topic Re: Data science roadmap in Data Science

Data science roadmap

powerbidev123 — Sun, 02 Aug 2026 13:37:35 GMT

OneLake is often highlighted as one of the key capabilities of Microsoft Fabric, especially for centralizing and managing enterprise data. I'm interested in hearing from those who are using it in production environments.

For those with hands-on experience:

What challenges have you encountered while implementing or managing OneLake?
Have you experienced any performance or scalability issues as your data volumes increased?
How do you structure and organize data across workspaces, domains, or business units in a large enterprise?
What governance, security, and access control practices have worked well for your organization?
Are there any lessons learned or best practices you wish you had known before adopting OneLake?

I'd love to hear about real-world experiences, whether positive or challenging. Your insights could be incredibly valuable for others planning or expanding their Microsoft Fabric adoption.

Re: Data science roadmap

ShivekMaharaj — Sun, 02 Aug 2026 15:51:49 GMT

Hi powerbidev123,

One lesson I would emphasise is that OneLake being logically unified does not mean the organisation should treat everything as one unrestricted data area.

I would structure the environment around clear business domains and use workspaces as the main operational and security boundaries. For example, Finance, Marketing and Customer Analytics could each have dedicated workspaces, with Fabric domains grouping related workspaces and allowing governance responsibilities to be distributed appropriately. Microsoft also recommends using dedicated workspaces for data domains and managing access, cost visibility and operational policies at the workspace level.

Some practical considerations I would plan for are:

1. Avoid unnecessary duplication: OneLake shortcuts are useful when data already exists in another OneLake location, ADLS or supported external storage. They allow teams to access the same data through a unified namespace without creating another physical copy. However, shortcut ownership, credentials and downstream dependencies should still be documented carefully.

2. Separate raw, curated and consumption layers: I would use a medallion-style structure:

Bronze for raw ingested data
Silver for validated and standardised data
Gold for business-ready data products

Microsoft identifies the medallion lakehouse pattern as the recommended design approach for organising data in OneLake.

3. Design security before onboarding users: Workspace roles alone can become too broad if they are used as the only access-control mechanism. I would apply least privilege, use security groups rather than assigning individuals repeatedly, and decide where access should be controlled at workspace, item, folder, table, row or column level. OneLake security roles now provide more granular control, but the complete permission path still needs to be tested because workspace and workload permissions can interact.

4. Treat governance as an operating process: Naming standards, ownership, endorsement, sensitivity labels, lineage and data-product responsibilities should be defined before the estate becomes large. The OneLake Catalog can provide a central view of Fabric items, permissions and governance state, while Microsoft Purview can support broader discovery and governance requirements.

5. Monitor capacity from the beginning: OneLake storage and data operations still affect cost and capacity consumption. I would monitor workspace-level consumption, throttling and workload peaks through the Fabric Capacity Metrics app rather than waiting until users report slow performance.

A common mistake is to focus only on getting data into OneLake and postpone ownership, security and lifecycle decisions. It is much harder to introduce consistent governance after many teams have already created their own workspaces, lakehouses, shortcuts and permission patterns.

My suggested starting point would be:

Define the business domains and owners.
Establish workspace and naming standards.
Design the Bronze, Silver and Gold data-product flow.
Define access using groups and least privilege.
Create a shortcut and duplication policy.
Configure lineage, endorsement and governance monitoring.
Establish capacity and cost-monitoring responsibilities.

I would be interested to hear whether others have found workspace sprawl, permission complexity or capacity management to be the biggest challenge in their own implementations.

Re: Data science roadmap

v-abhinavmu — Thu, 06 Aug 2026 05:53:05 GMT

Hi powerbidev123,

Thanks for reaching out to the Microsoft fabric community. and thanks to ShivekMaharaj for sharing valuable insights.

I wanted to check if you had the opportunity to review the information provided. Please feel free to contact us if you have any further questions.

Thank you.

Re: Data science roadmap

RachidBelouche — Tue, 18 Aug 2026 17:42:28 GMT

I can really invite you to check this link:

OneLake architectural guidance: A practical blueprint for the AI Era | Microsoft Fabric Community

Read about the 5 patterns and read OneLake Architectural Guidance Whitepaper, it can help you to understand what to expect.

OneLake is a data lake with support for lakehouse and warehouse, my advice is that you have to expect managing data in the OneLake not only storing them as centralized data lake solution to strore data, but managing data depend on the size of the company, it's best practice to have administrator or someone tasked to manage the storage of the data for each team. Other key thing to consider is to design security of the OneLake since you don't have to allow data to be accessible by anyone.

Always keep an eye on this page to know which features will be released:

Microsoft Fabric Roadmap