Fabric Data Reviews

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.

I am leading the entire Fabric Data CI/CD project, where the development has already been completed in Fabric Data. I am here to enable CI/CD and environment segregation in Fabric Data, where I use Fabric Data CI/CD libraries. I also work on a data engineering project where I build pipelines from end to end.

I have used the Fabric Data CI/CD library and MD files to create the pipelines. I have also used Copy Data activities in Azure Data Factory.

Because it is under one ecosystem, our time has been saved. Cost has been saved but not as much as I expected it to be.

Money and time have been saved significantly. The training and cost of training for people has reduced because Fabric Data is quite easy to understand.

The best features are that the entire ecosystem is inside Microsoft and it is under a SaaS platform. I do not have to rely on any other tools or cross-functional tools to deploy or develop. The entire CI/CD, from development to testing to deployment, the entire operation can be done under the same Fabric Data platform.

The all-in-one system has been the most helpful for me.

Earlier we used to rely on different tools and had to purchase different enterprise-level tools. Different billing used to happen and they were not in line or were very inconsistent. Now that the entire thing comes under a single ecosystem, we do not have such issues.

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.

I have been using Fabric Data for four months.

Currently, as Fabric Data is new, Microsoft is constantly developing it. There were a lot of issues in the initial days, but now Microsoft is working and trying to make it better every day.

Fabric Data does not have scalability issues.

Customer support receives a rating of six out of ten because they themselves are trying to figure out what is new and what the issue is.

I did not switch anything. Since the start, I have been in Azure and Azure cloud only.

I was considering choosing Databricks, but we are Microsoft partners, so I did not.

The initial setup was smoother than other tools.

The implementation team can use Fabric Data properly.

My overall review rating for Fabric Data is six 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 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.

My main use case for Fabric Data is centralized data analytics and reporting in my organization, where I work on integrating data from multiple sources, transforming it, and building reporting solutions using Power BI. Fabric Data helps me handle data storage, preparation, and analytics on a unified platform, reducing dependency on multiple separate tools, while also improving collaboration between data engineering and reporting teams for scalable and efficient BI solutions.

In my recent reporting project, I had data coming from multiple sources including SQL-based transactional systems and manual business data, and we used Fabric Data to centralize the data into a single analytical environment. The main challenge was efficiently handling large datasets and reducing report refresh time using Fabric Data components such as Dataflows and Lakehouse integration, along with Power BI. We streamlined the transformation process and created a centralized semantic model for reporting, which helped improve report performance, reduced manual effort, and provided faster business insights for stakeholders, enhancing collaboration between data preparation and reporting layers.

Apart from centralized reporting analytics, I also use Fabric Data for improving data accessibility and scalability for business users, especially through its integrations with Power BI as I am a Power BI developer and Business Intelligence Engineer. I also explored pipeline-based data movement and data preparation workflows to reduce manual intervention and improve consistency in reporting. Overall, my focus has mainly been on using Fabric Data to simplify data integration, improve reporting performance, and support scalable BI solutions.

Fabric Data offers many features, but one that stands out to me is the unified platform approach, where data integration, storage, transformation, and reporting are all connected within the same ecosystem. I find the seamless integration with Power BI very valuable for creating a semantic model that enables efficient reporting for business users. Another strong feature is the Lakehouse concept, which helps in managing both structured and semi-structured data effectively for analytics use cases, along with pipeline-based orchestration and scalability for handling growing data volumes and reducing manual effort in data workflows.

The Lakehouse feature specifically helps my team by providing a centralized and scalable data storage layer where both structured and semi-structured data can be managed effectively. Earlier, data was spread across multiple systems and formats, making transformation and reporting complex, but with the Lakehouse approach, it became easier to organize, access, and process data for analytics and reporting use cases. What I value most is the seamless integration with Power BI, which simplifies data connectivity, semantic modeling, and report development without requiring multiple disconnected tools, improving collaboration between teams as data engineers and BI developers work more effectively within the same ecosystem. Scalability and pipeline orchestration are also useful for supporting growing data volumes and more automated workflows.

An additional key feature that I find valuable is the flexibility Fabric Data provides across data engineering, analytics, and reporting. It reduces tool fragmentation and helps teams collaborate more effectively while offering flexibility for scaling analytics solutions as business data grows. Overall, I see it as a strong platform for building modern end-to-end BI and analytics solutions.

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 been using Fabric Data for around four years.

We did see a positive return on investment through reduced manual effort, faster reporting cycles, and improved operational efficiency. For example, before centralizing an analytics workflow, generating consolidated business reports from multiple systems involved significant manual data preparation. After streamlining the process with Fabric Data, reporting effort was reduced significantly, with approximately 40 to 50 percent faster turnaround time for activities. While it may not directly reduce headcount, it helped teams work more effectively by automating and simplifying several analytics and reporting processes.

Regarding the experience with pricing, setup cost, and licensing, it is generally good from a scalability and integration perspective, especially for organizations already using Microsoft tools such as Power BI and Azure. The unified ecosystem helps reduce complexity compared to managing multiple separate analytics tools, although larger workloads and enterprise-scale usage require proper capacity planning and cost monitoring to optimize resource usage effectively. Overall, the experience has been positive and manageable from both setup and usability perspectives.

My advice for others looking into using Fabric Data is to first focus on building a strong data foundation and clearly defining the business use cases before implementing Fabric Data. Since it is a broad unified analytics platform, organizations should plan their architecture, governance, and data workflows properly from the start to maximize benefits. I recommend beginning with a phased approach, starting with reporting and centralized analytics, and gradually expanding to advanced data engineering and large-scale analytics workloads. Investing time in understanding the integration between Lakehouse, pipelines, semantic models, and Power BI is crucial as that integration is one of Fabric Data's greatest strengths. For organizations already using Microsoft technologies and Power BI, Fabric Data can provide a very strong and scalable end-to-end analytics ecosystem.

Overall, I think Fabric Data is a very promising and modern analytics platform that simplifies end-to-end data workflows by bringing data engineering, analytics, and reporting together into a unified ecosystem. Its integration with Power BI, centralized data management approach, and scalability make it especially valuable for organizations looking to modernize their analytics landscape. While still evolving, I see strong long-term potential for enterprise analytics and collaboration use cases, and my experience with the platform has been positive. I would rate this product a 9 out of 10.

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 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.

My main use case for Fabric Data is to extract, transform, and load the data.

To start a transformer and load the data using Fabric Data, I transfer the data into one big database for data analytics.

Additionally, the normalization of the database is critical and I use this database for data analytics.

The best features Fabric Data offers are its versatility, as you can use data fabric for many uses in one single platform.

This unique platform for all teams helps because you can use it for various needs such as data analytics, data engineering, and database administration.

Fabric Data has positively impacted my organization by accelerating the development of the software.

It has accelerated my software development because it is a platform that allows you to develop software with a GUI.

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.

I have been working in my current field for 10 years.

Fabric Data is stable based on my experience.

Fabric Data is scalable.

To clarify, I have not tried the changes regarding Fabric Data's scalability.

Microsoft support is the best for Fabric Data.

I would rate the customer support for Fabric Data as a 10.

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

Regarding my experience with pricing, setup cost, and licensing, I have one year of experience.

I am a partner with this vendor beyond being just a customer.

I have indeed seen a return on investment with Fabric Data.

I measured that return on investment through time savings, which reflects increased productivity.

My experience with the pricing for Fabric Data shows that it is a little expensive.

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

The advice I would give to others looking into using Fabric Data is to focus on data analytics. I have provided an overall rating of 9 for this product.