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The biggest win for us has been the unified storage layer through OneLake. It helped us move away from siloed storage across multiple Azure subscriptions and finally establish a consistent single source of truth for cross department governance. The Power BI integration was smoother than expected. Instead of rebuilding legacy dashboards we have extended existing ones using Direct Lake mode which saved a significant amount of time and avoided redundant work. Having Data Factory built into the same ecosystem also made a difference. It simplified pipeline orchestration and reduced our reliance on separate services. On top of that Synapse Real Time Intelligence has added clear operational value by allowing us to move from batch processing to near real time insights for daily reporting.
Ease of use and integration into our existing Power BI landscape were a driving factor behind our pro fabric decision.
- unified analytics experience combining data engineering, pipelines, warehousing, and Power BI in a single environment; - OneLake architecture simplifies data sharing and reduces duplication across workloads; - strong integration with Microsoft ecosystem services such as Azure, Entra ID, Purview, and Defender; - lower operational overhead compared to managing multiple independent analytics services; - governance and security capabilities are integrated directly into the platform through centralized policies and sensitivity labels.
The consumption based pricing has been our biggest pain point. Since costs scale with workload spikes monthly billing has been less predictable than we had like making internal budgeting harder to manage. There's also a noticeable learning curve for engineers coming from traditional Azure Synapse and Data Factory environments. Its not just a UI change it requires a shift in how you think about architecture which takes time to adjust to. Finally some of the monitoring and debugging capabilities still feel a bit early stage compared to legacy Azure tools. Getting clear end to end visibility in production is not always straight forward and we have often needed more detailed logging when troubleshooting pipeline bottlenecks.
The cost control of this software platform is subpar to say the least. You have no clue how many CUs you're about to use when using one of the tools of the platform. You have to try which tools will be of best use for your use-case since the documentation is lacking in a lot of areas because there are so many products bundled up as one that microsoft cant keep up with writing the correct documentation for the updated suites of the software. A lot of the time in early setup stages you have the choice between paying for a oversized capacity and being able to try different things or having a right sized capacity and basically being not able to make mistakes because the capacity wil throttle after using too many CUs (which as i stated earlier are never mentioned while using any service). Other platforms are a lot more transparent about upfront costs and let you know what the tool, calculation, etc. will set you back. The only way to see which of your services sent your capacity into throttle is a self designed power bi dashboard with about half an hour of latency, which wouldnt be too bad as it is quite informative but again the missing of costs per service are what is a dealbreaker.
- some advanced enterprise capabilities are still maturing, especially around governance granularity, networking, and workload isolation; - troubleshooting complex pipelines or capacity-related issues can sometimes lack detailed diagnostics; - highly customized architectures may still require complementary Azure services outside Fabric; - An organization that adopts OneLake and Fabric-native workflows should evaluate long-term ecosystem dependency and portability considerations.