I Used Microsoft Copilot for Fabric and Saved Hours: How AI Turned My Data Engineering Burnout into Flow

Ever spent what feels like an entire weekend transforming a “simple” CSV file into something usable? In this episode, I walk through how Microsoft Copilot in Fabric turned those soul‑crushing hours of ETL, Spark debugging, and fiscal calendar building into tasks that now take minutes instead of days. You’ll hear the real story behind that shift—from skepticism and fear of being replaced to the moment automation finally started to feel like a partner instead of a threat.

We start with the painful reality of legacy workflows: manual conversions to Delta Parquet, brittle scripts, and late‑night debugging sessions that quietly eat your weekends. Then we look at what changes when you move those workflows into Fabric with Copilot fully in the loop: describing transformations in natural language, letting Copilot generate and fix code, and reserving your energy for architecture, data quality, and business logic instead of boilerplate. Along the way, you’ll see how prompt‑review‑improve becomes the new core loop of data engineering.

From there, we dive into concrete use cases that saved the most time: generating fiscal calendars with configurable parameters, building ETL pipelines “in plain English,” and using magic commands like %%create_chart and %%fix_errors to keep momentum instead of getting stuck in low‑value debugging. Real‑world demos from people like Inder Rana, Dan Taylor, and Greg Bowmont show that this isn’t theory—it’s already reshaping how teams work in production.

Finally, we tackle the emotional and organizational side: what it means when your value as a data engineer is no longer tied to how many lines of code you write, but to the problems you solve. You’ll hear how Copilot becomes a decoder ring for legacy code, a safety net for complex transformations, and a creativity unlock that lets you spend more time designing better data products—and less time fighting your tools.

WHAT YOU LEARN

• Why classic ETL, Spark scripts, and fiscal calendar projects cause burnout—and how Copilot in Fabric changes that.
• How to use natural language prompts, prompt‑review‑improve loops, and magic commands to build and debug data workflows faster.
• Concrete examples of Copilot handling CSV‑to‑Delta conversions, complex transformations, and fiscal calendars in minutes.
• How Copilot helps you understand and refactor legacy codebases instead of fearing them.
• What shifts in your role as a data engineer when automation takes over the repetitive work and your value moves to design and problem‑solving.

CORE INSIGHT

The core insight of this episode is that Microsoft Copilot in Fabric doesn’t make data engineers obsolete—it frees them from the slow, repetitive parts of the job so they can finally focus on the work that actually requires human judgment. When ETL, calendar generation, and even legacy‑code archaeology can be expressed in natural language and iterated with Copilot, the bottleneck moves from “how fast can I type Spark code” to “how clearly can I define the problem and design the data product,” which is where your unique value lives.WHO THIS IS FOR

• Data engineers and analytics engineers who feel burned out by repetitive ETL and transformation work.
• Fabric users curious whether Copilot is ready for serious, production‑grade data workloads.
• Tech leads and architects deciding how far to lean into AI‑assisted development inside data platforms.
• Developers inheriting legacy data codebases who need faster ways to understand, fix, and modernize them.

ABOUT THE HOST

Mirko Peters is a Microsoft 365 and cloud consultant who lives at the intersection of data platforms, modern work, and AI‑assisted development. He helps organizations move from manual, hero‑driven workflows to context‑driven systems in Microsoft 365, Fabric, and Azure where automation and AI take over routine tasks while people focus on design, governance, and impact. In M365.FM, Mirko turns long, hard‑won lessons—from burnout and brittle pipelines to Copilot‑powered flow—into practical stories and patterns you can apply in your own environment.

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