I Used Microsoft Copilot for Fabric and Saved Hours—Here’s How

From Code Cruncher to Creative Thinker: How Microsoft
Copilot in Fabric Rewired My Data Engineering Journey


Ever spent what felt like an entire summer afternoon just
transforming a CSV file? I have—and to say it sapped my
motivation would be an understatement. But that was before
Microsoft Copilot entered the chat. In this post, I’ll share the
winding, sometimes embarrassing, sometimes revelatory path I took
from dreading routine data engineering work to rediscovering why
I loved building things with code in the first place—all thanks
to a little AI magic (and a few hard-learned lessons).


When Burnout Met Automation: A Cautionary Tale


I used to lose entire weekends to CSV file conversions. Not
kidding. My Saturdays would dissolve into a blur of error
messages while debugging Spark code that refused to cooperate.
Coffee cups would pile up as the sun went down, and I'd realize
another day had vanished into the digital void.


Sound familiar?


The Weekend-Eating Monster


Converting files from CSV to Delta Parquet tables was my personal
nemesis. What should have been simple became a soul-crushing time
sink. I'd start Friday evening thinking, "This'll take an hour,
tops." By Sunday night, I'd be questioning my career choices.


Research backs up my pain – automation can reduce routine task
times by up to 40%. But knowing that didn't help
when I was knee-deep in code errors.


Skepticism: My Default Setting


When Copilot promised to handle these tasks, I laughed.
Seriously? Hand over my code to an AI assistant? The trust issues
were real.


* What if it made mistakes I wouldn't catch?


* What if it created more problems than solutions?


* What if I became... replaceable?


But desperation eventually trumped skepticism.


Old Me vs. New Me


The transformation was almost embarrassing:


Old me: Spent 6+ hours creating a fiscal
calendar, cursing at my screen.New me: Types a
prompt, reviews the generated code, done in 15 minutes.


Manual data transformation tasks that once devoured my weekends
now take minutes. ETL workflows that used to require days of
coding and debugging? Handled through natural language prompts.


"Sometimes, freeing yourself from a tedious workflow is
the most creative thing you can do." – Inder Rana


Rana's words hit different now. The relief of letting go was
unexpected. I found myself having actual free time. I
rediscovered hobbies. I remembered what my family looked like.


The Surprising Aftermath


The biggest shock wasn't the efficiency gain - it was the mental
space that opened up. Without the dread of endless debugging
sessions, my mind wandered to bigger questions and creative
solutions.


Yes, I still review everything Copilot generates. Yes, I
sometimes need to tweak the code. But the 40% time savings? In my
case, that's a conservative estimate.


My burnout didn't just meet automation. It was thoroughly
defeated by it.


The Lost Art of Prompt Engineering (Or: Talking To Robots
For Fun And Profit)


I never thought I'd develop a creative relationship with an AI,
but here we are. Writing prompts for Copilot has somehow become
one of the most unexpectedly creative parts of my job as a data
engineer.


Remember when programming meant memorizing exact syntax? Those
days feel distant now.


The Accidental Monster Factory


Last month, I was exhausted after a long day of data wrangling.
My brain was fried. I needed to create a simple data
transformation table, but somehow typed: "create fantasy monster
table with damage stats and special abilities."


Copilot's response? A bizarre mix of SQL syntax and fantasy RPG
content that made absolutely no sense. It tried to create columns
for "acidBreath" and "tentacleCount" alongside my actual data
fields.


I laughed for five minutes straight. Then realized something
important: I was talking to my development environment. Not
coding. Talking.


The Prompt-Review-Improve Loop


I've developed a workflow now:


* Write a natural language prompt


* Review what Copilot generates


* Refine my prompt with more details


* Repeat until perfect


It's less like programming and more like... coaching? Directing?
Whatever it is, it's changing how I approach problems.


Learning From The Pros


Industry demos have been eye-opening. Inder Rana showed how
Copilot could read files from CMS prescription folders into Spark
data frames with just conversational prompts.


Dan Taylor's demo converting Azure SQL data into date tables blew
my mind. As he said,


"The art of prompt engineering is the new craft for data
engineers."


I'm starting to believe him.


Getting Complex


My prompts have evolved beyond simple tasks. Now I'm asking for
column conversions, data type transformations, and even new
calculated columns based on business logic.


Sometimes my requests go sideways—I once got a perfect poetry
analysis instead of database code because I wasn't specific
enough. But that's part of the learning curve.


This new interface—natural language—feels more intuitive than
traditional scripting ever did. It's not perfect. You need human
oversight. But I'm spending more time thinking about what I want
to accomplish rather than how to accomplish it.


And honestly? That feels like progress.


ETL in Plain English: Goodbye Cryptic Scripts


Remember the old days of ETL? I sure do. A mess of scripts
sprawled across multiple files, confusing data type conversions,
and those dreaded broken data pipes that would bring everything
crashing down at 2 AM. Good times... not.


From Chaos to Conversation


Now? I literally just describe what I want to Copilot:


"Pull last quarter's sales data from our SQL database,
clean up the null values in the customer_id field, and create a
summary table with regional totals."


And just like that, Copilot assembles the code on the fly. No
more hunting through Stack Overflow or deciphering cryptic
documentation. It's almost unfair how simple it's become.


Magic Commands That Feel Like Cheating


The chart magic commands? Pure wizardry. Instead of spending
hours tweaking visualization code, I just type something like
%%create_chart sales by region and boom—instant visualization.


And don't get me started on %%fix_errors in notebooks. That
command has saved me countless debugging hours. It feels like
having a senior developer looking over my shoulder, catching
mistakes before they cause problems.


When Copilot Sees What You Don't


Last week, I was transforming some customer data when Copilot
politely suggested: "I notice you're trying to join these tables
on different column types. Would you like me to add a conversion
step?"


I hadn't even spotted the issue! That would have been hours of
debugging down the drain.


Trust, But Verify


Is every Copilot suggestion perfect? Nope. Sometimes it generates
code that looks plausible but doesn't quite work for my specific
scenario. But here's what I've noticed: the mistakes are becoming
fewer, and I'm getting better at prompting it correctly.


* The tedious parts of ETL now feel almost playful


* My focus has shifted from fixing code to designing workflows


* Human review is still essential, but much less painful


As Josh de put it: "With Copilot, describing data flows
in plain English isn't just possible—it's liberating."


I'm not throwing away my coding skills anytime soon. But I am
embracing a new reality where ETL creation has transformed from
slow and tedious to fast and, dare I say, enjoyable. And that's
something worth celebrating.


From Days to Minutes: Fiscal Calendars Without the
Fuss


I still get that sinking feeling when I think about fiscal
calendar projects. You know the ones—tedious, time-consuming
table creation that somehow always lands on your desk.


For years, I'd block out entire afternoons (sometimes days) to
build these calendars from scratch. Coding each parameter,
double-checking date ranges, fixing the inevitable bugs. It
was... painful.


The Game-Changer Approach


Then I saw Greg Bowmont's demonstration. My jaw literally
dropped.


He showed how Copilot could generate custom fiscal date calendars
almost instantly. Not in days. Not in hours. In
minutes.


"Automating the fiscal calendar put hours back into my
quarter. That's ROI you can feel." – Greg Bowmont


What used to consume half my week now takes less time than my
coffee break. That's not an exaggeration—I timed it!


The Secret Sauce: Configurable Parameters


* Column specifications tailored to your needs


* Flexible data types (no more conversion headaches)


* Custom date ranges that align with any fiscal structure


These configurable parameters change everything. Instead of
building from zero, I simply tell Copilot what I need, and it
generates the base code instantly.


A Wild Thought


Imagine a world where finance teams build their own fiscal
calendars without ever opening a code editor. Where they don't
need to wait for IT or data engineering to find time in their
sprint.


We're surprisingly close to that reality. The finance director in
my company—who has zero coding experience—recently used my
Copilot prompt template to generate a custom calendar for a
special project.


The Human Touch Still Matters


I'm not saying it's perfect right out of the box. A quick review
is still necessary—tweaking date formats here, adjusting column
names there. Sometimes business-specific calculations need
adding.


But starting with 90% of the work done? That's a game-changer.


When I think about all those days I spent hunched over fiscal
tables... well, I wish I could get those hours back. At least
now, with Copilot generating the heavy lifting, I can focus on
the interesting parts of data engineering instead.


Lost in Legacy Code? Copilot as Decoder Ring


We've all been there. That dreaded legacy codebase nobody wants
to touch. The one with sparse documentation and cryptic variable
names that make you question your career choices.


Last month, I inherited "the beast" - a 15,000-line monstrosity
written by a developer who left three years ago. My stomach
dropped when my manager cheerfully assigned it to me.


The Legacy Code Nightmare


Normally, I'd spend days just trying to understand what the code
actually did, let alone fix the reported bugs. But this time was
different. I had Copilot in my corner.


I opened the first file in a notebook and asked Copilot to
summarize it. Within seconds, it outlined the core functionality,
identified key dependencies, and even flagged potential issues in
the implementation.


Wait, what? That would've taken me hours to
figure out on my own.


Real-Time Code Translation


As I dug deeper, Copilot continued to amaze me:


* It explained complex functions in plain English


* Generated helpful inline comments


* Suggested better approaches for problematic sections


* Identified unused variables and redundant code


The debugging assistance was particularly impressive. When I hit
a strange error, Copilot explained not just what was wrong, but
why it was happening - context I would've spent ages tracking
down.


"Decoding someone else's work used to take me days. Now I
get my bearings in minutes." – Josh de


Josh's experience mirrors mine perfectly. The time saved in
orientation and troubleshooting is honestly hard to overstate.


Not Quite Magic


Is Copilot perfect? Of course not. I still caught a few instances
where it misinterpreted subtle business logic. Human eyes remain
essential, especially for domain-specific nuances that aren't
explicit in the code.


Sometimes I think Copilot should grade my code comments too.
"This comment is useless. Try explaining WHY instead of WHAT."
I'd probably become a better developer!


But even with its limitations, Copilot has fundamentally changed
how I approach legacy code. What was once a dreaded assignment is
now almost... interesting? I'm uncovering the logic and intent
behind complex codebases faster than ever before.


That project I expected would take weeks? I had a working fix in
three days. My manager thinks I'm a genius. I'm not telling if
you won't.


The Social Side: Bridging the Technobabble Gap


Remember those awkward meetings where I'd try explaining complex
data joins to my product manager? Eyes glazing over within
minutes was the norm. Not anymore.


Breaking Down the Wall


Last month, I faced explaining a particularly nasty multi-table
join to our non-technical product team. I braced myself for the
usual blank stares and polite nods.


Instead of my usual PowerPoint slides filled with SQL gibberish,
I brought up our new Copilot-powered semantic model connected to
Power BI. Something magical happened.


"The barrier between technical and business teams
cracked—not with a bang, but with a semantic link."


For once, the product manager actually understood the data
relationship. She even started asking intelligent questions about
the underlying patterns! I wasn't speaking a foreign language
anymore.


What Changed?


* The semantic models translated my technical jargon into
business contexts automatically


* Team members could interact directly with reports in notebooks
and Power BI


* Interactive elements let non-technical folks explore data their
way


* Real-time questions got answered without me playing translator


The bottlenecks disappeared. No more waiting for me to interpret
every data question or build custom reports for simple inquiries.


Unexpected Benefits


What I didn't anticipate was how quickly our team's overall data
literacy improved. When people can interact with data naturally,
they actually start using it.


Our marketing director, who once proudly declared herself
"allergic to spreadsheets," now regularly explores customer
segmentation data herself. Last week, she spotted a trend I had
completely missed!


Better yet? Our decision-making has improved. When everyone
understands the data, we make fewer assumptions and more
evidence-based choices.


Perhaps the biggest surprise was during our quarterly review. For
the first time ever, our executive team asked fewer clarifying
questions and more strategic ones. We spent the meeting
discussing implications rather than explaining basic concepts.


Who knew that semantic models and Copilot would become the
universal translators we never knew we needed?


Security: The Sober Second Thought


I almost messed up big time last week. There I was, rushing to
share some data insights with my team when Purview flagged me.
I'd nearly sent sensitive customer data to our entire department.
Yikes.


That heart-stopping moment made me realize something: for all the
speed and magic Copilot brings to my workflow, security can't be
an afterthought.


My Close Call


SharePoint literally saved me from a potential data breach. The
system recognized the sensitive content and blocked the share,
prompting me to review the permissions. I felt both embarrassed
and relieved.


Since then, I've become somewhat obsessive about our security
protocols:


* Tightening permissions on all our data sources


* Applying sensitivity labels to everything
(even stuff that seems harmless)


* Running weekly security reports to catch anything unusual


Putting Guardrails on Copilot


Here's something not everyone realizes: Copilot can be
controlled. We've implemented Data Loss Prevention (DLP) policies
that restrict what Copilot can access based on sensitivity
labels.


For really sensitive projects, I've even used PowerShell to lock
things down further. This little command has become my best
friend:


Set-SPOSite -Identity [site URL] -SearchScope "Site"


This limits search to just that specific site, preventing Copilot
from pulling in data from places it shouldn't.


Finding Balance


I still love the productivity boost Copilot gives me. But now I
approach it with what I call "the sober second thought" – that
pause to consider the security implications before diving in.


"You can automate a lot, but you can't automate good
judgment."


That quote from our CISO now hangs on my virtual desktop.


The tools are there – Purview reporting, SharePoint Advanced
Management, granular permissions – but they need a human to
implement them thoughtfully.


I've learned that speed and convenience mean absolutely nothing
without robust governance. In fact, they can be downright
dangerous.


My workflow now includes regular check-ins with our security
team, reviewing who has access to what, and making sure our DLP
policies align with how we're actually using Copilot in practice.


It's a bit more work upfront, but it lets me sleep at night. And
honestly? I'd rather spend 15 minutes on security protocols than
15 hours dealing with a data breach.


The Data Engineer's Renaissance (And What Comes
Next)


Looking back on my journey, I'm struck by how dramatically my
role has evolved. I've transformed from a code grunt—spending
endless hours on repetitive tasks—to a creative thinker with
space to innovate, all thanks to Copilot in Fabric.


The shift wasn't immediate. I was skeptical at first (aren't we
all with new tech?). But watching those hours of manual coding
shrink to minutes changed everything for me.


I'm not alone in this experience. Industry voices like Inder Rana
and Josh de have become advocates for this thoughtful integration
of AI. They emphasize something crucial: how we use these tools
matters as much as that we use them.


As Josh put it during a recent presentation,


"Copilot won't do your job for you, but it might finally
let you do your best work."


What Comes Next?


The future looks incredibly promising. I've already noticed my
prompt engineering skills improving—I'm getting better results
with more nuanced instructions. This is just the beginning.


More AI tools are heading our way. Microsoft's vision for Copilot
isn't static; it's evolving rapidly. The combination of human
creativity and automation is creating new potential for what data
engineers can accomplish.


What surprises me most? How Copilot has encouraged me to try
approaches I would have dismissed as too complex or
time-consuming before. It's given me permission to experiment.


This isn't just a handy script or convenient shortcut—it's a true
paradigm shift. The industry voices echo this sentiment clearly:
ignore AI at your peril.


For skeptics (and I was one), my encouragement is simple: try it.
Especially if you're doubtful. The transformation in how I
approach problems, collaborate with teammates, and think about
solutions has been profound.


As data engineers, we're experiencing a renaissance. Our role
isn't diminishing—it's expanding. We're moving from code
mechanics to solution architects, from data plumbers to insight
creators.


The tools will continue evolving. Our skills must too. But one
thing is certain—the future belongs to those who can blend
technical expertise with AI collaboration.


And frankly, after seeing what's possible, I wouldn't want it any
other way.


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