Tag: OneLake

OneLake Shortcuts + Spark: Practical Patterns for a Single Virtual Lakehouse

If you’ve adopted Microsoft Fabric, there’s a good chance you’re trying to reduce the number of ‘copies’ of data that exist just so different teams and engines can access it.

OneLake shortcuts are one of the core primitives Fabric provides to unify data across domains, clouds, and accounts by making OneLake a single virtual data lake namespace.

For Spark users specifically, the big win is that shortcuts appear as folders in OneLake—so Spark can read them like any other folder—and Delta-format shortcuts in the Lakehouse Tables area can be surfaced as tables.

What a OneLake shortcut is (and isn’t)

A shortcut is an object in OneLake that points to another storage location (internal or external to OneLake).

Shortcuts appear as folders and behave like symbolic links: deleting a shortcut doesn’t delete the target, but moving/renaming/deleting the target can break the shortcut.

From an engineering standpoint, that means you should treat shortcuts as a namespace mapping layer—not as a durability mechanism.

Where you can create shortcuts: Lakehouse Tables vs Files

In a Lakehouse, you create shortcuts either under the top-level Tables folder or anywhere under the Files folder.

Tables has constraints: OneLake doesn’t support shortcuts in subdirectories of the Tables folder, and shortcuts in Tables are typically meant for targets that conform to the Delta table format.

Files is flexible: there are no restrictions on where you can create shortcuts in the Files hierarchy, and table discovery does not happen there.

If a shortcut in the Tables area points to Delta-format data, the lakehouse can synchronize metadata and recognize the folder as a table.

One documented gotcha: the Delta format doesn’t support table names with space characters, and OneLake won’t recognize any shortcut containing a space in the name as a Delta table.

How Spark reads from shortcuts

In notebooks and Spark jobs, shortcuts appear as folders in OneLake, and Spark can read them like any other folder.

For table-shaped data, Fabric automatically recognizes shortcuts in the Tables section of the lakehouse that have Delta/Parquet data as tables—so you can reference them directly from Spark.

Microsoft Learn also notes you can use relative file paths to read data directly from shortcuts, and Delta shortcuts in Tables can be read via Spark SQL syntax.

Practical patterns (what I recommend in real projects)

Pattern 1: Use Tables shortcuts for shared Delta tables you want to show up consistently across Fabric engines (Spark + SQL + Direct Lake scenarios via semantic models reading from shortcuts).

Pattern 2: Use Files shortcuts when you need arbitrary formats or hierarchical layouts (CSV/JSON/images, nested partitions, etc.) and you’re fine treating it as file access.

Pattern 3: Prefer shortcuts over copying/staging when your primary goal is to eliminate edge copies and reduce latency from data duplication workflows.

Pattern 4: When you’re operationalizing Spark notebooks, make the access path explicit and stable by using the shortcut path (the place it appears) rather than hard-coding a target path that might change.

Operational gotchas and guardrails

Because moving/renaming/deleting a target path can break a shortcut, add lightweight monitoring for “broken shortcut” failures in your pipelines (and treat them like dependency failures).

For debugging, the lakehouse UI can show the ABFS path or URL for a shortcut in its Properties pane, which you can copy for inspection or troubleshooting.

Outside of Fabric, services can access OneLake through the OneLake API, which supports a subset of ADLS Gen2 and Blob storage APIs.

Summary

Shortcuts give Spark a clean way to treat OneLake like a unified namespace: read shortcuts as folders, surface Delta/Parquet data in Tables as tables, and keep your project’s logical paths stable even when physical storage locations vary.

References

This post was written with help from ChatGPT 5.2

Unveiling Microsoft OneLake: A Unified Intelligent Data Foundation

Microsoft recently introduced OneLake, a part of Microsoft Fabric, designed to accelerate data potential for the era of AI. One Lake provides a unified intelligent data foundation for all analytic workloads, integrating Power BI, Data Factory, and the next generation of Synapse. This solution offers customers a high-performing and easy-to-manage modern analytics solution.

OneLake: The OneDrive for All Your Data

OneLake provides a single data lake for your entire organization. For every Fabric tenant, there will always be exactly one OneLake, never two, never zero. There is no infrastructure to manage or set up. The concept of a tenant is a unique benefit of a SaaS service. It allows Microsoft to automatically provide a single management and governance boundary for the entire organization, which is ultimately under the control of a tenant admin.

Breaking down Data Silos with OneLake

OneLake aims to provide a data lake as a service without you needing to build it yourself. It enables different business groups to work independently without going through a central gatekeeper. Different workspaces allow different parts of the organization to work independently while still contributing to the same data lake. Each workspace can have its own administrator, access control, region, and capacity for billing.

OneLake: Spanning the Globe

OneLake covers this by spanning the globe as well. Different workspaces can reside in different regions. This means that any data stored in those workspaces will also reside in those countries. OneLake is built on top of Azure Data Lake Storage Gen2 under the covers. It will use multiple storage accounts in different regions, however, OneLake will virtualize them into one logical lake.

OneLake: Open Data Lake

OneLake is not just a Fabric data lake or a Microsoft data lake, it is an open data lake. In addition to being built on ADLS Gen2, OneLake supports the same ADLS Gen2 APIs and SDKs, making it compatible with existing ADLs applications, including Azure Databricks and Azure HDInsights.

OneLake: One Copy

OneLake with One Copy aims to get the most value possible out of a single copy of data without data movement or duplication. It allows data to be virtualized into a single data product without data movement, data duplication, or changing the ownership of the data.

OneLake: One Security

One Security is a feature in active development that aims to let you secure the data once and use it anywhere. One Security will bring a shared universal security model which you will define in OneLake. These security definitions will live alongside the data itself. This is an important detail. Security will live with the data rather than living downstream in the serving or presentation layers.

OneLake Data Hub

The OneLake Data Hub is the central location within Fabric to discover, manage, and reuse data. It serves all users from data engineer to business user. Data can easily be discovered by its domain, for example, Finance, HR, or Sales, so users find what actually matters to them.

In conclusion, OneLake is a game-changer in the world of data management and analytics. It provides a unified, intelligent data foundation that breaks down data silos, enabling organizations to harness the full potential of their data in the era of AI.

This blogpost was created with help from ChatGPT Pro.