This post is a part of the DP-600: Implementing Analytics Solutions Using Microsoft Fabric Exam Prep Hub; and this topic falls under these sections: 
Prepare data 
    --> Get data 
        --> Choose Between a Lakehouse, Warehouse, or Eventhouse

One of the most important architectural decisions a Microsoft Fabric Analytics Engineer must make is selecting the right analytical store for a given workload. For the DP-600 exam, this topic tests your ability to choose between a Lakehouse, Warehouse, or Eventhouse based on data type, query patterns, latency requirements, and user personas.

Overview of the Three Options

Microsoft Fabric provides three primary analytics storage and query experiences:

Option	Primary Purpose
Lakehouse	Flexible analytics on files and tables using Spark and SQL
Warehouse	Enterprise-grade SQL analytics and BI reporting
Eventhouse	Real-time and near-real-time analytics on streaming data

Understanding why and when to use each is critical for DP-600 success.

Lakehouse

What Is a Lakehouse?

A Lakehouse combines the flexibility of a data lake with the structure of a data warehouse. Data is stored in Delta Lake format in OneLake and can be accessed using both Spark and SQL.

When to Choose a Lakehouse

Choose a Lakehouse when you need:

• Flexible schema (schema-on-read or schema-on-write)
• Support for data engineering and data science
• Access to raw, curated, and enriched data
• Spark-based transformations and notebooks
• Mixed workloads (batch analytics, exploration, ML)

Key Characteristics

• Supports files and tables
• Uses Spark SQL and T-SQL endpoints
• Ideal for ELT and advanced transformations
• Easy integration with notebooks and pipelines

Exam signal words: flexible, raw data, Spark, data science, experimentation

Warehouse

What Is a Warehouse?

A Warehouse is a fully managed, SQL-first analytical store optimized for business intelligence and reporting. It enforces schema-on-write and provides a traditional relational experience.

When to Choose a Warehouse

Choose a Warehouse when you need:

• Strong SQL-based analytics
• High-performance reporting
• Well-defined schemas and governance
• Centralized enterprise BI
• Compatibility with Power BI Import or DirectQuery

Key Characteristics

• T-SQL only (no Spark)
• Optimized for structured data
• Best for star/snowflake schemas
• Familiar experience for SQL developers

Exam signal words: enterprise BI, reporting, structured, governed, SQL-first

Eventhouse

What Is an Eventhouse?

An Eventhouse is optimized for real-time and streaming analytics, built on KQL (Kusto Query Language). It is designed to handle high-velocity event data.

When to Choose an Eventhouse

Choose an Eventhouse when you need:

• Near-real-time or real-time analytics
• Streaming data ingestion
• Operational or telemetry analytics
• Event-based dashboards and alerts

Key Characteristics

• Uses KQL for querying
• Integrates with Eventstreams
• Handles massive ingestion rates
• Optimized for time-series data

Exam signal words: streaming, telemetry, IoT, real-time, events

Choosing the Right Option (Exam-Critical)

The DP-600 exam often presents scenarios where multiple options could work, but only one best fits the requirements.

Decision Matrix

Requirement	Best Choice
Raw + curated data	Lakehouse
Complex Spark transformations	Lakehouse
Enterprise BI reporting	Warehouse
Strong governance and schemas	Warehouse
Streaming or telemetry data	Eventhouse
Near-real-time dashboards	Eventhouse
SQL-only users	Warehouse
Data science workloads	Lakehouse

Common Exam Scenarios

You may be asked to:

• Choose a storage type for a new analytics solution
• Migrate from traditional systems to Fabric
• Support both engineers and analysts
• Enable real-time monitoring
• Balance governance with flexibility

Always identify:

1. Data type (batch vs streaming)
2. Latency requirements
3. User personas
4. Query language
5. Governance needs

Best Practices to Remember

• Use Lakehouse as a flexible foundation for analytics
• Use Warehouse for polished, governed BI solutions
• Use Eventhouse for real-time operational insights
• Avoid forcing one option to handle all workloads
• Let business requirements—not familiarity—drive the choice

Key Takeaway
For the DP-600 exam, choosing between a Lakehouse, Warehouse, or Eventhouse is about aligning data characteristics and access patterns with the right Fabric experience. Lakehouses provide flexibility, Warehouses deliver enterprise BI performance, and Eventhouses enable real-time analytics. The correct answer is almost always the one that best fits the scenario constraints.

Practice Questions:

Here are 10 questions to test and help solidify your learning and knowledge. As you review these and other questions in your preparation, make sure to …

• Identifying and understand why an option is correct (or incorrect) — not just which one
• Look for and understand the usage scenario of keywords in exam questions, with the below possible association:
  • Spark, raw, experimentation → Lakehouse
  • Enterprise BI, governed, SQL reporting → Warehouse
  • Streaming, telemetry, real-time → Eventhouse
• Expect scenario-based questions rather than direct definitions

1. Which Microsoft Fabric component is BEST suited for flexible analytics on both files and tables using Spark and SQL?

A. Warehouse
B. Eventhouse
C. Lakehouse
D. Semantic model

Correct Answer: C

Explanation:
A Lakehouse stores data in Delta format in OneLake and supports both Spark and SQL, making it ideal for flexible analytics across files and tables.

2. A team of data scientists needs to experiment with raw and curated data using notebooks. Which option should they choose?

A. Warehouse
B. Eventhouse
C. Semantic model
D. Lakehouse

Correct Answer: D

Explanation:
Lakehouses are designed for data engineering and data science workloads, offering Spark-based notebooks and flexible schema handling.

3. Which option is MOST appropriate for enterprise BI reporting with well-defined schemas and strong governance?

A. Lakehouse
B. Warehouse
C. Eventhouse
D. OneLake

Correct Answer: B

Explanation:
Warehouses are SQL-first, schema-on-write systems optimized for structured data, governance, and high-performance BI reporting.

4. A solution must support near-real-time analytics on streaming IoT telemetry data. Which Fabric component should be used?

A. Lakehouse
B. Warehouse
C. Eventhouse
D. Dataflow Gen2

Correct Answer: C

Explanation:
Eventhouses are optimized for high-velocity streaming data and real-time analytics using KQL.

5. Which query language is primarily used to analyze data in an Eventhouse?

A. T-SQL
B. Spark SQL
C. DAX
D. KQL

Correct Answer: D

Explanation:
Eventhouses are built on KQL (Kusto Query Language), which is optimized for querying event and time-series data.

6. A business analytics team requires fast dashboard performance and is familiar only with SQL. Which option best meets this requirement?

A. Lakehouse
B. Warehouse
C. Eventhouse
D. Spark notebook

Correct Answer: B

Explanation:
Warehouses provide a traditional SQL experience optimized for BI dashboards and reporting performance.

7. Which characteristic BEST distinguishes a Lakehouse from a Warehouse?

A. Lakehouses support Power BI
B. Warehouses store data in OneLake
C. Lakehouses support Spark-based processing
D. Warehouses cannot be governed

Correct Answer: C

Explanation:
Lakehouses uniquely support Spark-based processing, enabling advanced transformations and data science workloads.

8. A solution must store structured batch data and unstructured files in the same analytical store. Which option should be selected?

A. Warehouse
B. Eventhouse
C. Semantic model
D. Lakehouse

Correct Answer: D

Explanation:
Lakehouses support both structured tables and unstructured or semi-structured files within the same environment.

9. Which scenario MOST strongly indicates the need for an Eventhouse?

A. Monthly financial reporting
B. Slowly changing dimension modeling
C. Real-time operational monitoring
D. Ad hoc SQL analysis

Correct Answer: C

Explanation:
Eventhouses are designed for real-time analytics on streaming data, making them ideal for operational monitoring scenarios.

10. When choosing between a Lakehouse, Warehouse, or Eventhouse on the DP-600 exam, which factor is MOST important?

A. Personal familiarity with the tool
B. The default Fabric option
C. Data characteristics and latency requirements
D. Workspace size

Correct Answer: C

Explanation:
DP-600 emphasizes selecting the correct component based on data type (batch vs streaming), latency needs, user personas, and governance—not personal preference.