Fivetran vs. Microsoft Fabric

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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I would highly recommend Microsoft Fabric, especially for medium to large enterprises aiming to build a robust, scalable, and secure data analytics platform. It effectively unifies various data workloads, streamlining data integration, engineering, and particularly enhancing our ability to create and share reliable Power BI dashboards. The deep integration with Azure AD for features like Row-Level Security is a significant advantage for data governance.

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• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

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• Access Control / Security & Governance
• Collaborative Work up to Visualization
• Data Ingestion

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• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

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• Clarity and Documentation on Power BI Integration Changes and Best Practices
• More Flexible and Advanced Scheduled Report Delivery Options
• Enhanced Data Export Control Based on Sensitivity Labels

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Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

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I've rated Microsoft Fabric's overall usability as a 4, primarily due to its extensive and multifaceted feature set, which can make it challenging to navigate and determine the optimal functionality for a given task.While the breadth of capabilities is a core strength for large enterprises, it often leads to a sense of being "lost" or overwhelmed for teams like ours that do not have highly formalized roles or dedicated specialists for each Fabric "experience" (e.g., Data Engineering, Data Warehousing, Data Science).

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It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

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We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

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Microsoft Fabric integrates data ingestion, engineering, warehousing, and Power BI visualization into one cohesive environment. This "one-stop shop" approach dramatically reduces complexity, minimizes operational overhead, and eliminates the need to integrate disparate tools and manage data across multiple systems. It provides superior scalability for large datasets, supports open data formats, and offers a much broader suite of data engineering and data science capabilities.In essence, Fabric's integrated ecosystem and streamlined operational management were key differentiators, providing a more cohesive, scalable, and efficient solution for our evolving data strategy than combining specialized tools.

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• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

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• Transformation from Monthly Reports to Daily Insights, Accelerating Decision-Making
• Streamlined Reporting: Enhanced Data Consistency and 30% Reduction in Reports Provided
• Empowered Self-Service Analytics Through Elevated Employee Data Literacy

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info