Difficulty: Medium | Concepts: Azure Data Lake Storage Gen2, OneLake, Shortcuts, Hierarchical Namespace, ABFS

"Our company already has 50TB of data sitting in Azure Data Lake Storage Gen2 — do we need to copy all of it into Fabric?" is a very common Azure + Fabric interview question, and the correct answer — "no, use a Shortcut" — is exactly the kind of cost- and time-saving knowledge interviewers are screening for.

What Is a Fabric Shortcut and Why Does It Matter for ADLS Gen2?

A Shortcut is a reference inside a Fabric Lakehouse that points to data stored elsewhere — including in another Fabric workspace, or an external cloud store like Azure Data Lake Storage Gen2, Amazon S3, or Google Cloud Storage — without physically copying the data into OneLake. Because ADLS Gen2 uses the same hierarchical namespace and ABFS (Azure Blob Filesystem) driver concepts that OneLake is built on, Fabric can mount it almost natively: any Notebook, Pipeline, or Dataflow that reads from the Lakehouse sees the shortcut as if the files were local, while the actual bytes stay in the original ADLS Gen2 account. This avoids duplicate storage costs, avoids data staleness (since there's only one copy, updated by whoever owns the source), and dramatically speeds up onboarding of existing data estates into Fabric.

Scenario

Your company has an existing ADLS Gen2 account with historical sales data organized as /raw/sales/year=2023/, year=2024/, etc. Leadership wants Fabric Notebooks and Power BI reports to query this data without duplicating 50TB or waiting on a migration project.

Steps to Create the Shortcut

In the target Lakehouse, go to the Files section and select New shortcut.
Choose Azure Data Lake Storage Gen2 as the source type.
Provide the ADLS Gen2 endpoint URL (e.g., https://mystorageacct.dfs.core.windows.net) and the container/path (/raw/sales).
Authenticate using an Organizational account, Service Principal, or SAS token — Service Principal is recommended for production/automated scenarios.
Name the shortcut (e.g., sales_raw) — it now appears in the Lakehouse's Files pane exactly like a native folder.

Reading the Shortcut in a Notebook

# The shortcut behaves exactly like a native Lakehouse path
df = spark.read.format("parquet").load("Files/sales_raw/year=2024/")
df.show(5)

Shortcut vs. Copy Activity: When to Use Which

Approach	Best for
Shortcut	Existing, actively-maintained ADLS Gen2 data you want to query in place, no duplication
Copy Activity (Pipeline)	Data you need to transform, land in Delta format, or that must be independently versioned from the source

Visual Representation

Azure Data Lake Storage Gen2 (existing 50TB, owned/maintained externally) | Fabric Lakehouse -> Files -> New Shortcut -> ADLS Gen2 (auth via Service Principal) | Shortcut "sales_raw" appears in Lakehouse Files pane | Notebooks / Pipelines / Power BI read it exactly like native Lakehouse data | No data physically duplicated into OneLake

Common Mistake to Avoid

Candidates sometimes assume a shortcut is a one-time snapshot — it isn't. Because it's a live pointer, any changes made directly in the source ADLS Gen2 account are immediately visible through the shortcut. This is a feature, not a bug, but it means you must apply the same governance and access control on the source account as you would on the Lakehouse itself, since the shortcut inherits whatever the underlying data allows.

Conclusion

The interview signal here is recognizing that Shortcuts eliminate unnecessary data movement for existing, actively-maintained cloud storage — and knowing when a Copy Activity is still the right call instead (when transformation or independent versioning is required).