What needs improvement with Fabric Data?

One area Fabric Data can be improved is the semantic model refresh. Though it says it is a direct link, the refresh times of the semantic model sometimes need explicit refresh. This takes a bit of time to refresh. The second thing which can be improved is the latency between when we make changes in the PySpark notebook and when the changes reflect into the Lakehouse. There is a very slight bit of latency that can be improved. I chose nine out of ten for Fabric Data because, as I mentioned, there are a few improvements. Fabric pipelines can be improved by providing more features. The latencies can be improved a little bit. The sync time between the semantic model and the gold layer can be improved. Because of these things, I have given nine.

I would add one thing regarding improvements for Fabric Data. Most of the ingestion teams or tools are adding AI aspects into the existing tools. I suggest the same thing—adding some AI features, such as "Coco" in Snowflake or "Genie" in Databricks. You should also incorporate some parts where our code can identify quickly, and developers can understand fast based on that. Integrating those AI features into Fabric Data would be beneficial. If you improve some additional things, that would be a good part. Fabric Data should be able to understand governance and security regarding its AI capabilities because that is very important for AI solutions. Client data is more crucial than any task, and that aspect should be covered. Some improvements are needed for Fabric Data from the AI side. Day by day, AI is improving, and automating jobs is essential. The good thing is that you should continue developing your AI features.

Fabric Data could be improved in the future by increasing the size capability from terabyte to petabyte for deeper integration. Already built-in ADF integrated with Fabric Data means that in terms of integration, it is very good and similar to ADF.

One thing regarding needed improvements is related to the free tier or trial capacity. When I was learning Microsoft Azure services, it was very easy to get credits and a free account, but in Fabric, it was inconvenient to get a free tier or trial capacity. It was a very difficult and cumbersome process, so we found upskilling ourselves in Fabric difficult. If that gets sorted out, then many people can easily learn because Fabric is very easy software, and people can learn easily once the free trial capacity gets figured out.

I cannot say that the analytics and reporting capabilities of Fabric Data are good enough because it only provides compatibility with Microsoft Power BI. If the client has Tableau licensing other than Power BI, they might experience latency and performance issues within Fabric Data. Fabric Data is easy to use only with Power BI, so there is some limitation with the Power BI integration; it is not flexible for all tool integrations. I would like them to improve the integration with third-party tools, as clients might experience latency and other issues. Other than integration with third-party tools, I think Fabric Data could be improved and enhanced by advancing AI functionality. The global market is revolving around AI, so they need to focus on AI development within Fabric Data, making it a bit more configurable through visual screens. Currently, we work with data coding, so they might need to come up with layout screens to easily configure things, and the pipeline can be easily metadata-driven.

The improvement part I foresee for Fabric Data is going to be with the AI. Copilot in Power BI feels weak compared to standalone third-party assistants. Microsoft has the platform advantage but the model integration could be much more powerful and offer better insights.

I felt some features of Fabric Data, particularly around Dataflow Gen2 error handling and pipeline monitoring, lack clear documentation at the time of my study. The needed improvements in Fabric Data include that the learning curve for newcomers can be steep when moving beyond the guided tutorials into independent project work.

I felt some features, particularly around the Dataflow Gen2 error handling and pipeline monitoring, lacked clear documentation at the time of my study. The learning curve for newcomers can be steep when moving beyond the guided tutorials into independent project work.

Fabric Data can be improved by releasing more features that are currently in preview, and once those features are fully released, that will be an improvement. Also, improving the real-time data capabilities, like the KQL dataset, would be beneficial. The main improvement I would like to see is more integration with other tools; for example, SAP integration should be there because there are more integration tools available in Azure Data Factory than in Fabric Data, and I would like to have more integrations in Fabric Data.

If I could change something to improve Fabric Data, I think it would be to fix the basics and make everything work at least as well as it does in Data Factory. I would make CU usage much more transparent regarding what costs and what does not cost as much. It would also help to have people who actually work with Fabric Data now, giving feedback on all the pain points. The inability to monitor properly and having to build in fail conditions in pipelines, or navigate around it, was so painful that it is borderline unuseful for any large company in a production environment, and that would be at the top of my list. The list for improvements is very long, but starting there and making it stable and functional is essential.

I have observed some limitations with Fabric Data, especially when it comes to bringing data from private networks. Being a SaaS product, there is limited control, so having the option to create data gateways more easily within Fabric Data itself would be great. It would help to have more clarity about licensing capacities, as there are various capacities such as F16 and F64. A more detailed knowledge section within Fabric Data would be beneficial, while Microsoft Fabric Learn has extensive details, I would prefer to see this information integrated directly within Fabric Data itself. The user interface of Fabric Data is quite standard, with regular changes published by Microsoft that come in handy when I have multiple tabs open. As for scalability, it is based on use cases. I do not think scalability is the biggest advantage of Fabric Data compared to any other Azure resources. We have auto-scaling in other facilities available, but it is not a major advantage compared to usage.

I find the integration between these different tools within Fabric Data has some learning curves because Fabric Data is growing. It can sometimes be challenging to learn new tasks and items to get it to function, but overall, it usually works pretty well, even when things are in preview mode. Fabric Data can be improved because it tends to be run by Fabric Capacity, which is basically the compute cycles, and it is not very clear on how and what that is going to be used. There should be a lot more transparency on what things actually cost when it comes to Fabric Capacity. In addition, some of the tools offered by Fabric Data don't provide really good guidelines for how to accomplish things inside Fabric Data; they just have all these tools and you have to know which one to go to make it work. Fabric Data is laid out an umbrella with all the tools underneath it, and I don't find that their use case or how to maneuver or manage inside Fabric Data is intuitive. I wish they had spent more time developing the menus or how to get from one place to the other.

To improve Fabric Data, I suggest more integration with additional data sources and better integration for data agents.

Fabric Data is a strong platform overall but still has areas for improvement. One area is performance optimization and monitoring visibility for large-scale workloads. Having more granular monitoring and troubleshooting capabilities would help teams manage workloads more effectively. Another area is the learning curve and usability. Since Fabric Data combines multiple capabilities in one ecosystem, better simplification and guidance for new users could enhance adoption. Deeper integration across certain enterprise scenarios and third-party tools could also continue to improve as the platform matures, with some organizations needing more maturity in advanced governance and cost optimization features for large enterprise environments. One feedback I have heard from my team is that because Fabric Data is evolving rapidly, some features and integrations are still maturing compared to more established enterprise data platforms. Teams face challenges in understanding the best architectural approach, especially when combining multiple services such as Lakehouse, pipelines, semantic models, and reporting. Another pain point discussed involves cost and capability management visibility for larger workloads, where organizations want more detailed optimization and monitoring controls. Governance and role-based access management can also become complex as the platform scales across larger teams and projects. However, most feedback has been positive, as the platform significantly simplifies end-to-end analytics and improves collaboration between data engineering and BI teams.

I have an idea for Fabric Data regarding improvements. I would note that Fabric Data is a perfect software, which reflects my thoughts on the needed improvements.

Fabric Data needs more ecosystem support. It needs a lot of support on the CI/CD part. It is still in development. It needs more improvement on aspects like CI/CD.