Microsoft Fabric vs. Qlik Cloud: a comparative analysis

In recent months, the technology landscape has been abuzz with discussions and excitement around Microsoft Fabric, an end-to-end analytics solution with full-service capabilities including data movement, data lakes, data engineering, and data integration. This article aims to provide an objective evaluation of Microsoft Fabric alongside Qlik Cloud, shedding light on their respective features and capabilities.

As a technology-agnostic consultancy company, we are keenly aware of the industry's evolving trends and have closely followed the rising popularity of Microsoft Fabric. Our goal is to present a comprehensive comparison that reflects our experience and expertise. By delving into the features and functionality of both platforms, we aim to assist organizations in making informed decisions based on their unique needs and requirements.

Single Platform

Both Qlik Cloud and Microsoft Fabric offer a unified platform for data integration, providing seamless data orchestration and transformation. Qlik Cloud operates as a Software-as-a-Service (SaaS) offering, while Microsoft Fabric also delivers its capabilities through a SaaS model. This allows organizations to leverage a comprehensive set of data integration functionalities within a single environment.

Cloud Support

Microsoft Fabric is primarily designed to integrate with Azure, Microsoft's cloud platform. The deep integration with Azure services provides organizations with seamless connectivity and enhanced interoperability within the Azure ecosystem. On the other hand, Qlik Cloud offers cloud support on AWS by default, but customers have the flexibility to choose alternative cloud platforms according to their preferences. This flexibility allows organizations to leverage their existing cloud infrastructure investments or select the cloud provider that best aligns with their strategic goals.

Change Data Capture

Qlik Cloud incorporates Change Data Capture (CDC) capabilities, utilizing log-based mechanisms to track and capture changes made to the data sources. This log-based approach enables real-time data synchronization and minimizes data latency, making it suitable for scenarios that require up-to-date information. Conversely, Microsoft Fabric does not natively support CDC. However, it is worth noting that Azure Data Factory, a component of the Microsoft ecosystem, offers CDC functionality as a standard feature. Organizations utilizing Microsoft Fabric can leverage Azure Data Factory to achieve CDC when necessary, ensuring data accuracy and consistency.

Data Storage

Microsoft Fabric focuses on integrating with Azure services, including Azure Data Lake Storage. This tight integration simplifies data ingestion and processing within the Azure environment, ensuring efficient data movement and reducing complexities associated with cross-platform integrations. Qlik Cloud, on the other hand, offers integration capabilities with various data storage solutions, including Snowflake, Databricks, Synapse, BigQuery, and AWS services. This wide range of supported data storage platforms allows organizations to leverage their existing investments and choose the most suitable solution for their specific needs.

Direct Query (Applications)

Both Qlik Cloud and Microsoft Fabric support direct querying of data for applications. Direct query functionality allows applications to retrieve data directly from the source systems in real-time, without the need for intermediate data storage or replication. This capability enables organizations to analyze the most up-to-date data and make informed decisions based on real-time insights. However, it is important to note that Qlik Cloud may have some limitations in terms of the features available for direct query. On the other hand, Microsoft Fabric offers direct query functionality without limitations, providing organizations with unrestricted access to data sources and empowering them to build dynamic and interactive applications.

Additional Considerations:

Performance

Both Microsoft Fabric and Qlik Cloud are designed to deliver high-performance data integration capabilities. However, the actual performance may vary depending on factors such as data volume, complexity of transformations, network latency, and the underlying infrastructure. It is recommended to conduct performance tests and benchmarks specific to the organization's use cases to determine which platform meets their performance requirements.

Scalability

Scalability is a crucial aspect of data integration platforms, especially as organizations deal with increasing data volumes and evolving business needs. Qlik Cloud and Microsoft Fabric are designed to scale horizontally, allowing organizations to handle growing workloads efficiently. However, it is essential to assess the scalability features and performance benchmarks of each platform to ensure they align with the organization's scalability requirements.

Ease of Use

User experience and ease of use play significant roles in the adoption and productivity of data integration platforms. Qlik Cloud focuses on providing a user-friendly and intuitive interface, enabling users to design and manage data integration workflows with ease. Microsoft Fabric, being part of the Azure ecosystem, offers a familiar interface and integrates well with other Azure services, providing a seamless user experience for organizations already invested in the Microsoft ecosystem. It is advisable to consider the platform's user interface, documentation, training resources, and community support to gauge its ease of use for the organization's teams.

Integration with Other Systems

Integration capabilities extend beyond data storage and include integration with various systems and tools. Qlik Cloud and Microsoft Fabric support integrations with a wide range of systems such as databases, cloud platforms, data warehouses, business intelligence tools, and more. It is important to assess the integration options available for the systems and tools already in use within the organization's technology stack to ensure seamless interoperability and maximize the value derived from the data integration efforts.

It appears that Microsoft Fabric shines particularly bright for organizations already immersed in the Microsoft ecosystem, while Qlik Cloud Data Integration suits particularly well for organizations with enterprise data transformation needs, especially SAP or Mainframe.

Conclusion

The decision to choose between Microsoft Fabric and Qlik Cloud hinges on several factors such as cloud platform preferences, applications used within the organization, data storage requirements, the need for Change Data Capture capabilities, performance, scalability, ease of use, and integration with other systems.

As mentioned above, Microsoft Fabric shines particularly bright for organizations already immersed in the Microsoft ecosystem. Its seamless integration with Azure services, coupled with its advanced data quality features and emphasis on collaboration and governance, make it an attractive choice for those leveraging the power of Azure for their data integration needs. By leveraging the native connectivity and interoperability within the Microsoft ecosystem, organizations can streamline their data integration workflows and maximize their investments in Azure.

However, for organizations seeking cloud flexibility, Qlik Cloud offers a compelling alternative. Qlik Cloud offers integration capabilities beyond data storage, encompassing various systems and tools. It seems to be a strong choice when specific Change Data Capture (CDC) requirements are necessary, and stands out for handling enterprise data transformation needs, especially when dealing with complex sources like SAP or Mainframe.

By carefully assessing the technical landscape, business objectives, and compatibility with existing systems, organizations can make an informed decision and embark on a successful data integration journey that drives valuable insights and empowers data-driven decision-making.

Our experience allows us to offer impartial advice and assist organizations in selecting the most suitable solution for their specific needs. By carefully evaluating different factors and considering the unique requirements of the organization, businesses can make an informed decision and embark on a successful data integration journey.