Introduction

Microsoft Fabric offers multiple storage and processing solutions for different analytical needs, including lakehouses, warehouses, and eventhouses. For the DP-600 certification exam, understanding when to use each option is crucial for designing efficient data architectures.

In this tutorial, you'll learn how to differentiate between lakehouses, warehouses, and eventhouses in Microsoft Fabric, helping you choose the best option for your use case. We'll walk you through the characteristics of each and provide guidance on selecting the right solution for your workload.

Data Storage Options in Microsoft Fabric

Microsoft Fabric provides three primary data storage and processing solutions:

  • 🏞️ Lakehouse: Combines the flexibility of a data lake with structured querying capabilities, using Delta Lake for storage.
  • 🏢 Warehouse: A fully managed enterprise-scale SQL-based data warehouse optimized for business intelligence and reporting.
  • ⚡ Eventhouse: A specialized solution designed for high-volume event streaming and real-time analytics, using KQL databases for storage.
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Each option caters to different workloads and use cases, making it important to choose the right one based on your specific data needs.

Microsoft Fabric Book

We are delighted to publish a hands-on guide to implementing end-to-end data projects in Microsoft Fabric. This hands-on book walks you through the key components and functionalities of Microsoft Fabric and invites you to actively follow the steps yourself.

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✅ Comparing Lakehouse, Warehouse, and Eventhouse

Properties

Feature 🏞️ Lakehouse 🏢 Warehouse ⚡ Eventhouse
Data Volume Unlimited Unlimited Unlimited
Data Type Unstructured, semi-structured, structured Structured Unstructured, semi-structured, structured
Primary Roles Data Engineer, Data Scientist Data Analyst Data Engineer, Data Scientist, Data Analyst
Data Organization Folders and files, schemas and tables Schemas and tables Databases and tables
Read Operations Spark, T-SQL T-SQL, Spark KQL, T-SQL, Spark
Write Operations Spark T-SQL KQL, Spark, connector ecosystem

🏞️ When to Choose a Lakehouse

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