and consultants.

As a secure platform with convenient functionality for cross-functional data management and governance, as well as integration with services for advanced analytics and AI/ML development, Fabric provides a comprehensive and reliable solution. However, there are certain scenarios in which Fabric’s advantages over other data platforms are especially relevant.

Let’s explore these Microsoft Fabric use cases in more detail.

If you want to establish a corporate culture where data is available for cross-department collaboration, Microsoft Fabric proves to be an efficient solution. In particular, it fits a data fabric architecture— an architectural approach that provides a unified, intelligent, and governed layer connecting data across an organization. Microsoft Fabric provides strong support for the data fabric software architecture because it consolidates diverse data workflows, such as engineering, analytics, and AI development, within a single platform.

By implementing Microsoft Fabric, you can break down data silos and promote interoperability across an organization. To ensure data reliability, integrity, and auditability across business units, you can use Fabric’s features like built-in governance, security, and lineage tracking.

Overall, Microsoft Fabric provides capabilities for establishing a scalable, consistent, and connected data environment with standardized analytics and data formats. This reduces friction between technical and business teams and improves alignment on data-driven strategies.

By automating workflows and reducing reliance on multiple tools, Microsoft Fabric allows teams to deliver insights faster. As noted earlier, it enables the management of diverse workflows and services through a unified interface designed for user convenience. Instead of spending time learning separate systems, handling multiple logins, or manually moving data across platforms, analysts can access storage, processing, visualization, and governance features in one place, which can accelerate the workflow.

The platform also offers built-in features for security and data lineage that are accessible to users without technical expertise. As a result, team members can leverage analytical capabilities of a data platform without involving data engineers, which can ultimately accelerate time-to-insight.

One of Microsoft Fabric’s key strengths lies in its tight integration with the broader Microsoft ecosystem, including Power BI, Azure, and Microsoft 365. This connectivity enables organizations to unify their data workflows, analytics, and collaboration tools without the friction that often comes with connecting disparate platforms.

For example, imagine a retail company that uses Microsoft 365 for collaboration, Azure SQL for its transactional databases, and Power BI for reporting. By adopting Microsoft Fabric, the company can directly pull sales and inventory data from Azure SQL into Fabric’s data pipelines, transform it with built-in AI and analytics tools, and automatically publish insights to Power BI dashboards.

Whether your organization has previously relied on Microsoft services or is building a Microsoft-powered ecosystem from scratch, Fabric offers a unified system. It ensures seamless data flows, consistent formats, and standardized billing and licensing. These benefits translate into improved efficiency and easier system management.

Every organization has unique data sources, workflows, compliance obligations, and performance needs, which makes tailoring the Microsoft Fabric platform far more than a straightforward installation. To ensure the platform delivers maximum business value, it is essential to rely on experienced data engineers who understand both the technical capabilities of Fabric and the nuances of your organization’s data strategy.

At Leobit, we are ready to help. Our experience from delivering over 45 Azure projects has equipped us with strong expertise in managing and configuring Azure cloud environments. We also have significant AI expertise, as our software engineers are skilled in configuring AI-powered analytics and insights, AI and machine learning integration, and custom LLM development. Our team is also skilled in setting up data analytics and BI workflows, providing a range of business intelligence development services.

For example, we developed advanced BI forecasting workflows for a global e-commerce platform. Our data engineers also configured complex analytical algorithms for a multi-module payment processing system. We also apply advanced data management practices to empower our AI-based lead scoring and proposal generation solution for the sales team.

Microsoft Fabric is not a one-size-fits-all solution, but it offers clear value for organizations seeking a unified, Microsoft-native data platform. Its ease of use, integration with the Microsoft ecosystem, and simplified pricing model make it an efficient tool for organizations that prioritize cross-functional collaboration and fast time-to-insight, as well as want to rely on a Microsoft-based ecosystem.

While configuring and setting up Microsoft Fabric environments can cause some challenges, a team with a strong cloud and BI expertise can help you get an efficient and business-tailored system.

Whether you need help with optimizing your Fabric-based system or want experts to set up your data infrastructure from scratch, contact us and let’s boost your analytics, data management, and business intelligence capabilities.