What really stands out about Microsoft Fabric is how it brings everything into one place. Combining Data Factory and OneLake cut down a lot of the back end effort we used to deal with when managing separate pipelines and storage, and it made data more consistent across teams.
From a usability standpoint, the interface is fairly intuitive once you spend some time with it. It is not overly complex but there is still a bit of a learning curve if you're coming from traditional Azure tools, mainly because everything is now bundled into a single experience rather than separate services.
The Power BI integration was another big win. We did not have to rebuild existing reports we were able to extend what we already had, which made adoption much easier for business users. Direct Lake has also helped improve performance, especially for larger datasets, without adding extra layers of complexity.
We have also seen solid value from Synapse Real Time Intelligence. It has given us a practical way to move toward real time visibility for certain use cases which was difficult to achieve with our earlier batch driven setup. From an AI and analytics perspective, having these capabilities closer to the data layer makes it easier to experiment with more advanced use cases over time.
In terms of pricing, the consumption-based model gives flexibility, but it takes some time to understand how workloads translate into actual costs. We had to actively monitor usage in the beginning to avoid unexpected spikes and improve ROI.
Support and onboarding were decent overall but most of the learning came from hands on experience and internal experimentation rather than formal guidance.
That said it is not completely straightforward. Teams coming from traditional Azure services should expect a learning curve. The capacity based model can be confusing at first and governance needs to be set up early to keep costs under control as adoption grows. Análise coletada por e hospedada no G2.com.
What we have found challenging with Microsoft Fabric is that moving to a unified platform comes with some trade offs. The UI is clean but it can feel overwhelming at first since multiple services are bundled into the same workspace. For teams used to separate Azure tools just figuring out where everything sits takes some adjustment.
The pricing model has been another headache. Since it is consumption-based and tied to Capacity Units (CU) costs are not always easy to predict especially when workloads scale or multiple teams are using the platform at the same time. We have had to closely monitor usage to avoid unexpected spikes and better understand the actual return on spend.
From a day to day operations perspective some of the monitoring and debugging capabilities still feel early compared to standalone Azure services. Getting clear visibility into pipeline failures or performance bottlenecks is not always as straightforward as it should be.
Onboarding could also be smoother. While documentation exists much of the learning has come through trial and error rather than structured guidance particularly for more advanced scenarios.
Finally while Fabric is pushing into AI and real time capabilities some of these features still feel like they are evolving. They are useful but not always as mature or flexible as expected for more complex production use cases. Análise coletada por e hospedada no G2.com.
