Fabric personas were originally designed to break down the various functional roles within Microsoft Fabric—such as Power BI, Data Factory, Data Activator, Data Engineering, Data Science, Data Warehouse, and Real-time Analytics—into more manageable, bite-sized sections. The goal was to prevent users from feeling overwhelmed by the platform’s breadth. However, this feature has since been discontinued, as it did not effectively communicate the seamless integration between these roles. Still, the underlying concepts can be useful when thinking about how you might approach Fabric from a functional standpoint.

Do you like the change on one large white canvas, or did personas have a use for you? Let us know in the comments below.

We hope you enjoyed this conversation on personas in Microsoft Fabric. If you have questions or comments, please send them our way. We would love to answer your questions on a future episode. Leave us a comment and some love ❤️on LinkedIn, X, Facebook, or Instagram.

The show notes for today's episode can be found at Episode 285: Who is Using Microsoft Fabric. Have fun on the SQL Trail!

Once you have your data stored in OneLake, you'll be ready to start transforming it to improve it's usability, accuracy, and efficiency.

In this episode of the podcast, Belinda Allen takes us on a delightful journey through Data Flows, Power Query, Azure Data Factory (Pipelines), and discusses the merits of shortcuts. We also learn about a handy way to manually upload a table if you have some static data you need to update.

There are many tools and techniques that can be used for data ingestion and transformations. And while some of these options we discuss will be up to individual preference, there are pros and cons to each. One of the blessings and curses of Fabric is that there are many ways of achieving the same result, so what you choose may depend on the nature of the data you have and your goals, but might also be dictated by personal experience.

We hope you enjoyed this conversation with Belinda on ingesting and transforming data in Microsoft Fabric. If you have questions or comments, please send them our way. We would love to answer your questions on a future episode. Leave us a comment and some love ❤️on LinkedIn, X, Facebook, or Instagram.

The show notes for today's episode can be found at Episode 284: The Four-Letter Word ETL - Data Movement. Have fun on the SQL Trail!

Microsoft Fabric offers two enterprise-scale, open-standard format workloads for data storage: Warehouse and Lakehouse. Which service should you choose? In this episode, we dive into the technical components of OneLake, along with some of the decisions you’ll be asked to make as you start to build out your data infrastructure.

These are two good articles we mention in the podcast that could help inform your decision on the services to implement in your OneLake.

• Microsoft Fabric Decision Guide: Choose between Warehouse and Lakehouse - Microsoft Fabric | Microsoft Learn
• Lakehouse vs Data Warehouse vs Real-Time Analytics/KQL Database: Deep Dive into Use Cases, Differences, and Architecture Designs | Microsoft Fabric Blog | Microsoft Fabric

We hope you enjoyed this conversation on the nuances of data storage within Microsoft OneLake! If you have questions or comments, please send them our way. We would love to answer your questions on a future episode. Leave us a comment and some love ❤️ on LinkedIn, X, Facebook, or Instagram.

The show notes for today's episode can be found at Episode 283: Data Lakehouse vs Data Warehouse vs My House. Have fun on the SQL Trail!