Fabric Origin Nexus

My main use case for Fabric Data is building data solutions for one of the retail firms in the US. I use Fabric to process source data, perform data processing, and provide analytical reports for end users.

Regarding my main use case with Fabric Data, the challenging part was that initially we identified a few challenges while using the deployment strategy and the deployment pipelines. However, the deployment process is now more streamlined and matured.

The best features Fabric Data offers include the integration of multiple services such as OneLake, which is one of the unique features and truly useful. The availability of the notebooks where I can use PySpark and SQL to process the data and perform all the transformations is one of the key features which is very helpful. The integration of the semantic model into OneLake is one of the unique features. These entire features help me in processing the data from the source to the target in a very seamless way.

Out of the features I mentioned, OneLake has made the biggest difference in my work.

OneLake and the shortcuts provided to read the source data from various other sources is one of the unique features which we found helpful. It is the easiest way to read the data from related sources. OneLake is very helpful to reference the data in any of the notebooks, use the same in the data warehouse, or use OneLake in the reports. It is a central and a single source of truth for the entire ecosystem.

Coming to the semantic model onto OneLake, this has helped us to speed up some of our reports by pointing the semantic model to read directly from OneLake via OneLake security. For Delta tables especially, we can directly read it from OneLake using OneLake security. This feature helped us to speed up some of the reports, including the processing time and the display time of the reports.

Shortcuts is one of the important features, along with the availability of the notebooks wherein we have the options to write the code in PySpark and SQL to perform all the heavy lifting of processing and transformations. Delta tables and the ease of use of the capacity are also important. We do not have to manage or maintain clusters, the number of nodes and all those things. The integration of Power BI into OneLake is also one of the important features.

Fabric Data has positively impacted my organization by providing a unified platform for the entire data for the customer which we are working on. It has simplified multiple things into the unified platform. This is an advantage compared to having data in a data lake, having a warehouse, using Synapse for the data warehouse and using Power BI with a separate license. This overhead has reduced. The unified model has brought everything into one single licensing model. The overhead of maintaining or managing the clusters and all those things has been simplified. Developers with various different skill sets, whether having PySpark knowledge, SQL knowledge or Power BI knowledge, can all be enabled to work on Fabric Data. This is one of the advantages that I have observed.

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 have been using Fabric Data for around one year.

I have experienced downtimes a couple of times, and Microsoft has provided communications on the downtimes.

Fabric Data's scalability has met my needs as my data and usage has grown. It is scalable. Comparatively, it provides a similar kind of experience that we have with other cloud services. It performs well regarding scalability.

The customer support for Fabric Data is really good. We have reached out to Microsoft multiple times because this is one of the earlier implementations in Fabric and we tried to set up Fabric deployment pipelines. We encountered some issues with shortcuts initially while deploying. To resolve all those things, we reached out to Microsoft. The customer support is on time and it is good. They also provided a review of the architecture for the implementation of Fabric that we did. Overall, it is a good experience.

I rate the customer support as ten because they have always responded, provided solutions, reached out and provided all the necessary details for us.

I have not used a different solution earlier because this has been implemented in Fabric from the beginning.

I have seen a return on investment with Fabric Data because Fabric is easier to set up compared to the setup required for separate services. It is a single license pricing model compared to setting up separate lakes like Azure Data Lake, Azure Synapse, and a separate Power BI license. The capacities we have include the highest F128, then F64, then F32. Compared to other services, this is a better priced model, provided the kind of services and the features that it offers end-to-end. It is better priced and easier to set up.

I can share a specific metric related to the customer that the overall cost we are able to reduce by implementing some of the best options available in Fabric. We are also able to reduce the report times and improve the performance overall.

Before choosing Fabric Data, I evaluated other options. One of them was having separate services such as a separate data lake, then using Synapse service, and then Power BI separately, and then trying to integrate everything into a unified layer. Because the end goal is to develop Power BI reports for executive leadership and internal teams, for the business users and for the business sales teams. The options evaluated are primarily related to Azure services. At some point, we also tried to evaluate Databricks. Compared to Fabric Data and Databricks, the ease of setup of Fabric Data and a unified layer made the difference. We do not have to move in and out of the cloud. Everything being in OneLake has tilted the decision towards Fabric Data.

My advice for others looking into using Fabric Data is to be careful while using shortcuts. Make sure that you understand how to create shortcuts and how to use them. While deploying, you have to have a clear-cut understanding on how they work. This is one area where we have struggled, so I would give that tip.

Fabric Data is one of the simplified unified platforms which actually makes developers and setup infra operations easy. It provides multiple features that we can use and explore for reporting and all those things. In addition to providing AI capabilities, which I have not explored much, I see that there are other capabilities provided. Overall, Fabric Data simplifies development in some ways and also the setup to a major extent. It is a good tool to use.

I rate this review nine out of ten.

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.

Fabric Data serves as my main solution for day-to-day operations, depending on the services we implement for our needs.

We use Fabric Data for deeply integrated fabrics, data flows, and more efficiently integrate it with Power BI for reporting models.

For any requirement with Fabric Data, if the source volume is less than a terabyte, or for day-to-day handling of data volumes in terabytes, Fabric Data is a good service that provides end-to-end services we can rely on.

Fabric Data offers best-in-class features including one unified platform for a data lake and data service, so we do not need to have separate storage or separate services for each function.

For example, when using Fabric Data with streaming data, we can integrate from the streaming application and then store it in the data lake.

The impact of Fabric Data on my organization is positive; for costing, it is very effective since it uses a capacity-based model.

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.

I have been using Fabric Data for around six months to one year.

Fabric Data is stable; scalability is managed by designing its metadata framework more effectively.

Customer support for Fabric Data depends on the volume and type of services the business fits into the solution.

The earlier solution was in staging.

Before choosing Fabric Data, we evaluated other options, but since our current source system is more focused on Microsoft-based services, we decided to go with Fabric Data.

I have seen a return on investment that is highly notable; the solutions I am getting from transitioning traditional ETL solutions to Fabric Data are remarkable.

My experience with pricing, setup cost, and licensing is limited; these factors are decided by the customer engaging this framework.

The accuracy and reliability of Fabric Data's output depend on how you design the solution based on the requirements.

My advice for others looking into using Fabric Data is to understand the existing services according to their requirements.

TCS is already a partner; for any suggestions or upcoming solutions, they will choose Fabric Data based on customer and organizational discussions.

Before concluding, I recommend considering capacity-based requirements when choosing Fabric Data.

My overall review rating for Fabric Data is 7 out of 10.

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.

Approximately 90% of our projects are based on Fabric Data because we are the data solution team, and we provide solutions that clients primarily request. From the last two years, most projects have been on Fabric Data. The remaining 10% involves on-premise solutions because we also work with some banking and telecom companies that prefer to avoid moving data to the cloud, so they use on-premise solutions like Informatica PowerCenter, Informatica BDM, and Denodo-related solutions.

We are currently only suggesting Fabric Data to clients. When they come with their requirements, we inform them about specific suggestions we can proceed with. They typically need solutions related to cost efficiency, performance, and comprehensive final reporting.

Customers usually use Fabric Data for warehousing because it is fundamentally a warehousing solution combined with business intelligence reporting. The second priority is the AI functionality to create AI modules. Since we provide a centralized solution, the complete client data from the company will be inserted into Fabric Data so they can easily apply AI models and business intelligence reporting.

The most valuable features or capabilities of Fabric Data vary based on requirements and different scenario needs since we are not moving to the OLTP system.

What I appreciate about Fabric Data is that it is easy to use and user-friendly. Additionally, it is cost-efficient. When clients come with solutions, their first and priority question is cost—how much they will be charged. Fabric Data offers an easy-to-use and cost-efficient solution.

Regarding Fabric Data's data integration feature, it is very good because of its compatibility. We have multiple connectors, and we can directly connect with the dynamic using Synapse Link. There is no requirement for any development, and we have direct connectivity with shortcuts for some cloud integration, meaning from AWS to Fabric Data. You do not need to implement any pipeline; it is simple to configure the connector, and all tables from AWS can be available within Fabric Data in minutes. The connectivity for cloud infrastructure is very good within Fabric Data.

Fabric Data's machine learning for predictive analytics is very good because the power engine with PySpark provides excellent performance impact. We can apply multiple different levels of AI modules, such as forecasting and other suggestions. This is effective because I also use Databricks and Data Robot, which are similar to Fabric Data, but Fabric Data has a larger picture making it easier to use.

Within Fabric Data, we have resources like Microsoft Purview, which provides governance capabilities. It is very easy to configure Fabric Data with Purview, and it provides complete lineage and metadata management easily. It requires only configuration with no extra development or additional inputs.

I find Fabric Data effective in real-time processing and providing timely insights for key business decisions. Fabric Data has provided PySpark capabilities along with webhook configurations. We can easily configure Fabric Data with real-time data, though there is a minimal separate cost associated with it. Based on the licensing cost, we can easily work with real-time data and also with near-to-real-time data.

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.

We have been using it for more than two years.

Regarding the stability and reliability of Fabric Data, I think it is good. I do not have any complaints about that. We have been using it for more than two years, and there have not been any major issues with resource availability or anything with the development team working. Fabric Data is active and available twenty-four hours a day, seven days a week.

The scalability of Fabric Data is very easy, but it depends on whether you have a dedicated server. If you do, you can avoid scaling up the server manually. By default, based on load processing, it automatically scales up. It is not a problem to manage; Fabric Data manages itself.

For the technical support of Fabric Data, we have a separate DevOps team and an administration team here. If we encounter any issues, the Microsoft ticket log creation process and the support team are good and cooperative, so I do not have any complaints beyond this.

I have participated in the initial setup of Fabric Data.

Participating in the initial setup of Fabric Data is very easy; you only need some administrative rights. It requires just two or three clicks, and the environment is ready for development and production easily. There is no extra effort or additional input; it is just a few clicks away.

Fabric Data is only affordable and cost-effective if you proceed with a long-term commitment and have a license for one or two years with completely dedicated servers. It will be cost-effective, but if we go with the pay-as-you-go and monthly basis, then it will be expensive. With pay-as-you-go, you have to manage most of the things yourself. You have to pause the servers and do some manual interventions.

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.