Microsoft Fabric vs. Snowflake: Key Differences You Need to Know

Selecting the appropriate data platform becomes crucial as businesses depend more and more on data to inform their decisions. Although they take quite different approaches, Microsoft Fabric and Snowflake, two of the top players in the current data landscape, both provide strong capabilities. Understanding how these platforms compare can assist you in selecting the best option for your company, regardless of your role as a data engineer, business analyst, or decision-maker.

Real-World Example: The Dilemma of a Retail Giant

Take ShopSmart, a global retail chain that operates both online and offline. The company wants to combine its sales, inventory, and customer data in order to facilitate real-time reporting and predictive analytics. Two top platforms are being assessed by the IT team for this change.

Azure, Power BI, and Microsoft 365 are already widely used by ShopSmart, which is in line with Fabric’s integrated ecosystem. The alternative, however, provides more multi-cloud flexibility and strong performance on structured data. The group has to choose between selecting a more specialized warehousing solution with more deployment options or making use of its current Microsoft investments.

Let’s examine the differences between the two platforms.

What do you mean by Microsoft Fabric?

Microsoft Fabric is a unified, end-to-end data platform introduced by Microsoft to simplify analytics workloads for an organization. It incorporates elements from several Microsoft products working together, like Power BI, Azure Synapse Analytics, Data Factory, and OneLake, into a single SaaS experience.

Features:

• OneLake (a single, unified data lake)
• Integrated Data Engineering, Data Factory, Data Science, Real-Time Analytics, and Power BI
• Built-in Governance and Security using Microsoft Purview
• Deep Integration with Microsoft Ecosystem (365, Teams, Azure, Power Platform)
• SaaS model (infrastructure management is not required).

Fabric is meant for organizations looking for a single pane of glass across their data estate with seamless integration and a low learning curve for Microsoft users.

Next, we will see what Snowflake is

What is Snowflake?

Snowflake is a cloud-native platform for data warehouses that prioritizes collaboration, scalability, and performance. In contrast to conventional warehouses, it keeps computation and storage apart, allowing for cost-effectiveness and dynamic scaling. It provides real multi-cloud flexibility in its operations on AWS, Azure, and Google Cloud.

Its multi-cluster shared data architecture is one of its primary features.

• Data sharing across regions and clouds
• Native support for JSON, Parquet, and other structured and semi-structured data
• Robust query engine with performance optimization
• Wide-ranging ecosystem integrations using programs like Looker, Tableau, and DBT

For businesses seeking scalable performance, cloud-agnostic warehousing, and robust developer and ecosystem support, this is a good fit.

Now that you have a clear understanding of both the Tools, let’s take a look at the Architecture of both the tools

Microsoft Fabric vs. Snowflake: Architecture

Microsoft Fabric Architecture

Azure is the foundation of Microsoft Fabric, a Software-as-a-Service (SaaS) data platform. Data integration, data engineering, data warehousing, real-time analytics, data science, and business intelligence are among the analytics tasks it unifies into a single, cohesive interface. No matter the workload, Fabric stores all data on OneLake, a single, unified data lake built on the Delta Lake model. Since all of Fabric’s tools run natively on OneLake, real-time performance without data duplication is possible in Direct Lake mode.

Because of the architecture’s ability to abstract infrastructure complexity, users can focus solely on data workflows. Additionally, Fabric has deep integrations with Power BI for visualization and Microsoft Purview for governance, resulting in a smooth experience for both business users and data professionals.

Snowflake Architecture

The platform’s multi-cluster shared data architecture completely decouples computation and storage, enabling each to scale independently for increased concurrency and cost effectiveness. Additionally, it offers genuine multi-cloud flexibility by integrating easily with AWS, Azure, and GCP.

JSON, Avro, Parquet, and other structured and semi-structured data types are supported by the natively optimized proprietary format used by the cloud storage layer. Its on-demand virtual warehouses scale automatically to handle several workloads simultaneously. Collaborative analytics and external data exchange are further made possible by features like data clean rooms and secure sharing.

Now let’s compare the features of Microsoft Fabric and Snowflake.

Microsoft Fabric vs. Snowflake: Feature Comparison

1. Type of Platform

• Fabric is a completely managed software-as-a-service (SaaS) platform. It combines several data tools into a single user interface, including Power BI, Data Factory, Synapse, and OneLake. Infrastructure provisioning and management are not necessary because everything is accessible through a single portal.
• Snowflake is completely managed, but its main focus is on the data warehouse layer, and users need to integrate with other tools for BI, ML, or ETL.

Ideal for:

• Business-centric workflows involving fabric
• Snowflake = environments with a lot of developers and data engineers

2. Cloud support

• Microsoft Fabric: Works only on Microsoft Azure. It’s tightly integrated with the Azure ecosystem, which is splendid for organizations that already use Azure AD, Power BI, and other Microsoft services.
• Snowflake: Offers multi-cloud support, which is present on AWS, Azure, and Google Cloud. Store and query data across regions and cloud providers, which really matters for global enterprises and hybrid environments.

Ideal for:

• Fabric: Microsoft-centric organizations
• Snowflake: Multi-cloud flexibility seekers

3. Incorporating Data Lakes

• Microsoft Fabric: It is a one-stop data lake akin to OneDrive for the storage of files called OneLake. The storage is Delta Lake format standardized, and it supports Direct Lake access in Power BI (which is about real-time performance) so that all workloads can read/write natively to the lake.
• Snowflake: does not have its own data lake but works perfectly as an external lake along with S3, Azure Data Lake Storage (ADLS), and GCP buckets that can use external tables and Snowpipe to ingest semi-structured data.

Ideal for:

• Fabric makes the administration of data lakes much simpler; Snowflake provides flexible options for using external lakes.

4. Pricing and Calculation Model

• Microsoft Fabric: Shares compute across all workloads under an SKU (F2, F64, etc.) and employs a capacity-based pricing model. Instead of managing compute clusters, you manage capacity units. For large-scale batch jobs, this model might be less detailed, but it is predictable.
• Snowflake: Uses virtual warehouses and a pay-per-second computation model. Every workload launches a separate compute cluster that can grow or shrink on its own. This makes it possible to control performance and cost precisely.

Ideal for:

• Fabric – Enterprise Teams’ Simplicity
• Snowflake – Scalability and cost control for workloads involving a lot of engineering

5. Compute and Storage Separation

• Fabric from Microsoft: Although storage and computation are separated, Microsoft controls the abstraction. You pay more attention to workloads (like Lakehouse and Warehouse) than to computing infrastructure. Power BI’s Direct Lake mode is used to query files directly without loading data into memory.
• Snowflake: provides true storage-compute separation. To prevent resource contention, workloads can be isolated, and warehouses can be scaled independently of storage.

Ideal for:

• Fabric provides ease of use with built-in abstraction, while Snowflake offers more fine-grained control.

6. Capabilities for Data Sharing

• Microsoft Fabric: OneLake, Power BI workspaces, and Microsoft Entra (Azure AD) are the main internal channels for data sharing. In Microsoft environments, it’s great for safe collaboration.
• Snowflake: Offers natural data sharing across regions and clouds without data duplication. Secure cross-company collaboration is made possible by the Snowgrid and clean room features.

Ideal for:

• Snowflake works better when working with data from multiple organizations.

7. Integration of Business Intelligence (BI)

• Fabric from Microsoft: Business users can access real-time data without the need for ETL, thanks to its native integration with Power BI. With Direct Lake mode, semantic models, and visualization-first design, BI is integrated throughout the platform.
• Snowflake: Through connectors, supports third-party BI tools such as Tableau, Power BI, Looker, and others. The ability to visualize is not native.

Ideal for:

• Fabric –  End-to-end with BI integrated.
• Snowflake – Adaptable but dependent on external BI 

8. Analytics in Real Time

• Microsoft Fabric: Contains KQL (Kusto Query Language)-powered real-time analytics. Event Streams and Real-Time Hubs are excellent for managing streaming data from logs or the Internet of Things.
• Snowflake: Facilitates native streaming ingestion, Kafka connectors, and streaming via Snowpipe. Although it requires more work to set up and maintain streaming flows, it works better for semi-real-time pipelines.

Ideal for:

• Fabric is better for unconventional real-time analytics, particularly for Microsoft users.

9. Integration of AI and Machine Learning

• Microsoft Fabric: Connects to Notebooks, OpenAI APIs, and Azure Machine Learning. Data scientists can use Python or Spark environments to train and deploy models directly within Fabric.
• Snowflake: It supports third-party tools like AWS SageMaker, DataRobot, or Vertex AI, and it recently launched Snowpark for data science. More engineering setup is needed for integration.

Ideal for:

• Fabric – Easier for integrated ML workflows
• Snowflake – More flexible but engineering-heavy

10. Security & Governance of Data

• Microsoft Fabric: Provides centralized governance, lineage, sensitivity labels, and compliance management through its integration with Microsoft Purview. For access control, it is closely linked with Microsoft Entra (Azure AD).
• Snowflake: Although it provides tokenization, masking, and granular access control, it necessitates third-party tools (like Collibra and Alation) for complete governance visibility.

Ideal for:

• Fabric – Native governance 
• Snowflake – Strong, but needs additional equipment

We’ll see now. Does Microsoft Fabric have superiority over Snowflake?

Is Microsoft Fabric better than Snowflake?

It ultimately depends on your use case, team capabilities, and cloud strategy. Here’s a quick comparison:

Choose Fabric if:

• You need an all-in-one SaaS platform that combines Power BI, data engineering, real-time analytics, and machine learning.

• Your organization is already invested in the Microsoft ecosystem (e.g., Office 365, Power BI, Azure).

• You prefer low-code/no-code tools that enable faster adoption across both technical and business teams.

Go with Snowflake if:

• You require a multi-cloud data warehouse with advanced data sharing and cross-region support.

• Scalability and high performance are critical for your large-scale analytics workloads.

• You’re looking for a top-tier warehousing solution and already have a mature BI stack.

• You plan to use Python, UDFs, or Snowpark for custom processing within the platform.

Conclusion: 

• Microsoft Fabric = Microsoft-native & unified experience
• Snowflake = Scalable, cloud-independent computing and storage

Final Thoughts

Snowflake and Microsoft Fabric are both strong, but they have different purposes. Fabric excels in user experience, simplicity, and integration, particularly for businesses that already use Microsoft products. But Snowflake is a great option for more complicated or vendor-neutral data environments because it provides best-in-class performance, flexibility, and multi-cloud capabilities.

Fabric would be a logical choice for ShopSmart if its objective is quick integration and making the most of its current Microsoft stack. However, Snowflake might provide them with greater long-term flexibility if their approach incorporates a hybrid-cloud model and best-of-breed tools.

This blog post explains the main distinctions between these two in terms of architecture, features, integration, and use cases. We examine how each platform, from analytics and AI to data processing and storage, contributes in a different way to the contemporary data journey. Whether you’re comparing scalable cloud warehousing and unified analytics, this comparison will help you determine which option best suits your company’s requirements.

If you’re looking to upskill in Fabric and build a strong foundation in modern data engineering, Edureka’s Microsoft Fabric Data Engineer Associate Training (DP-700) is a great place to start. This course covers everything from working with OneLake and Lakehouse architecture to building data pipelines, managing workloads, and optimizing performance in Fabric. With hands-on labs, real-world scenarios, and guidance aligned with the official DP-700 certification, this program helps you gain the expertise needed for high-demand roles in data engineering and analytics.

Do you have any questions or need further information? Feel free to leave a comment below, and we’ll respond as soon as possible!

FAQs

1. Is it possible for one platform to completely replace another?

Not precisely. Fabric is a unified SaaS solution that combines real-time analytics, data engineering, and BI tools like Power BI, even though both provide data warehousing capabilities. Conversely, the alternative emphasizes robust multi-cloud flexibility and high-performance cloud warehousing.

2. Is it feasible to integrate the two?

Power Query connectors, which allow connections to external data sources, including this platform, are supported by Fabric. This enables users to directly visualize data in Power BI or ingest it into Lakehouse or Dataflows.

3. What is the best platform for real-time analytics?

It offers native real-time analytics through Kusto Query Language (KQL) and integrated streaming features. Snowflake supports Snowpipe streaming, but it usually requires additional setup and integration of external tools.

4. Is Snowflake more expensive than Microsoft Fabric?

This depends on use. Snowflake, which charges by the second for computing usage, offers granular control. The Fabric uses capacity-based pricing and pool computation. Whereas it may be more cost-effective with workloads optimized for accuracy and scalability, Fabric may be less costly for predictable costs.

5. Is Snowflake compatible with Power BI?

Yes, native connectors allow for a seamless integration between Power BI and Snowflake. A lot of companies use Power BI for visualization and Snowflake as the data backend.