Fabric Origin Studio

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  includes using Data Factories, Lakehouse, Data Warehouse , and Data Pipeline, Gen2 flow, shortcuts, and some libraries in my projects.

A specific example of a project where I used Fabric Data  is when I worked with big data and big data frames, where I utilized the Medallion Architecture design pattern. In the Bronze layer, I was configuring different source data to land in the Bronze layer, mapping data with source to destination, data types, and configuring tables one by one in the Bronze layer. I was also using an ETL pipeline and a try-and-catch block to handle the pipeline error and understand the error, along with using data changes, data type changes, and CDC (Change Data Capture ) while also utilizing fact and dimension tables.

In addition to my main use case for Fabric Data, I encountered the shortcut method, which allows me to land data in Lakehouse from different sources, such as AWS  and Azure , using a shortcut without copying the data to store it in Lakehouse.

The best features of Fabric Data include the OneLake architecture, as it combines data analytics, data engineering, and machine learning all in one platform. I can load data directly into Lakehouse without copying it, utilize the Medallion Architecture design pattern, clean data stored in Delta Lake, and use any cloud to store Delta Lake, which is a significant benefit to land data and store it in a Parquet file. The data is stored in a Parquet file, and without copying, I can use one raw data in a completely semantic model.

Fabric Data has positively impacted my organization by decreasing the storage-level cost, and we now have different teams, including a data analytics team and a data engineering team, all on one platform, allowing us to directly check the data analytics part. If the data analytics team needs some KPIs, the data engineering team can create a materialized view and store it directly in a Delta Lake-structured format. This is a benefit for all teams, from the starting project to the end project.

I believe Excel sheets have some issues when creating a data frame; however, JSON data works fine for Fabric Data. When using an Excel sheet, we need some extra libraries, and that feature would be useful because most e-commerce sites store data in Excel. Therefore, I need a way to directly store an Excel sheet in Delta tables.

I would like to add that we have DataBricks in my organization, which serves various purposes related to data handling.

I have been using Fabric Data for two-plus years, and I have completed two end-to-end Fabric Data projects.

In my experience, Fabric Data is stable.

Fabric Data is good for security and scalability, with row-level security and column-level security, and the ability to track any pipeline, making it easy and understandable for users, including non-IT persons, at a graphic level.

When I reached out regarding some issues we had encountered, I found the customer support to be good.

Before using Fabric Data, I worked on one project in DataBricks; however, since the client needed Fabric Data and had data stored in Azure , it was easy for me to load data from Azure into Fabric Data using one account, which is why I switched to Microsoft Fabric  Data.

I have indeed seen a return on investment, as different employees use one cloud account, leading to fewer employees needed, thereby saving costs.

My experience with pricing, setup cost, and licensing is that we have an Azure license, making it easy to use Fabric Data.

Before choosing Fabric Data, I evaluated other options, specifically DataBricks and Snowflake .

I do not have extensive experience in Fabric Data currently, as I have only worked on two projects. Fabric Data is new for me, and I do not encounter any problems in my projects at this time. If there is any problem, I will read and discuss it.

I chose a rating of nine out of ten for Fabric Data because some features are not available. For example, DataBricks has certain features that Fabric Data currently does not have.

My advice for those looking into using Fabric Data is that it is easy to use. You can load from on-premise into Lakehouse, utilize copy activity from another cloud, leverage the shortcut method, and use Fabric Data pipeline. It is straightforward to load raw data in Fabric Data, and the Medallion Architecture is also straightforward, covering Bronze, Silver, and Gold layers. Additionally, analyzing historical data in the analytics field and accessing the data engineering and machine learning fields, all in one platform, is advantageous. I believe Fabric Data will be in high demand in the coming years.

I am currently learning about a Fabric Data project, and if there are any needed new updates, I will contact the customer.

My main use case for Fabric Data  is to create a data pipeline that ensures that the data coming from the source has been properly cleaned and provided to the end user with a dashboard. Fabric Data  helped with multiple tools for this, including notebooks, Gen  flows, and pipelines. I created something where I could use the pipeline tool as an orchestrator and manage my overall pipeline.

I found Fabric Data to be very useful for data analysis. The dashboards that we can create are pretty much code-free and very easy to learn. Fabric Data can be a bit slow with dependencies, but overall it is quite good.

The best features Fabric Data offers are its Gen2 flows and its pipelines because they are all code-free and low-code tools. This means any person with a non-technical background can use them. In Fabric Data, we can connect with multiple other sources from GCP , Google Cloud , AWS , and Azure . I love that everything is on one lake, which is Delta Lake underneath, and everything integrates well with Microsoft tools as well as with Google Gen  and AWS .

Fabric Data has positively impacted my organization because, compared to others, I found it pretty easy to use. Being with a group of business analysts, it was straightforward for all of us, especially since we were using Azure  at that time. Having Fabric Data was an easier decision to make because both link to Microsoft, resulting in easier integrations and overall good performance. Although Databricks  has more competency, as a data analyst, I find Fabric Data is at its peak.

I think Fabric Data could be improved by adding more notebooks, even though it currently has one.

I wish Fabric Data included more complexity because most of the tools are low-code or no-code. Adding more complexity would provide a more complete package, making it better than Databricks . I believe it should include Git  as well, which it currently does not.

I have been using Fabric Data for about two years.

I have not found any issues with the reliability of Fabric Data. It is pretty secure.

Fabric Data has handled larger workloads and growing data volumes well and has scaled effectively.

Customer support for Fabric Data has been good. Our customers were pretty happy and delighted with the service.

My first solution was Microsoft Fabric , and later I was introduced to Databricks.

I cannot give exact figures regarding money saved, but after the application was built with Fabric Data, our client experienced significant growth in their field, leading to a lot of profit inflow because the end users loved the application. In terms of workforce, as it goes forward, there can be a reduction in team members, but that depends on the project's complexity. Overall, as a platform, Fabric Data is easy to learn and quite good.

My experience with pricing, setup cost, and licensing was not really discussed. I am not entirely aware of what our pricing was. If I had to guess, I think we were using something around 64, which comes to about $200 or $300 per month.

Something I wish I could do with Fabric Data is create my own application, which I have not done before. However, I have worked on other applications and end-to-end pipelines, along with dashboards, so I am trying to do a side project of my own using Fabric Data.

I noticed improvements because when I was first introduced to Fabric Data, I had no idea about it and was more of a code person. However, once I started using Fabric Data, I found it pretty easy to learn and quickly grasped it. Because it is low-code and more of a drag-and-drop tool, I could easily play around and become accustomed to it. Additionally, it is free for many users.

Since adopting Fabric Data, we saved a lot of time because we all did not have to code. If I were using Databricks, I would have spent multiple hours writing complex code, but using Fabric Data allowed us to save much time. I think a project that was supposed to take eight months could be completed in about six months, so we saved around two months. The performance has been quite good, and we did not find any lags, although there were some difficulties and slowdowns with dependencies, but overall it is quite good.

My advice for others looking into using Fabric Data is that if they are building something simple that does not require frequent maintenance, Fabric Data would be a suitable solution. However, if it is very complex and demands regular maintenance, Fabric Data might not be the best choice. For simple projects, especially in startups or where there are fewer tech staff and users transitioning from non-tech to tech, Fabric Data would be an excellent starting point.

Everybody should give Fabric Data a try because it is the easiest tool that I have ever used. I would rate this review an 8.

I have been using Fabric Data  for the past two years.

My main use case for Fabric Data  is building pipelines with notebooks and then surfacing the output in Power BI. Doing the data engineering inside Fabric  makes it easier to ingest, clean, and shape the data into the exact structure I need for reporting, getting it from point A to point B in one workspace.

I work primarily with the pipelines because they give me a full end-to-end flow - I can take raw data and report on it in one place instead of going back and forth between databases and engineers. It lets me operate as a data scientist, data engineer, and business analyst from a single workspace, with end-to-end visibility and control over the pipeline.

Fabric Data's best features are the automation and the data engineering capabilities I can handle on my own.

The automation and engineering features stand out because they sit on top of the Microsoft infrastructure, making it very easy to connect to various data sources. I can ingest data even when it's not in my Azure  Blob storage - from other SQL servers, on-prem connectors, or anywhere else - and the Power BI connector library lets me connect to data from almost any source, join multiple sources together, and provide reporting on top of it.

The AI features are still maturing, but Microsoft has a clear roadmap and direction for that to improve over the next few releases.

Fabric Data's best features are automation and the engineering part of the data that I can handle on my own, and the company is now getting into the AI part, which I feel is still not the best, but there is a feature for that to come.

The automation and engineering features stand out for me because they contain the Microsoft infrastructure, making it very easy to connect to various data sources, allowing me to ingest data even if it's not in my Azure  Blob storage, such as from other SQL servers or any location, and it has extensive Power BI connectors that enable me to connect from almost anywhere, with a huge number of connectors to bring data from anywhere I need, join multiple sources together, and then provide my reporting on top of it.

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 have been using Fabric Data for the past two years.

Fabric Data appears to be stable.

Fabric Data is scalable; if I build a good database model in the Lakehouses, it scales well for reporting.

The customer support for Fabric Data is good; especially if I raise a critical-level ticket, they contact me directly, but they could improve in some areas.

Previously, everything was done with Excel, and using Excel was a significant challenge for storing, manipulating data, and then publishing it online with Power BI when the Excel file sits on my desktop. However, pushing it into Fabric  makes everything much easier, cleaner, and more transparent.

Fabric was the natural choice given our existing Microsoft stack Power BI, Azure, and Microsoft 365 and the deep native integration outweighed evaluating standalone alternatives.

My advice for others looking into using Fabric Data is to try to understand what users need before building from beginning to end; there are many ways to bring data, engineer it, and report it, such as data shortcuts, mirroring, imports, and data lakes. I suggest understanding the whole project from start to finish and evaluating each option that could work best for your case since there are numerous ways to bring in a single source of data, depending on the best use case to provide the most efficient and cost-effective Fabric solution. I would rate my overall experience with this product an 8 out of 10.

My main use case for Fabric Data  involves using it as part of my preparation for the Microsoft Fabric  Data Engineer Associate certification, where my hands-on practice covers building data pipelines, working with Lakehouse and OneLake storage, transforming data using Dataflow Gen2, and connecting outputs to Power BI for visualization. All work is done in a personal lab environment following Microsoft Learn  guided exercises.

Fabric Data  offers the best features by delivering a well-integrated, modern data engineering experience as a learning and certification platform. Fabric Data impacts my work positively, with one of its strongest aspects being its native integration across the Microsoft data stack. OneLake serves as a single unified storage layer across all Fabric  workloads, meaning data written by a pipeline is immediately accessible in Lakehouse, Warehouse, and Power BI without duplication or manual transfer. This eliminates the data silo problem that commonly affects multi-tool environments.

This unified storage in Fabric Data impacts my workflow by making Dataflow Gen2 use the familiar Power Query interface, allowing accessibility for analysts already working in Excel or Power BI. The output of a dataflow can be directly directed into a Lakehouse table, which then becomes queryable via the SQL analytics endpoint without additional configuration.

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 have been using Fabric Data for three months.

My experience with pricing, setup cost, and licensing involves having a student trial.

Fabric Data is a strong solution that delivers value as a learning and certification platform. I have no further suggestions at this time. I would rate this product a nine out of ten.