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.

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.

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.