Fabric Data Reviews

My main use case for Fabric Data is to get the data from multiple data sources, whether on-premises or other cloud service providers, and store that data into Lakehouse or warehouse, prepare a data model for them, and create reports with the Power BI Desktop.

A specific example of how I have used Fabric Data recently includes a project where data was coming from Oracle and IBM, and there was another data source. All of it was getting combined in Snowflake, and I performed Snowflake mirroring with Fabric Data where all the data is mirrored into the Fabric environment, and then I had to create the data models for the Power BI reports.

Fabric Data enables me to get the data from multiple resources, whether on-premises or any other Azure service providers, and also allows me to transfer and migrate the data from any other platform to Fabric Data smoothly. I accomplish this in the form of files or text, using the functional features of Delta Lake in the Parquet format for transactional data and historical data, and I can store the data in the form of tables or create a data warehouse for data modeling and more.

One use case I can share is that if we have a tenant in which we have multiple users, each user gets a Fabric Data free trial of sixty days in which he or she can explore Fabric Data items depending upon the client's requirement. This gives us the opportunity to only pay for one particular tenant level Fabric Data capacity while all the other users can use the same.

The best features that Fabric Data offers include that in Lakehouse, it has the form of tables and files where I can store the Delta Lake format, including the transactional data or historical data where I can roll back to the version level or find out the historical data. It also has a very good compute engine for the data warehouse where all the queries and the storage is mainly computerized in the back end via compute size, and it provides similar use cases of data engineering solutions that I can have in ADF, Synapse Analytics, and basically, it acts as a SaaS platform combining all the data-related fields and profiles that I can encounter.

Regarding the Delta Lake versioning format, I can get the data in the previous version to perform the SCD1 or SCD2 type to check that I am only loading the incremental data. If I am talking about the compute engine, it mainly focuses on querying the data, how much transactional data is being queried in the back end, and how much data is stored in the form of stored procedures, tables, views, functions, and many other features.

I have not encountered any challenges in Fabric Data up until now.

I did encounter one challenge recently in Power Query editor where I had to perform the same amount of transformations for multiple reports, repeating the transformations for each row each time. I think they need to improve in that scenario.

I feel there are a few challenges that I might not have analyzed right now. Nevertheless, it is still in preview and evolving. I am waiting for the challenges to be renewed or modified, and then definitely, I might be rating it higher.

I have been using Fabric Data for more than two years.

Fabric Data is stable at a limited amount of storage.

Fabric Data is scalable since whenever I start my Fabric Data free trial capacity, it gives me a scalable amount of sixty days where I can explore Fabric Data items, and after that, I need to purchase the paid Fabric Data starting from F2 to 256. I am not sure about the highest amount, but I can scale up and down depending upon the workloads.

I have not explored customer support yet because I have not encountered any major issues with Fabric Data that require reaching out.

Previously, I worked with ADF and Synapse Analytics since they provided different functionality depending upon their deployment methods. However, since Fabric Data gained prominence around May 2026, I have transitioned most of my workloads, creating data pipelines and reports from various services to a single SaaS platform.

I was using Azure Data Factory and Synapse Analytics while I also utilized Power BI Desktop for creating the reports before choosing Fabric Data.

Pricing, setup cost, and licenses are not mainly handled by my team since we are mainly focusing on creating scalable pipelines for the migration of data from data sources to Fabric Data. I do not have much expertise on that subject.

Since Noventiq is currently working as a Microsoft service provider, we mainly focus on services provided by Microsoft. Fabric Data was launched around May 24, and the first project I did with Fabric Data was with a client where I had to create different layers of cementing models; I did raw, silver, and gold in Fabric Data layer in Lakehouse and warehouse as well. After that, I created multiple reports.

I actually encountered a few deliverables that were very helpful for the client, such as the incremental load and bifurcations of different layers of data, where I performed some transformations and the data modeling was performed in the gold layer so that I could have a perfect star schema in the form of fact and dimension tables. I was also able to create insightful business reports.

Depending upon the client's requirements, if the data is in the form of on-premises, I use the on-premises data gateway by deploying a virtual machine that is indirectly connected to on-premises and Microsoft data, and in the back end, it gets connected via Azure Relay. I can also connect the data via the virtual network gateway where Fabric Data is being deployed, and the paid Fabric Data is deployed in a particular virtual network connected with Fabric Data environment to get the data output.

I mainly use Azure, but there were two or three projects that I have worked on with AWS as well.

I did not purchase Fabric Data through the AWS marketplace for those AWS projects; it was actually set up by the client environment. I just had to migrate the data from AWS to Fabric Data.

My advice to others looking into using Fabric Data is that it is a one-stop solution for all the upcoming data-related profiles, such as data analysts, data engineering, data science, and Power BI development. All these things can be encountered on one platform; I just need to know how to manage different public items that are being deployed in Fabric Data. I would rate my overall experience with Fabric Data as 7.5 out of 10.

My main use case for Fabric Data is data integration, ETL, and BI. A specific example of how I use Fabric Data for integration, ETL, and BI is that we integrate several data sources such as SAP, Business Central, or any kind of data source, database, or API to build a Lakehouse. With the information on the Lakehouse, we perform an ETL process. Finally, we create BI solutions with Power BI.

The best features Fabric Data offers for my work include its integrated environment with many solutions inside the same platform. The features I use most often or find most valuable are Lakehouse, pipelines, dataflows, data agents, and reports and semantic models.

Fabric Data has positively impacted my organization as my company is a consultant company and we have implemented these projects for our clients. I have seen positive results and feedback from my clients after implementing Fabric Data. Specific outcomes and examples of how my clients benefited include cost savings and various KPIs, depending on the business.

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

I have been using Fabric Data for three years.

In my experience, Fabric Data is stable.

Fabric Data's scalability is adequate; it is very scalable.

Customer support for Fabric Data is provided through partners.

Before Fabric Data, I used MicroStrategy. I switched from MicroStrategy to Fabric Data because of the revolution with Power BI.

I have seen a return on investment with time saved.

My experience with pricing, setup cost, and licensing is that it is acceptable.

Before choosing Fabric Data, I did not evaluate other options.

I do not have any advice to give to others looking into using Fabric Data. I have no additional thoughts about Fabric Data. I found this interview satisfactory and nothing should change for the future. I would rate this review an 8.

Private Cloud

Microsoft Azure

Fabric Data is used for ingestion purposes and some transformation tasks. When data exists in specific sources, we ingest data from those particular sources and load that data into our staging or landing zone while dynamically passing variables. The ingested data is then transformed, and we use it for creating dimensions and fact tables. Once everything is properly modified, that data goes into our published layer, which our business people use.

I work for a service-based company where many of our clients use Fabric Data. They mostly use it for ingestion purposes. We create one job, and the pipeline is created with multiple tables loaded based on that. Fabric Data is very useful for our organization and our clients as well, saving time, simplicity, and offering many benefits. Some parts we use custom solutions for too, and due to Fabric Data, our clients and our organization save money, speed, and time.

I am using Fabric Data in our organization on a private cloud.

Recently, I find the copy feature of Fabric Data very helpful. The copy feature is very simple. I already work both on-premises and in the cloud. On-premises, we copy data from specific databases to other databases for their testing or development purposes. With zero-copying, we just copy some data for development or testing purposes, which is very easy to use and requires no maintenance. That is good. There are many features.

Fabric Data is very useful for our organization and our clients as well, saving time, simplicity, and offering many benefits. Some parts we use custom solutions for too, and due to Fabric Data, our clients and our organization save money, speed, and time.

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.

I have been using Fabric Data for two years, which is twenty-four months.

Fabric Data is stable.

The scalability is good. Out of ten, I can give it a nine.

Customer support for Fabric Data is also good. Out of ten, I can give it a ten.

We have not previously used a different solution, but sometimes clients needed that specific aspect. However, mostly we are using Fabric Data.

We directly connected with Fabric Data without evaluating other options.

My experience with pricing, setup cost, and licensing is that pricing and those aspects are a different matter. It is not a concern for me because it matters to the client. Compared to other options, it is good—neither very high nor very low.

Most people find Fabric Data simple to use and workable. If your cost is less, then that is a substantial matter.

I rate Fabric Data a ten out of ten overall.

I choose a ten out of ten for Fabric Data because it is very simple to use. It is excellent for our work purpose and is easy to use. Support for Fabric Data is good as well, and most of the clients also use this feature, which is also a good part. There are many features as well.

The accuracy and reliability of Fabric Data's output are good compared to others, and its reliability is also good.

If someone is looking into using Fabric Data, I would advise choosing Fabric Data because currently in the market, Fabric Data is good compared to others. My overall review rating for Fabric Data is ten out of ten.

Private Cloud

Microsoft Azure

My main use case for Fabric Data is to ingest data from our ERP system, go through medallion architecture, create integrations from it, and prepare the data for reports and integrations.

A specific example of how I use Fabric Data for this process is the integration I made from our on-premises ERP system to Fabric where we take a quite large database with some tables containing two billion rows. I ingested these tables, and that was primarily my responsibility.

Fabric Data offers several standout features that are best in class. It is built on a quite mature way of looking at pipelines and ease of use, which is Data Factory, a good product that is actually production-ready. I do not understand why many of the good things from Data Factory have not made it into Fabric yet, but it includes very good points that are visually appealing and easy to work with.

However, it has not been a positive experience for Fabric Data in my organization.

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 been using Fabric Data for about one and a half years.

I do not think Fabric Data is very stable.

We have not tried scaling Fabric Data further, but it is probably quite scalable if you are willing to pay.

Fabric Data's customer support has been awful. Every time there was a problem, I would create a ticket and get an outsourced company that would be first-line support. I would spend a lot of time explaining the error to different people, and eventually, nothing would come of it, or they would just create a ticket for real developers at Microsoft and still nothing would happen, making it an awful experience.

We previously used AWS Databricks, and we tried Fabric Data for a good year. Now we are migrating everything we have in Fabric Data back to Databricks because my client did not think Fabric Data solved the job very well.

I would say Fabric Data must have had a very negative return on investment since so many hours were wasted on it, and nothing really came of it.

Before choosing Fabric Data, we were deciding just between Fabric Data and Databricks.

My advice to others looking into using Fabric Data is to stay away until it proves itself to be much more reliable than it is currently. I would rate this review as a two out of ten.

Hybrid Cloud

Microsoft Azure

My main use case for Fabric Data is that I have been using Fabric for around one and a half to two years, and typically in our project, we have been trying to shift from regular Azure-based services and Databricks services to Fabric itself because it is a complete all-in-one solution. We have been creating new pipelines in Fabric, and all development is being done in Fabric itself because it supports pipelines and notebooks. Previously, we were using notebooks from Databricks and pipelines from Azure Data Factory, but currently, we are utilizing the notebooks and pipelines in Fabric itself, and the storage and everything is in the same UI, making it easier for us. We are doing complete end-to-end development in Fabric itself.

A quick specific example of a use case where Fabric Data made a big difference for my team is that previously we had to create our notebooks in Databricks and deploy those notebooks separately, and we had to deploy our pipelines separately. This was a scenario that we overcame by creating the pipelines and notebooks in the same place and deploying them directly by using deployment pipelines. This was a big difference for us. Previously, all things were scattered. We were using Synapse Analytics for storing our data and ADLS for storing our files and tables, so everything was scattered across different services. Now we have everything under a single umbrella.

The best features that Fabric Data offers are the unified UI and seamless integration, and these are the standout features for me.

Fabric Data has helped my project further because previously we were connecting Power BI to Synapse Analytics itself, but now that we have data warehouses and lakehouses in place, we can directly connect here as well. The UI is easier, and the data governance team has found it easier to manage access and everything at a single place because previously they had to manage access for all the different services individually. For ADLS, they had to give different access and add different user groups, which was hectic. For me, when I was doing some proofs of concept, it was very difficult to understand. Currently, access and everything is simplified in Fabric, which is another valuable aspect.

Fabric Data has impacted my organization positively because collaboration has been better and deployments have been faster. Deployments have been faster, getting access sorted out has been faster, and the overall project nomenclature and the whole project structure has been simplified because everything can be found at a subfolder level and folder level. We do not have to go to Azure Data Factory to find pipelines, and we do not have to go to Synapse to find warehouse data. We do not have to go to ADLS and we do not have to go to its directory to find source files or archive files. Everything is in a single UI, so it saves time for development and also for the data governance team for giving and managing accesses and for our data operations people for doing deployments.

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 have been using Fabric Data for four years.

Fabric Data is quite stable, and I have not faced any downtime issues.

Fabric Data's scalability is good because it handles growing workloads well.

Before using Fabric Data, we were using Azure and Databricks, and it was very difficult to manage everything individually. It has been much easier for us now.

My experience with pricing, setup cost, and licensing was that this was done by our data governance team. I did not have any role in this setup and pricing.

Before choosing Fabric Data, we evaluated other options in Databricks, which we have used extensively, and it also had similar features.

Since moving to Fabric Data, I have saved around ten hours per month, but that is a very rough estimate because I have never thought in this way or never had a metric regarding this.

Regarding Fabric Data's governance and security, I think these aspects are great and have been proving very useful for our data governance team.

I have not used much of Fabric Data's AI capabilities, so I might not be able to answer that question fully.

I would recommend others looking into using Fabric Data to check out Databricks itself if possible, but I am not sure about the pricing part. Our project had people who checked it, so if they have considered Fabric over Databricks, then I think it is well and good. If you are coming from a setup of Azure plus Databricks or just Azure, Fabric makes a lot of sense. I would rate my overall experience with Fabric Data as an eight point five out of ten.

Public Cloud

Microsoft Azure