Compare Microsoft Fabric and Databricks, two leading data platforms. Highlights their features, strengths, and unique offerings across various domains like data engineering, data analytics, data science, DevOps, security, integration with other tools, cost management, and governance. Microsoft Fabric is noted for its low-code/no-code solutions and seamless integration with Microsoft tools, making it accessible for users with varying technical skills. Databricks is praised for its high-performance capabilities in big data processing and collaborative analytics, offering flexibility and control for experienced data teams.
In a dynamic data engineering scenario, Sam, a skilled professional, adeptly navigates urgent requests using Microsoft Fabric. Collaborating with Data Steward Lisa and leveraging OneLake, Sam streamlines data processes, creating a powerful collaboration between engineering and stewardship. With precision in Azure Data Factory and collaboration with a Data Scientist, Sam crafts a robust schema, leading to a visually appealing Power BI report.
NOTE: This is the first draft of this document that was assembled yesterday as a solo effort. If you would like to contribute or have any suggestions, check out my first public GIT repository - KratosDataGod/LakehouseToPowerBI: Architectural design for incorporating a Data Lakehouse architecture with an Enterprise Power BI Deployment (github.com) This article is NOT published, reviewed, or approved by ANYONE at Microsoft. This content is my own and is what I recommend for architecture and build patterns.
