My main use case for Microsoft Fabric is data engineering. A specific example of something my team has used Microsoft Fabric for is to collect information from many sources and put it in the data warehouse.
Analyst at Cepstrum(EEE Students Society), IIT Guwahati
Real User
Top 5
Mar 4, 2026
My usual use cases for Microsoft Fabric involve using it as a unified platform where we perform all data engineering tasks, all data analysis tasks, and all data ML models on the same platform because they are integrated together. When someone creates a pipeline and stages the data, the data can be used by all different data analysts, data engineers, or data scientists so that the data remains consistent across the platform and there would not be any data issues; we stick to one data source. That's one advantage. All these services are so integrated in Microsoft Fabric that if someone wants to do visualization, they can use the same data and connect to Power BI directly from there. If someone wants to do ML modeling, they can connect to Python and run the code for analysis. Therefore, it is a unified platform where they can perform all tasks in one place. I use the Data Pipeline feature in Microsoft Fabric. We use Data Pipelines in Microsoft Fabric to stage our data from different sources according to our requirements. We have seven or eight admin systems for the client that I work with, and based on our requirements such as operations and customer service, we stage the data using the Data Pipelines. We do not want all the billions of records; we just stage to five years or two years according to our requirement and we perform analysis on that. Therefore, it helps us determine what we want.
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My main use case for Microsoft Fabric is data engineering. A specific example of something my team has used Microsoft Fabric for is to collect information from many sources and put it in the data warehouse.
My usual use cases for Microsoft Fabric involve using it as a unified platform where we perform all data engineering tasks, all data analysis tasks, and all data ML models on the same platform because they are integrated together. When someone creates a pipeline and stages the data, the data can be used by all different data analysts, data engineers, or data scientists so that the data remains consistent across the platform and there would not be any data issues; we stick to one data source. That's one advantage. All these services are so integrated in Microsoft Fabric that if someone wants to do visualization, they can use the same data and connect to Power BI directly from there. If someone wants to do ML modeling, they can connect to Python and run the code for analysis. Therefore, it is a unified platform where they can perform all tasks in one place. I use the Data Pipeline feature in Microsoft Fabric. We use Data Pipelines in Microsoft Fabric to stage our data from different sources according to our requirements. We have seven or eight admin systems for the client that I work with, and based on our requirements such as operations and customer service, we stage the data using the Data Pipelines. We do not want all the billions of records; we just stage to five years or two years according to our requirement and we perform analysis on that. Therefore, it helps us determine what we want.