Microsoft Fabric was released on May 2023 with many surprises, and I can’t wait to use all of its features. While the data and analytics industry had a million options in the market, Microsft believed that all these options still made the data engineering teams and the analytics/AI teams have difficulty utilizing the output produced from one team to another. TBH, I felt the same working in the recent past jobs where I had produced data products for business by consuming data and analytics from other teams, even when we adopted a single vendor. I’ve been closely following Microsoft’s Fabric’s product architecture since day one and was very happy about its choices, especially for storing data!

In this article, let us understand why Fabric’s OneLake is important and how this is different to make data engineers, analysts, and AI/ML dev’s life much easier.

P.S. The views/contents in this post are based on my experience and implementation success only and are not related to any company.

• One Data lake for the whole enterprise

Fabric OneLake is a single & unified data lake for the whole organization bringing customers not to go elsewhere even if their job differs and requires various computes like SQL, Spark, Reporting, etc. Every Microsoft Fabric tenant will have just one instance of OneLake deployed, which is behind-the-scenes managed ADLS Gen2 for customers providing massive availability and scalability for any workload. In the past, Data engineers, analysts, and AI/ML developers would implement some data storage capability to store/query their data. But, with Fabric, it comes OOB, and we don’t need to create and manage one. When you log in to the Fabric tenant, your storage is ready to read and write by any computing engine.

• OneCopy of your data that can seamlessly work with many computational engines

With OneLake data storage, we can create a single trusted copy of the data, which is totally decoupled from computes. In my experience, every organization has departments that prefer to adopt a query engine – some prefer to analyze the data using SQL, some through Power BI, some through Python (spark), and some through GUI-based query tools like Power Query/Alteryx. With one copy of the data in OneLake, we can deploy any data computation engines to read and work with the data without moving it.

• Open Data Formats are the defaults.

Using OneLake we can store both structured (data in rows and columns, JSONs, XMLs, CSV etc.) and unstructured binary data files like images, audio, and videos. When you consume the data by any Fabric compute engines and create a lakehouse, warehouse, or real-time data pipes, the data will be stored as Delta Parquets in Onelake by default. While we don’t have the option to change this today, it makes sense – as parquets are the most performant columnar format that is proven and widely adopted by many.

• Data Security with OneLake

Because OneLake will be a single data lake for the whole company, it is important to implement a scalable and robust security model. One Security model will be released as part of OneLake, allowing users to create security definitions at the lake at various levels – Object, column, and row. This is an excellent feature (though yet to be released) as this allows us to ensure the same security model will be applied across any workloads we develop on top of this data without us redefining the access protocols repeatedly. If I had to implement this in the past – define the security (who can see what data – row level and column level) at multiple layers. TBH, this was really painful – once in database(s), once in reporting tools, and once in the data lake(s).

• Organizing OneLake through the Fabric Workspace

Since we have a data lake for the whole organization, the next immediate question that might come to mind is, how will I organize the data items for easier management by various business verticals? This is where Workspace comes in. If you have used Power BI then this concept is not new. Power BI has workspaces to differentiate projects/business teams to store their Power BI artifacts. The same concept applies to OneLake too. You can think of Workspaces as folders or repositories, or containers that you can create for each project or for each business team where they would like to create and store the related data artifacts. Workspaces allow to segregate and separate workloads in OneLake.

• Data Virtualization in OneLake

Another very important piece of the puzzle with OneLake is ‘Shortcuts’. Shortcuts allow us to access the data across domains or between workspaces virtually. This feature helps a team to access the data products produced by another team with few clicks, thus eliminating data duplication and all the efforts one has to put into making it accessible to others. With the help of shortcuts, Fabric was able to help adopt and follow the ‘OneCopy of data’ principle. Now there is a better chance for the YTD Sales numbers to match between Finance and Sales departments 🙂

• Connecting Outside OneLake

After reading the above on data virtualization, the next obvious question is, how can I move/use the data sitting on my existing data lake(s) implemented through Azure Data Lake or AWS S3 buckets? Data from existing ADSL Gen2 storage in Azure and Amazon S3 buckets can be virtualized through shortcuts. This means we can truly cross-query between clouds without ETL’ing or copying the data!

• Direct lake mode with Power BI

While OneLake provides the storage, we need a way to put a schema on top of it to query the data efficiently. Lakehouse in Fabric is our #1 preferred option because of its ease, and we will talk more about Fabric’s Lakehouse in the next post of this blog series. Power BI can directly read data from OneLake, called ‘Direct lake mode for Power BI.’ With this option, we don’t need to import data and create a Power BI dataset. Power BI can now directly connect with one lake without losing any performance from import mode. Another significant feature to help us to eliminate one full hop of moving the data to the reporting platform, which was needed in the old days for a few sec performance requirements on data visualizations.

• OneLake File Explorer

With Fabric OneLake, Microsoft also released a nice desktop tool called ‘OneLake File Explorer’, which can be installed on any Windows machine today. This sneaky tool gives us a more convenient way to browse the files/data stored in OneLake with a much more familiar Windows file explorer interface allowing business and non-technical users to navigate and access the data easily. This tool also resembled me more of the ‘One Drive File Explorer’ if you have used it.

• OneLake DataHub

With OneLake being the single data lake for the organization, we also get a centralized data catalog for all the data hosted within OneLake, allowing central data discovery and reuse for the Fabric users. We can quickly search or identify the certified and trusted data objects created by various teams in the org through this datahub and view its lineage.

• OneLake’s support for Data Mesh design patterns

Adopting a data mesh design pattern in an organization has honestly been my dream in my past jobs, but this was very difficult to implement as the data often needed to be copied/duplicated for performance reasons, or it was hard to group the related databases, projects by business domains due to the fact that no single platform ever supported all data workloads including reporting. OneLake natively supports Data Mesh architecture and allows building business domains like Sales, Supply Chain, Finance, etc., and assigning ownerships accordingly!

• OneLake and Data Sovereignty

I discussed how we could create many workspaces in OneLake, by project or business vertical. We can also create workspaces for regional data sovereignty needs. Every workspace we create has to be powered by Fabric’s computational capacity (in other words, we have to assign a capacity to the workspace for it to be functional). These capacities can be created specific to a region, thus helping to achieve data privacy requirements where the data cannot leave the required region. Users can create a workspace for such needs and store the data in that workspace within that region.

Hopefully, this blog was helpful for architects and engineers working or planning to adopt OneLake from Microsoft Fabric. The one area I did not cover here is data governance on OneLake. Microsoft Fabric and Purview work hand in hand, and governance for OneLake is achieved through Purview. If you would like to decouple this and write in an easy-to-understand manner, please leave me a comment, preferably with specifics on what you are trying to achieve with OneLake data governance. Thanks for reading!

About the Author

Solving Business Challenges with AI & Data | Passionate Speaker & Reader | #Analytics #AI #Data #DesignPatterns #Python #Spark #TeamBuilding

Periyasamy, A (2025). Microsoft Fabric’s OneLake approach is a crucial differentiator for modern data needs! Available at: Microsoft Fabric’s OneLake approach is a crucial differentiator for modern data needs! | LinkedIn [Accessed: 10th May 2025].