Case Study Multinational Steel Company Automates Permission Management in Databricks Using Tags

From manual permissions to automatic control by tags

When data grows in Databricks, managing permissions on an object-by-object basis is no longer viable.

Industry
Steel Multinational

-80%
Manual management

↓ -80%
Manual permission management

↑ Automatic
Tag deployment

↓ -45%
Inconsistencies between DEV and PRO

↑ 50%
Speed to analyze lineage by use case.

Learn how to activate Data Governance

BEFORE

Tags were not automatically propagated between development and production.

Permits had to be applied and reviewed manually.

It was difficult to know which tables were involved in each use case.

NOW

Tags are automatically deployed between environments.

Permissions are applied according to tags and metadata.

Lineage allows filtering tables by use case.

THE PROBLEM

Manually managing permissions does not scale.

Every change required technical intervention.
Permits depended on manual reviews.
Tags did not automatically travel between environments.

THE SOLUTION

Dynamic permissions based on tags.

Automatic tag deployment in Databricks.
Metadata table to relate group, permission and tag.
Periodic script that applies permissions according to the defined configuration.
Filter by tags in the lineage report.

THE IMPACT

More control.
Less operational burden.

The organization can manage permissions in a scalable, traceable and automated way, reducing manual work and improving control over who accesses what data.

These types of initiatives are part of a broader enterprise data platforms in Databricks strategy, key to scaling data usage with automation, governance, and security.