Microsoft Fabric vs Qlik Cloud Choosing the Right Data Analytics Fabric

Microsoft Fabric vs Qlik Cloud : In the dynamic landscape of data analytics platforms, selecting the right tool for your organization is a pivotal decision. In this post, we’ll conduct an in-depth exploration of two key contenders: Microsoft Fabric and Qlik Cloud. Through a detailed feature-by-feature comparison, a comprehensive comparison table, external links for further exploration, and FAQs, we aim to provide a comprehensive guide for organizations navigating the intricate realm of data analytics.

What is Microsoft Fabric

Microsoft Fabric stands as a holistic analytics solution designed for enterprises, encompassing a spectrum of functionalities spanning data movement, data science, real-time analytics, and business intelligence. This all-inclusive platform provides a comprehensive suite of services, consolidating data lake management, data engineering, and data integration into a unified and seamless experience.

What is Qlik cloud

Qlik Cloud is a cloud-based analytics platform offered by Qlik, a leading provider of business intelligence and data visualization solutions. Qlik Cloud enables users to access, analyze, and visualize data from various sources in the cloud. It provides a range of features, including data preparation, interactive dashboards, and collaboration tools, allowing businesses to make informed decisions based on real-time insights. With a focus on self-service analytics, Qlik Cloud empowers users to explore and understand their data, fostering a more data-driven and collaborative approach to decision-making.

Comparison table for Microsoft Fabric vs Qlik Cloud

Feature Microsoft Fabric Qlik Cloud
Data Integration & Preparation Leverages Azure Data Factory for robust data orchestration, transformation, and loading. Offers seamless integration with other Azure services, including Power BI and Azure Databricks. Boasts pre-built connectors for various data sources and provides data cleansing and transformation capabilities within the platform.
Analytics & Visualization Integrates with Power BI for interactive visuals and reports. Additionally, Azure Machine Learning provides advanced analytical and AI capabilities. Offers native visual exploration with its associative engine, enabling self-service analytics and data storytelling.
Collaboration & Governance Utilizes Azure role-based access control (RBAC) for granular security and enables seamless collaboration within workspaces. Provides flexible user roles and granular access control. Collaboration features include shared workspaces and commenting on visualizations.
Deployment & Scalability Deployed entirely on Microsoft Azure, offering scalability and integration with other Azure services. Multi-cloud deployment option available, including Azure, AWS, and Google Cloud Platform.
Pricing & Licensing Pay-as-you-go model based on resource consumption. Requires additional licenses for Power BI and Azure Machine Learning. Subscription-based pricing depending on user count and features required.

Choosing the tool Microsoft Fabric vs Qlik Cloud

Choosing the champion depends on your specific needs and existing infrastructure.

Microsoft Fabric shines for:

  • Organizations deeply invested in the Microsoft ecosystem.
  • Enterprises requiring advanced data engineering and machine learning capabilities.
  • Scenarios demanding tight integration with other Azure services like Power BI and Data Factory.

Qlik Cloud takes the stage for:

  • Businesses needing self-service analytics and intuitive data exploration.
  • Organizations with multi-cloud deployment preferences.
  • Companies dealing with complex data sources requiring pre-built connectors.

Pros and Cons of Microsoft Fabric vs Qlik Cloud

When deciding on a data analytics platform, understanding the strengths and weaknesses of each option is paramount. Let’s break down the pros and cons of Microsoft Fabric and Qlik Cloud to guide you in making an informed decision:

Microsoft Fabric


  1. Deep Integration with Azure:
    • Seamlessly collaborates with other Azure services like Power BI, Data Factory, and Databricks, ensuring a unified data management and analytics experience.
  2. Advanced Data Engineering:
    • Boasts robust data orchestration, transformation, and loading capabilities through Azure Data Factory, catering to intricate data engineering needs.
  3. Strong AI and Machine Learning Integration:
    • Leverages Azure Machine Learning, providing advanced analytics and predictive modeling capabilities.
  4. Scalability and Reliability:
    • Backed by the extensive resources of Microsoft Azure, ensuring scalability and reliable uptime.
  5. Security and Governance:
    • Utilizes Azure role-based access control (RBAC) for granular security and effective data governance.


  1. Limited Deployment Options:
    • Only available on Microsoft Azure, which may limit flexibility for organizations preferring multi-cloud environments.
  2. Steeper Learning Curve:
    • Requires familiarity with Azure services and tools, potentially posing challenges for beginners.
  3. Complex Pricing Structure:
    • Pay-as-you-go model may be challenging to predict and manage, leading to potential cost-related complexities.
  4. Heavier Focus on Technical Users:
    • More tailored for organizations with dedicated data engineering and analytics teams, potentially excluding less technical users.

Qlik Cloud


  1. Self-Service Analytics:
    • User-friendly interface and associative engine empower business users to independently explore data, promoting self-service analytics.
  2. Multi-Cloud Deployment:
    • Offers flexibility by allowing deployment on Azure, AWS, or Google Cloud Platform, catering to diverse organizational preferences.
  3. Wide Range of Data Connectors:
    • Provides pre-built connectors for various data sources, including SAP and Mainframe systems, enhancing connectivity.
  4. Strong Visualization Capabilities:
    • Offers flexible and customizable dashboards and visualizations, facilitating effective storytelling with data.
  5. Collaboration Features:
    • Shared workspaces and commenting abilities promote teamwork and knowledge sharing among users.


  1. Limited Data Engineering Capabilities:
    • Lacks the advanced data transformation and orchestration functionalities present in Microsoft Fabric.
  2. Limited Integration with External Tools:
    • Less seamless integration with non-Qlik tools compared to Fabric’s comprehensive Azure integration.
  3. Subscription-Based Pricing:
    • Can be costly for large teams or organizations with complex needs, as pricing is based on a subscription model.
  4. Not Ideal for Highly Technical Users:
    • May lack the depth and advanced functionalities required by data scientists and engineers, potentially limiting its appeal to highly technical users.

In considering these pros and cons, it’s essential to align them with your organization’s specific requirements, technical capabilities, and overall goals. This comprehensive evaluation aims to empower you to make a well-informed decision tailored to your unique data analytics needs.

External Links for Further Exploration:

  1. Microsoft Fabric Documentation
  2. Qlik Cloud Documentation

Frequently Asked Questions (FAQs)

Q: Is Qlik Cloud suitable for businesses with multi-cloud preferences?

A: Yes, Qlik Cloud supports multi-cloud deployment, providing flexibility for organizations with preferences for Azure, AWS, or Google Cloud Platform.

Q: Can Microsoft Fabric integrate with services beyond Azure?

A: While Microsoft Fabric is deeply integrated with Azure services, it can also interact with services beyond the Azure ecosystem, ensuring versatility in data integration.

Q: What level of customization does Qlik Cloud offer for visualizations?

A: Qlik Cloud provides extensive customization options for visualizations and dashboards, allowing users to tailor their analytics experience to specific requirements.


In conclusion, the choice between Microsoft Fabric and Qlik Cloud requires a nuanced understanding of your organization’s unique needs. By carefully evaluating features, deployment options, pricing structures, and considering additional factors like learning curve and customization, you can weave a decision that seamlessly integrates with your data insights