Introduction

Over the years I’ve worked on many different aspects of software and data projects, and one thing I’ve learned is that data engineering is rarely as clean as it looks in a presentation.

Source → Transform → Load → Done.

But in a real project, the source data may have missing values, incorrect data types, unnecessary columns, inconsistent values, and business rules that need to be applied before the data is ready for analytics.

That’s exactly why I wanted to create my latest Microsoft Fabric hands-on tutorial on Dataflow Gen2.

Rather than making another video explaining the definition of Dataflow Gen2, I decided to approach it the way I would approach a real project.

My Latest Microsoft Fabric Tutorial

n this video, I take a sales dataset and walk through the process of turning raw data into something that can actually be used for analytics.

The overall flow is:

Raw CSV → Dataflow Gen2 → Clean → Transform → Calculate → Lakehouse

And then I take it one step further by discussing how this can be integrated into a Fabric Pipeline for automation.

The dataset I’m using is a Microsoft-provided sample, which makes it easy for anyone following the tutorial to reproduce the exercise.

Why I Like Dataflow Gen2

One of the things I personally like about Dataflow Gen2 is that it gives me a visual, low-code way of performing many common data transformation tasks.

If you’ve worked with Power Query before, the experience will feel familiar.

For example, I can:

• Remove unnecessary columns
• Filter records
• Handle missing values
• Change data types
• Rename columns
• Create calculated columns
• Perform transformations
• Prepare data for downstream analytics

For many of these requirements, I don’t need to start by writing a large amount of code.

And that’s important to me.

I’m not against writing code—in fact, code is extremely important in data engineering. But I also don’t want to write code simply because I can.

I want to choose the right tool for the problem.

A Simple Example From the Tutorial

In the tutorial, I’m working with sales data containing fields such as:

SalesOrderID, OrderDate, CustomerID, LineItem, ProductID, OrderQty and LineItemTotal.

At first glance, it looks pretty straightforward.

But before loading this into a Lakehouse, I need to ask some basic questions:

Do I need every column?

Are there missing dates?

Are quantities valid?

Are my data types correct?

Can I derive additional business information from the existing columns?

This is where the real transformation work begins.

For example, I remove columns that aren’t required for my final analytics dataset.

Then I check for missing OrderDate values.

I also validate OrderQty and make sure I’m not passing invalid quantities into the downstream system.

After that, I standardize the data types and rename columns to make them easier for business users to understand.

Creating Business Logic

One of my favorite parts of the tutorial is creating a Unit Price column.

The source doesn’t directly provide Unit Price.

But we already have:

Line Total

and

Quantity.

So we can derive:

Unit Price = Line Total ÷ Quantity

This is a simple example, but it demonstrates an important concept.

Data engineering isn’t always about simply moving data from point A to point B.

A lot of the value comes from turning raw technical data into useful business information.

That’s the mindset I try to bring into my Fabric tutorials.

Where Does Dataflow Gen2 Fit in Fabric?

This is also an important question for anyone preparing for DP-700 or a Microsoft Fabric interview.

Fabric gives us several tools, and sometimes the difficult part isn’t knowing what each tool does.

The difficult part is knowing which tool to choose for a particular scenario.

For example:

Dataflow Gen2

I would consider Dataflow Gen2 when I need a visual, low-code data transformation experience using Power Query.

Pipeline

I would use a Pipeline when I need to orchestrate and automate different activities.

Notebook

If I have complex code-based transformations or need advanced Spark processing, a Notebook may be a better option.

Copy activity / Copy job

If my primary requirement is moving data from one location to another, a copy operation may be more appropriate.

So my approach is not:

“Dataflow Gen2 is the best tool.”

It’s:

“What problem am I trying to solve, and which Fabric capability is the right fit?”

I think this way of thinking is particularly important for both certification exams and real-world projects.

What About DP-700?

I’m also creating these tutorials because I’m preparing content for people working toward the Microsoft Certified: Fabric Data Engineer Associate (DP-700) certification.

When I prepare for or teach a certification topic, I personally prefer to connect the theory with a practical scenario.

Instead of remembering:

Dataflow Gen2 = data transformation

I’d rather remember:

“I have messy source data. I need to clean it, apply business rules, calculate additional fields and load the result into a Lakehouse. Dataflow Gen2 could be a good fit.”

That scenario is much easier to remember.

And more importantly, it reflects how we actually work in technology projects.

The Interview Question I Would Ask

If I were interviewing someone for a Fabric Data Engineer role, I might ask:

“You receive a daily CSV containing sales transactions with missing values and invalid quantities. How would you clean, transform and load this data using Microsoft Fabric?”

I wouldn’t necessarily be looking for someone to simply say:

“I’ll use Dataflow Gen2.”

I’d want to hear the reasoning.

What transformations would you perform?

How would you handle missing data?

How would you validate the data?

Where would you store the output?

How would you automate the process?

Would Dataflow Gen2 be sufficient, or would you combine it with a Pipeline or Notebook?

Those follow-up questions are often where the real interview discussion starts.

What I would actually deploy

Alongside my YouTube tutorials, I’ve created two Udemy resources based on the same philosophy.

🎓 Microsoft Fabric Interview Questions

I created this course for people who want to practice scenario-based Microsoft Fabric interview questions, rather than just memorizing definitions.

It covers areas such as:

• OneLake
• Lakehouse
• Warehouse
• Dataflow Gen2
• Pipelines
• Notebooks
• Spark
• KQL
• Data ingestion
• Security
• Performance
• Architecture scenarios

👉 Check out my Microsoft Fabric Interview Questions course:

Microsoft Fabric Interview Questions – Udemy

DP-700 Practice Test

I’ve also created a DP-700 Microsoft Certified Fabric Data Engineer Practice Set.

My goal with this practice test is to help you get comfortable with scenario-based questions and identify the areas where you need more preparation before taking the certification exam.

👉 Check out my DP-700 Practice Test on Udemy:

DP-700 Microsoft Certified Fabric Data Engineer Practice Set – Udemy

🙌 Why I'm Making These Videos

For me, creating these videos isn’t just about putting another certification tutorial on YouTube.

I’ve spent many years working in IT, and I’ve seen how quickly the technology landscape changes.

What worked a few years ago isn’t necessarily how we would design a solution today.

That’s why I’m enjoying learning and teaching Microsoft Fabric.

I try to take a topic, simplify it, put it into a realistic scenario, and then explain how I would approach the problem as an IT professional.

Hopefully, that makes the learning experience a little more practical.

Follow Along

If you’re preparing for DP-700, working with Microsoft Fabric, or preparing for a Fabric Data Engineer interview, I would love to have you along for the journey.

I’ll continue sharing:

🔹 Microsoft Fabric hands-on tutorials 🔹 DP-700 preparation 🔹 Fabric interview questions 🔹 Scenario-based questions 🔹 Data engineering concepts 🔹 Real-world architecture discussions 🔹 Tips from my own experience working in IT

If you find this content useful, please subscribe to my YouTube channel and follow me here on LinkedIn.

And I’d genuinely like to know:

What Microsoft Fabric topic should I cover in my next tutorial?

Drop your suggestion in the comments. 👇

If there’s a particular DP-700 topic or Fabric interview scenario you’re struggling with, mention that too. I may turn it into my next video.

Thanks for reading, and thanks for supporting my learning journey and content.

— Ravi Singh

#MicrosoftFabric #DP700 #FabricDataEngineer #DataflowGen2 #MicrosoftFabricInterview #DP700Certification #DataEngineering #Lakehouse #OneLake #PowerQuery #FabricTutorial #MicrosoftCertification #DataEngineer #Azure #DataAnalytics

About the author

R, Singh (25/08/2026) (4) How I Use Microsoft Fabric Dataflow Gen2 in a Real-World Data Engineering Scenario | LinkedIn