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Welcome back to Episode 9!
Now, when you put all of a company's files into one single lake, there is one obvious question every IT team asks: How do we make sure people only see what they are
supposed to see?
Putting all your data in one place definitely does not mean making it open to everyone. Fabric manages security and governance across four simple layers:
• Workspace Roles: This is your basic team access. You decide who can build data pipelines, who can run Spark code, and who is just an everyday viewer
opening dashboards.
• Row and Column Level Security: You can easily restrict specific rows or columns. For example, if a sales lead in Europe runs a SQL query on a global sales
table, they will only see European numbers—even though data for India and the US sits in the exact same table in OneLake.
• Sensitivity Labels: Fabric connects directly with Microsoft Purview Information Protection. If you mark a table as "Confidential" or "Internal Only," that security
label sticks to the data. Even if an analyst exports the numbers into an Excel sheet or builds a Power BI chart, the file remains locked and protected.
• Automatic Lineage: Thanks to Microsoft Purview, Fabric draws a visual family tree for your data. If someone spots a strange number on a dashboard, you
don't need to ask five people what happened. You can just click the visual map and trace that number backward—through the SQL query, through the Spark
cleaning script, all the way to the raw file that arrived in the morning.
It gives your IT and security teams complete peace of mind because they can see exactly who is touching what.
In Episode 10, we’ll bring all the pieces together and wrap up the entire series.
About the Host
Rajnish Pandey is a Senior Data Engineer with over decade of experience in the data industry. Through QueryZens, he shares practical insights, real-world experiences, and conversations around Data Engineering and modern data platforms.
Hosted by Rajnish Pandey | QueryZens
Making Data Engineering easier to understand, one conversation at a time.
