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 
        --> Discover data by using OneLake catalog and Real-Time hub

Discovering existing data assets efficiently is a critical capability for a Microsoft Fabric Analytics Engineer. For the DP-600 exam, this topic emphasizes how to find, understand, and evaluate data sources using Fabric’s built-in discovery experiences: OneLake catalog and Real-Time hub.

Purpose of Data Discovery in Microsoft Fabric

In large Fabric environments, data already exists across:

• Lakehouses
• Warehouses
• Semantic models
• Streaming and event-based sources

The goal of data discovery is to:

• Avoid duplicate ingestion
• Promote reuse of trusted data
• Understand data ownership, sensitivity, and freshness
• Accelerate analytics development

OneLake Catalog

What Is the OneLake Catalog?

The OneLake catalog is a centralized metadata and discovery experience that allows users to browse and search data assets stored in OneLake, Fabric’s unified data lake.

It provides visibility into:

• Lakehouses and Warehouses
• Tables, views, and files
• Shortcuts to external data
• Endorsement and sensitivity metadata

Key Capabilities of the OneLake Catalog

For the exam, you should understand that the OneLake catalog enables users to:

• Search and filter data assets across workspaces
• View schema details (columns, data types)
• Identify endorsed (Certified or Promoted) assets
• See sensitivity labels applied to data
• Discover data ownership and location
• Reuse existing data rather than re-ingesting it

This supports both governance and efficiency.

Endorsement and Trust Signals

Within the OneLake catalog, users can quickly identify:

• Certified items (approved and governed)
• Promoted items (recommended but not formally certified)

These trust signals are important in exam scenarios that ask how to guide users toward reliable data sources.

Shortcuts and External Data

The catalog also exposes OneLake shortcuts, which allow data from:

• Azure Data Lake Storage Gen2
• Amazon S3
• Other Fabric workspaces

to appear as native OneLake data without duplication. This is a key discovery mechanism tested in DP-600.

Real-Time Hub

What Is the Real-Time Hub?

The Real-Time hub is a discovery experience focused on streaming and event-driven data sources in Microsoft Fabric.

It centralizes access to:

• Eventstreams
• Azure Event Hubs
• Azure IoT Hub
• Azure Data Explorer (KQL databases)
• Other real-time data producers

Key Capabilities of the Real-Time Hub

For exam purposes, understand that the Real-Time hub allows users to:

• Discover available streaming data sources
• Preview live event data
• Subscribe to or reuse existing event streams
• Understand data velocity and schema
• Reduce duplication of real-time ingestion pipelines

This is especially important in architectures involving operational analytics or near real-time reporting.

OneLake Catalog vs. Real-Time Hub

Feature	OneLake Catalog	Real-Time Hub
Primary focus	Stored data	Streaming / event data
Data types	Tables, files, shortcuts	Events, streams, telemetry
Use case	Analytical and historical data	Real-time and operational analytics
Governance signals	Endorsement, sensitivity	Ownership, stream metadata

Understanding when to use each is a common exam theme.

Security and Governance Considerations

Data discovery respects Fabric security:

• Users only see items they have permission to access
• Sensitivity labels are visible in discovery views
• Workspace roles control discovery depth

This ensures compliance while still promoting self-service analytics.

Exam-Relevant Scenarios

On the DP-600 exam, you may be asked to:

• Identify how users can discover existing datasets before ingesting new data
• Choose between OneLake catalog and Real-Time hub based on data type
• Locate endorsed or certified data assets
• Reduce duplication by reusing existing tables or streams
• Enable self-service discovery while maintaining governance

Best Practices (Aligned to DP-600)

• Use OneLake catalog first before creating new data connections
• Encourage use of endorsed and certified assets
• Use Real-Time hub to discover existing event streams
• Leverage shortcuts to reuse data without copying
• Combine discovery with proper labeling and endorsement

Key Takeaway
For the DP-600 exam, discovering data in Microsoft Fabric is about visibility, trust, and reuse. The OneLake catalog helps users find and understand stored analytical data, while the Real-Time hub enables discovery of live streaming sources. Together, they reduce redundancy, improve governance, and accelerate analytics development.

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
• Pay close attention to when to use OneLake catalog vs. Real-Time hub
• Look for and understand the usage scenario of keywords in exam questions (for example, discover, reuse, streaming, endorsed, shortcut)
• Expect scenario-based questions that test architecture choices, rather than direct definitions

1. What is the primary purpose of the OneLake catalog in Microsoft Fabric?

A. To ingest streaming data
B. To schedule data refreshes
C. To discover and explore data stored in OneLake
D. To manage workspace permissions

Correct Answer: C

Explanation:
The OneLake catalog is a centralized discovery and metadata experience that helps users find, understand, and reuse data stored in OneLake across Fabric workspaces.

2. Which type of data is the Real-Time hub primarily designed to help users discover?

A. Historical data in Lakehouses
B. Structured warehouse tables
C. Streaming and event-driven data sources
D. Power BI semantic models

Correct Answer: C

Explanation:
The Real-Time hub focuses on streaming and event-based data such as Eventstreams, Azure Event Hubs, IoT Hub, and KQL databases.

3. A user wants to avoid re-ingesting data that already exists in another workspace. Which Fabric feature best supports this goal?

A. Data pipelines
B. OneLake shortcuts
C. Import mode
D. DirectQuery

Correct Answer: B

Explanation:
OneLake shortcuts allow data stored externally or in another workspace to appear as native OneLake data without physically copying it.

4. Which metadata element in the OneLake catalog helps users identify trusted and approved data assets?

A. Workspace name
B. File size
C. Endorsement status
D. Refresh schedule

Correct Answer: C

Explanation:
Endorsements (Promoted and Certified) act as trust signals, helping users quickly identify reliable and governed data assets.

5. Which statement about data visibility in the OneLake catalog is true?

A. All users can see all data across the tenant
B. Only workspace admins can see catalog entries
C. Users can only see items they have permission to access
D. Sensitivity labels hide data from discovery

Correct Answer: C

Explanation:
The OneLake catalog respects Fabric security boundaries—users only see data assets they are authorized to access.

6. A team is building a real-time dashboard and wants to see what streaming data already exists. Where should they look first?

A. OneLake catalog
B. Power BI Service
C. Dataflows Gen2
D. Real-Time hub

Correct Answer: D

Explanation:
The Real-Time hub centralizes discovery of streaming and event-based data sources, making it the best starting point for real-time analytics scenarios.

7. Which of the following items is most likely discovered through the Real-Time hub?

A. Parquet files in OneLake
B. Lakehouse Delta tables
C. Azure Event Hub streams
D. Warehouse SQL views

Correct Answer: C

Explanation:
Azure Event Hubs and other event-driven sources are exposed through the Real-Time hub, not the OneLake catalog.

8. What advantage does data discovery provide in large Fabric environments?

A. Faster Power BI rendering
B. Reduced licensing costs
C. Reduced data duplication and improved reuse
D. Automatic data modeling

Correct Answer: C

Explanation:
Discovering existing data assets helps teams reuse trusted data, reducing redundant ingestion and improving governance.

9. Which information is commonly visible when browsing an asset in the OneLake catalog?

A. User passwords
B. Column-level schema details
C. Tenant-wide permissions
D. Gateway configuration

Correct Answer: B

Explanation:
The OneLake catalog exposes metadata such as table schemas, column names, and data types to help users evaluate suitability before use.

10. Which scenario best demonstrates correct use of OneLake catalog and Real-Time hub together?

A. Using DirectQuery for all reports
B. Creating a new pipeline for every dataset
C. Discovering historical data in OneLake and live events in Real-Time hub
D. Applying sensitivity labels to dashboards

Correct Answer: C

Explanation:
OneLake catalog is optimized for discovering stored analytical data, while Real-Time hub is designed for discovering live streaming sources. Using both ensures comprehensive data discovery.