Microsoft Fabric and Databricks integration/comparison

[amaaiia]

I don't know if this community is the best place to ask this question about Databricks and Fabric, because I want to know in which scenarios is better to chose Databricks.

What's the point of paying for a Fabric Capacity with which you can run notebooks, jobs... for data processing and then run, for example, a medallion architecture with Databricks?  What can Databricks provide that Fabric for example cannot provide in that architecture?

There are some best practices for data architectures that recommend using Databricks for data processing. I can't understand that.

[emily2056]

Great question! Microsoft Fabric is excellent for end-to-end Azure-based workflows with strong Power BI integration and ease of use. However, Databricks stands out in large-scale, complex data processing, especially for advanced ML, real-time streaming, and multi-cloud flexibility. If your architecture needs fine-tuned performance, scalable ML workflows, or cross-cloud capabilities, Databricks can complement or even extend Fabric's limits in those areas.

[vanessafvg]

Fabric has a different audience with the nocode / lowcode capability - making it easier for  citizen dev to do more advanced data analytics than power bi.  It also makes transition into more complex data engineering from power bi easier.

Obviously there are professional dev on Fabric too.  Besides the obvious differences between paas and saas,  Databricks is a much more mature product and i personally enjoy using it more than Fabric, but I am a professional developer, but that might change as fabric matures.  

Medallion architecture is an approach and you can do it in both fabric and databricks.   

[nilendraFabric · Accepted solution]

Hello @amaaiia 

lets me try to answer this question from what Databricks bring extra to the table:

Multi-Cloud Deployments: If your organization requires flexibility across different cloud providers (AWS, Azure, Google Cloud), Databricks supports multi-cloud environments

Customization and Control: For teams requiring fine-grained control over their data infrastructure and processing workflows, Databricks offers more flexibility and customization options

Advanced Cluster Management: For scenarios requiring detailed control over cluster management and scalable CI/CD pipelines, especially in complex Spark-based environments

I think it more around SAAS AND PAAS comparison. Fabric is still evolving and Databricks is more mature when compared to Fabric.

hope this helps 

please accept this solution and give kudos if it resolves your query.

thanks