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.

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