Welcome to the second chapter of our Microsoft Fabric Tutorial Series. In this Fabric Lakehouse tutorial, you’ll learn how to create and manage Lakehouses using Microsoft Fabric’s unified data platform. We’ll walk you through the end-to-end workflow: provisioning your first Lakehouse, ingesting data, transforming it using notebooks, and analyzing it with SQL—all within the Fabric experience. This hands-on guide is designed for engineers, analysts, and data professio…
🎯 What You Will Learn in This Fabric Lakehouse Tutorial
- ✔️ Understand what a Lakehouse is and how it’s different from a traditional warehouse or data lake
- ✔️ Create your first Lakehouse in Microsoft Fabric
- ✔️ Ingest data into Lakehouse using pipelines and shortcuts
- ✔️ Use notebooks and Spark for data transformation
- ✔️ Query and analyze Lakehouse data using SQL analytics endpoint
- ✔️ Build a live Power BI dashboard on Lakehouse Delta tables
🔍 What is a Lakehouse in Microsoft Fabric?
A Lakehouse is a hybrid data architecture that combines the scale and flexibility of a data lake with the performance and querying capabilities of a traditional data warehouse. In Microsoft Fabric, Lakehouses are deeply integrated with OneLake, making your data instantly available to Spark, SQL, and Power BI engines.
🔧 Core Lakehouse Features:
- Delta table support (ACID transactions, schema enforcement)
- Auto-generated SQL analytics endpoints
- Direct integration with Power BI via Direct Lake mode
- No data duplication—everything stays in OneLake
🚀 Step-by-Step: Create Your First Lakehouse
🧱 Step 1: Open Microsoft Fabric and Create a Lakehouse
- Login to Microsoft Fabric
- Select your workspace or create a new one
- Click on + New → Choose Lakehouse
- Give it a name like SalesLakehouse and click Create
Once created, you will see tabs like Files, Tables, SQL Endpoint, and Notebook—these are your development tools.
📂 Step 2: Ingest Data into the Lakehouse
You can ingest data using one of the following methods:
- Pipeline: Use Dataflow Gen2 or Pipelines to ingest from sources like Azure SQL, Blob Storage, or REST APIs
- Shortcut: Link to existing files from OneLake or ADLS Gen2 without copying
- Manual Upload: Drag and drop CSV, JSON, or Parquet files into the Files tab
✅ Hands-on Lab (Optional):
Upload sales_data.csv and place it inside the /Files/raw folder.
🛠️ Transforming Data in Lakehouse Using Notebooks
📓 Step 3: Create a Spark Notebook
- From the Lakehouse view, click on New Notebook
- Choose
PySparkas the kernel
Sample Code:
# Read raw data
df = spark.read.option("header", True).csv("Files/raw/sales_data.csv")
# Clean data
df_clean = df.dropna().dropDuplicates()
# Write to Delta table
df_clean.write.format("delta").mode("overwrite").save("Tables/CleanSalesData")
📊 Step 4: Query Using SQL Endpoint
Click on the SQL Analytics Endpoint tab and run SQL queries directly:
SELECT TOP 10 * FROM CleanSalesData
ORDER BY Revenue DESC
📈 Visualize Lakehouse Data in Power BI
📌 Step 5: Connect Lakehouse to Power BI (Direct Lake Mode)
- Open Power BI Desktop or Power BI web portal
- Choose Get Data → More → Microsoft Fabric Lakehouse
- Authenticate and select your workspace and Lakehouse
- Choose Delta table
CleanSalesDataand click Load
You can now build live dashboards using native Direct Lake performance with zero imports.
📚 Resources to Deepen Your Lakehouse Skills
🔗 What’s Next in the Microsoft Fabric Tutorial Series?
In the next post, you’ll explore Fabric Data Ingestion Techniques—learning how to automate, schedule, and monitor robust pipelines across environments. 👉 Read next: Data Pipelines in Fabric– Microsoft Fabric Tutorial Series
