Snowflake VS Microsoft Fabric

Soumak Das
3 min readJul 20, 2023

Microsoft Fabric and Snowflake are both data platforms that cater to enterprise-level customers, but they have distinct differences in their architecture and functionality. Here’s a brief comparison of the two:

  1. Architecture:

Microsoft Fabric: Microsoft Fabric, also known as Azure Service Fabric, is a distributed systems platform designed for building scalable and reliable cloud-based applications. It provides tools and services to deploy, manage, and monitor microservices-based applications across clusters of machines. Fabric enables enterprises to develop and run highly available and elastic applications.

  • Snowflake: Snowflake is a cloud-based data warehousing platform that focuses on data storage, processing, and analytics. It follows a unique architecture known as Multi-Cluster, Shared Data Architecture, which separates storage and compute layers, allowing users to scale each independently. This approach ensures elasticity and performance for data processing.

2. Data Model:

  • Microsoft Fabric: As a platform for building applications, Microsoft Fabric does not specifically focus on data warehousing or analytics. It provides the foundation for deploying and managing applications, but it does not offer built-in data warehousing capabilities.
  • Snowflake: Snowflake is primarily designed for data warehousing and analytics. It supports structured and semi-structured data, making it suitable for handling large datasets and complex queries.

3. Scalability:

  • Microsoft Fabric: Fabric is built to handle the scaling of application components. It enables automatic scaling of microservices to meet changing demand, making it suitable for applications with varying workloads.
  • Snowflake: Snowflake’s architecture allows for seamless and independent scaling of compute and storage resources. This flexibility ensures optimal performance for data processing tasks, irrespective of data volume or complexity.

4. Ease of Use:

  • Microsoft Fabric: Fabric is a more complex platform, targeting developers and organizations with the expertise to build and manage distributed applications.
  • Snowflake: Snowflake is designed to be user-friendly, with SQL-based querying and easy-to-use interfaces, making it accessible to data analysts and business users.

5. Impact on Enterprise-Level Customers and Future Business Implications:

a. For Enterprise-Level Customers:

  • Microsoft Fabric: Enterprises with a need for building and deploying scalable, distributed applications can benefit from Microsoft Fabric. It provides the infrastructure and tools to manage complex microservices architectures efficiently.
  • Snowflake: Enterprises dealing with large volumes of data and requiring sophisticated data analytics capabilities can leverage Snowflake’s data warehousing platform. Its multi-cluster architecture allows for efficient scaling and processing of data.

b. Future Business Implications:

  • Microsoft Fabric: As cloud-based microservices architectures become more popular, Microsoft Fabric can play a significant role in enabling organizations to build and manage modern, scalable applications. Its impact will likely be in improving application performance and resilience.
  • Snowflake: The demand for data analytics and warehousing solutions is expected to grow as businesses rely more on data-driven decision-making. Snowflake’s scalable architecture can help organizations process vast amounts of data efficiently, leading to better insights and enhanced business outcomes.

In conclusion, while Microsoft Fabric and Snowflake serve different purposes, they both address critical needs of enterprise-level customers. Microsoft Fabric empowers developers to build scalable applications, while Snowflake provides a robust data warehousing and analytics platform. The success of each platform will depend on the specific requirements and strategies of businesses, leading to their respective impacts on the future of the enterprise software landscape.



Soumak Das

Sr. Data Engineer @EY & Snowflake/Airflow/Databricks/AWS writer