What is your primary use case for Fabric Data?

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.

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.

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.

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.

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.

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.

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.

I used Fabric Data as part of my preparation for the Microsoft Fabric Data Engineer Associate certification. My hands-on practice covered building data pipelines, working with Lakehouse and OneLake storage, transforming data using Dataflow Gen2, and connecting outputs to Power BI for visualization. All work was done in a personal lab environment following Microsoft Learn guided exercises. I have additional observations about my main use case and my experience using Fabric Data for certification prep. I felt some features, particularly around the Dataflow Gen2 error handling and pipeline monitoring, lacked clear documentation at the time of my study.

My main use case for Fabric Data is for data ingestion, data transformation, and for data visualization. A quick, specific example of how I use Fabric Data for data transformation or visualization in my daily work is that I was working on a project for a leading pharmaceutical organization, and they were using Excel for complex calculations. There were more than 50 Excel sheets, and they were applying formulas to them. We transformed that project and the whole process by writing the transformation logic in Fabric Data Notebooks. We automated that process and did data visualization using Fabric Data BI service. We also applied incremental logic by using Fabric Data pipelines to ingest raw data using an SFTP source connection. Then, we used Fabric Data Notebooks for writing all the complex calculations and implemented the Medallion architecture. Afterwards, we used Power BI service to visualize the data.

My main use case for Fabric Data is to ingest data from our ERP system, go through medallion architecture, create integrations from it, and prepare the data for reports and integrations. A specific example of how I use Fabric Data for this process is the integration I made from our on-premises ERP system to Fabric where we take a quite large database with some tables containing two billion rows. I ingested these tables, and that was primarily my responsibility.

We are pulling data from an upstream tool, which is D365 ERP, and pushing it to a particular DB or lakehouse for BI purposes, to be consumed by the BI users. We are using Fabric Data notebooks to integrate systems using Microsoft Graph APIs, pulling data from D365 ERP and pushing it to a compliance tool where the business users will make decisions. Our main use cases for Fabric Data are building ETL and integrating systems. We are using data pipelines as well as Fabric Data notebooks majorly. We have also built an interim solution using SharePoint list and Fabric Data as an MDM solution, though it is not a true MDM.

As a Data Scientist, my main use case for Fabric Data is to manipulate data inside Lakehouses and develop Power BI reports and publish to them in Fabric. A quick specific example of how I have used Fabric Data recently is that I imported a dataset and used Python to run a notebook against the data to extract latitude and longitude for 300,000 addresses. I don't have anything else to add about how I use Fabric Data in my day-to-day work; it is a great tool and a good tool to have in your toolbox.

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

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.

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.

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.