Fabric Notebooks for Data Transformation and ML: How to Replace Click‑Heavy ETL with Transparent, Scalable Code on Your Lakehouse

Ever wrangled data in Power BI and thought, “There has to be an easier way to prep and model this—without a maze of clicks”? In this episode, I show how Fabric Notebooks let you control every stage, from raw Lakehouse data to a clean dataset ready for ML, all inside a Python or R environment that feels natural to devs and analysts alike. Instead of hiding transformations behind UI steps and scattered tools, you centralize logic as code that is transparent, testable, and repeatable right where your data lives.

We start by breaking the clicks‑and‑drag cycle most teams rely on: Power Query chains, Excel patches, and fragile scripts that quietly drift over time. You’ll hear why this patchwork creates “spreadsheet archaeology” every time a column name changes or a step goes missing—and how Fabric Notebooks replace that with one source of truth where every cast, filter, and join is explicit code backed by Spark. The result is fewer broken refreshes, fewer mystery numbers, and a workflow you can actually explain to new team members and auditors.

From there, we walk through a realistic end‑to‑end journey. You’ll see how to pull raw Lakehouse tables into a Notebook, clean and join messy datasets, engineer features, and write the results back as curated tables that Power BI or ML pipelines can use immediately. Using examples like churn prediction and multi‑source sales analysis, we show how the same scripts scale from hundreds of thousands to millions of rows without changing tools, exports, or “final_v2” files.

By the end, Fabric Notebooks won’t just look like another editor—you’ll see them as the backbone of a more reliable analytics and ML workflow. You’ll walk away with a mental model where the Lakehouse holds your data, notebooks hold your logic, and everything else—dashboards, reports, models—builds on top of a transformation layer you fully control.

WHAT YOU LEARN

• Why traditional Power BI + Excel + script patchworks create hidden data quality and governance problems.
• How Fabric Notebooks centralize transformation logic as Python/R code running directly against Lakehouse data with Spark.
• How to go from raw tables to cleaned, joined, feature‑rich datasets ready for dashboards or ML in one notebook flow.
• How code‑based transformations improve transparency, repeatability, and troubleshooting compared to click‑only UIs.
• Why teams burned by lost Power Query steps and “final_v2” files are moving to notebook‑driven pipelines in Fabric.

CORE INSIGHTThe core insight of this episode is that the real upgrade with Fabric Notebooks isn’t just using Python or R—it’s replacing fragile, click‑driven ETL chains with transparent, versionable code that runs where your data lives. When your transformations move into notebooks on top of the Lakehouse, you stop fighting missing steps and broken refreshes and start building analytics and ML workflows you can scale, debug, and trust.WHO THIS IS FOR

• Power BI and analytics teams tired of juggling Power Query, Excel, and ad‑hoc scripts for every new dataset.
• Data engineers and scientists who want Spark‑scale transformations without leaving the Microsoft Fabric environment.
• Architects designing Lakehouse‑centric analytics and ML workflows on Fabric.
• Leaders who want fewer “mystery numbers” and more auditable, repeatable data preparation.

ABOUT THE HOSTMirko Peters is a Microsoft 365 and cloud consultant and the host of M365.FM, focused on modern work, security, and data architectures that hold up in production. He helps organizations move from patchwork BI setups and fragile ETL flows to context‑driven systems on Microsoft 365, Fabric, and Azure where notebooks, Lakehouse, and governance work together. In M365.FM, Mirko turns longform experiments—like rebuilding data prep on Fabric Notebooks—into practical stories and patterns listeners can apply in their own environments.

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