🇧🇷PT: Da teoria à prática: descubra como o Data Contract garante que sua IA no Microsoft Fabric trabalhe com dados limpos e precisos. Aprenda a guiar o GitHub Copilot para construir um Lakehouse blindado contra erros.

🇺🇸EN: From theory to practice: discover how the Data Contract ensures your AI in Microsoft Fabric works with clean and precise data. Learn how to guide GitHub Copilot to build a Lakehouse shielded against errors.

🇪🇸ES: De la teoría a la práctica: descubra cómo el Data Contract garantiza que su IA en Microsoft Fabric trabaje con datos limpios y precisos. Aprenda a guiar a GitHub Copilot para construir un Lakehouse blindado contra errores.

A structured look at how SQL workloads have evolved across deployment models — from on-premises SQL Server to SQL database in Microsoft Fabric — including what's Generally Available, what's in Preview, and what's coming next.

In large restaurant chains, keeping tabs on thousands of assets equipment, inventory, purchase orders, and transfers across hundreds of locations is a significant operational challenge. When that data lives in disconnected systems, gets reported manually, and arrives 24–48 hours late, the business is always flying behind the windshield.

This post walks through how we helped Restaurant Industry Company replace a fragile, manual reporting pipeline with a fully automated Micr... , reducing data latency from 48 hours to under 3, eliminating manual reporting effort, and laying the foundation for AI driven forecasting.

How a unified Lakehouse architecture in Microsoft Fabric improved data consolidation, reduced latency, and strengthened solar forecasting workflows.

Unleash the full potential of your Lakehouse! We show you the critical steps to deploy AI Data Agents in Microsoft Fabric—the breakthrough solution that allows anyone to talk to their data, not code it.

I recently got a chance to explore GraphQL, it was an absolute revelation. I realized I could finally have a more elegant way to expose the clean, curated data from our new Microsoft Fabric environment to modern applications. This blog post is a summary of that journey, explaining exactly what GraphQL is, why you need it, and how it transforms data access.

In this post I want to cover studying for Fabric certifications during the age of AI. In order to highlight some AI-related aspects to others.Ask Learn functionality

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.