Over the last few months, I’ve been diving deep into Microsoft Fabric — especially the analytics side of things. After passing the DP-700, I set my sights on its counterpart: the DP-600: Fabric Analytics Engineer Associate certification.

I’m excited to share that I’ve officially cleared the DP-600 exam — and in this article, I’ll walk you through my preparation strategy, what the exam covers, and some tips to help you succeed if you’re planning to take it next.

If you’re diving into Microsoft Fabric, you’ve likely heard about its powerful capabilities for unified analytics. One of its standout features? Notebooks. Whether you’re coming from a data science, engineering, or BI background, notebooks provide a flexible space to explore, transform, and visualize data — all within the Fabric ecosystem.

In this guide, we’ll walk through how to get started with Notebooks in Microsoft Fabric — from creating your first notebook to running code and visualizing results. No fluff, just a practical walkthrough to help you hit the ground running.

As organizations move toward unified analytics platforms, Microsoft Fabric offers two powerful data storage models: Lakehouses and Data Warehouses. Each has unique advantages, and understanding when and how to use them is essential for effective data engineering and analytics.

This article explains the difference between Lakehouses and Warehouses in Fabric, shows how to create simple tables and load data, and includes links to practical exercises to get started.

Lakehouse vs. Data Warehouse in Microsoft Fabric
🔹 What Is a Lakehouse?
A Lakehouse combines the scalability of a data lake with the structure of a data warehouse. It stores structured, semi-structured, and unstructured data in open formats such as Parquet and Delta.

Key Features:

Supports files and tables in the same environment.
Ideal for big data analytics, data science, and machine learning.
Works seamlessly with Notebooks, Spark, and PySpark.

What Is a Data Warehouse?
A Data Warehouse in Fabric is optimized for structured data and BI reporting. It follows traditional SQL-based modeling and is best suited for OLAP workloads, business dashboards, and high-performance queries.

Creating Tables and Uploading Data
Let’s walk through how to create tables in both Lakehouses and Warehouses inside Microsoft Fabric.

🔹 In a Lakehouse:
Open your Lakehouse from the Fabric workspace.
Upload a CSV or Excel file.
Use the Data Engineering interface or a Notebook to create a Delta table

df = spark.read.csv("/Files/sales.csv", header=True, inferSchema=True)
df.write.format("delta").saveAsTable("SalesData")

In a Warehouse:
Go to your Warehouse in Fabric.
Use the SQL Editor to create a table:

CREATE TABLE Products (
ProductID INT PRIMARY KEY,
Name NVARCHAR(100),
Price DECIMAL(10,2)
);

3. Insert data manually or use the Load Data tool to import from OneLake, Excel, or an external source.

Practice Labs and Learning Resources
Here are some official and community resources where you can practice working with Lakehouses and Warehouses:

🔗Implement a Lakehouse with Microsoft Fabric - Training | Microsoft Learn
🔗Implement a data warehouse with Microsoft Fabric DP-602T00 - Training | Microsoft Learn
🔗 Sign in to your account

Choosing between a Lakehouse and a Warehouse depends on your workload. For data science and big data exploration, Lakehouse is ideal. For business intelligence and structured data analytics, the Data Warehouse provides better performance and SQL capabilities.

let’s connect on LinkedIn: https://www.linkedin.com/in/rufyda-abdelhadirahma/

Thinking of taking the DP-700 exam? Here’s how I passed it—and why it could change your career in data engineering with Microsoft Fabric.

DP-600 is a Microsoft certification called:
Implementing Analytics Solutions Using Microsoft Fabric

In short: If you want to prove that you can work with modern data tools and build end-to-end data solutions, this exam is for you.
Learning Microsoft Fabric is exactly what you need to pass it.

 What can you do with Fabric?

Here are just a few examples:
• Combine messy data from different places (Excel, SQL, APIs)
• Clean and prepare it with easy tools
• Visualize it in Power BI (right inside Fabric!)
• Run machine learning models
• Share reports with your team instantly
 Let’s break down the core building blocks of Fabric

 Tool  What it does ?

Lakehouse A place to store raw + processed data (like a folder in the cloud, but smarter)
Warehouse For structured, SQL-style analytics (think: tables and rows)
Notebook A coding environment for data science and AI
Power BI Build interactive dashboards and reports
Pipelines Automate data flows from source to report
Don’t worry if these sound new — we’ll explain each of them one by one in the next articles.

Microsoft Fabric is:
• A one-stop platform for all your data tasks
• Perfect for beginners who want to level up
• The core tool to learn if you're aiming for the DP-600 exam
• Easier than it sounds .. and kinda fun once you get into it 

let’s connect on LinkedIn: https://www.linkedin.com/in/rufyda-abdelhadirahma/

Facing issues with data integration, performance, or governance in Microsoft Fabric? This guide breaks down the most common challenges and offers practical, real-world solutions to help you get the most out of your Fabric environment.