Unifying Tags Across The Cloud: Ending Metadata Fragmentation in Snowflake, Databricks, and Beyond

Organizations today manage data across dozens of platforms - cloud warehouses like Snowflake and Databricks, transformation tools like dbt, and BI tools like Tableau. As this ecosystem expands, maintaining control and compliance becomes increasingly complex.

Source tags, the object tagging mechanisms provided by external source systems, should be the solution. Tags enable data classification, support governance policies, and power discovery across your data estate.

But while every modern platform supports tagging, they operate in isolation, creating fragmentation that silently undermines governance .

According to Gartner, the average financial impact of poor data quality on organizations is $12.9 million per year. Much of this stems from metadata fragmentation: when a "CUSTOMER_DATA" tag defined in Snowflake doesn't appear in dbt models, or Tableau dashboards can't inherit "CONFIDENTIAL" classifications from the warehouse, governance breaks down without warning.

This isn't just a technical inconvenience - it's a business risk that forces teams to spend more time managing tools than extracting value from data.

Tag drift: The hidden cost of inconsistent metadata

This financial impact isn't theoretical. It manifests daily through what we call "tag drift." Most modern data platforms support tagging or classification, but they don’t play well together. As a result, tags fragment quickly:

• Case differences (e.g., INDUSTRY vs industry) create duplicate fields.

• Schema-scoped definitions break policy logic.

• Stewards re-tag assets by hand when schema deselection wipes existing tags.

This silent failure: tag drift, chips away at trust in the catalog, and weakens every governance process that depends on it. You’ve likely seen it when:

• A masking policy fails silently because of a tag mismatch.

• A PII audit misses assets because a schema was deselected.

• Analysts routinely skip tag filters because they don't know which 'INDUSTRY' tag is real.

What data teams expect from tags

We’ve spoken to dozens of data leaders across sectors. They all want the same thing:

“If I define a tag once, I want it to show up once. Everywhere.”

This isn't a UI preference. It’s a demand for consistency, traceability, and shared understanding across the entire data ecosystem.

But in practice, tagging is scattered. Tags don't just drift; they get redefined, repurposed, and duplicated across tools. A tag like "CLASSIFICATION" might be defined in Snowflake, manually recreated in dbt with different values, and relabeled again in Tableau. Each system maintains its own rules, owners, and inconsistencies. There’s no shared coordination layer. Teams fill the gaps with spreadsheets, tribal knowledge, and rework.

Unified tags in Alation: One consistent layer

Unified Tags in Alation addresses this core architectural challenge: source tags that should be unified aren't, leaving teams unsure which metadata to trust.

With unified tags you can:

• Consolidate same-name tags into a single, trusted field across Snowflake and Databricks Unity Catalog.

• Preserve applied tag values (even if a schema is removed from extraction) so historical classifications remain visible for auditing.

• Maintain clear tag origins, with indicators showing where each tag came from.

• Use the same unified tags across BI and other catalog assets, so discovery and classification are consistent.

• Reduce manual overhead, eliminating the need for spreadsheets and re-tagging.

How Unified Tags work under the hood

Unified Tags are built on an architecture designed for flexibility and safety: 

• Name-based merging without semantic assumptions: Tags with identical names are automatically unified across platforms like Snowflake and Databricks Unity Catalog, eliminating duplication while preserving flexibility. If teams want to keep them distinct, they simply rename them at the source.

• Non-destructive value preservation: Applied tag values remain on assets, even if the source system no longer defines them.

• Source-aware visibility: Snowflake tags can be updated individually (where supported) to stay in sync. Unity Catalog tags are visible with clear attribution markers.

• Cross-platform consistency: Non-source assets—including BI objects, documentation, and even traditional RDBMS objects—can be tagged with the same unified tag set, enabling true catalog-wide consistency regardless of asset type.

• Zero-setup overheads: Tags require no additional configuration to start working. 

What changes isn’t just the metadata structure—it’s metadata trust. 

• Admins stop guessing which field is real.

• Stewards can classify once, confidently, without re-tagging assets or maintaining spreadsheets to track inconsistent labels.

• Governance leaders get end-to-end, auditable visibility into how classification and sensitivity tags are applied across their data estate, even as schemas evolve.

• For end users, search just works. 

• Policies can be enforced more reliably, reducing the risk of silent failures when tags drive processes like PII masking or access control.

Beyond unification: The road ahead

Unified Tags opens the door to advanced governance capabilities:

• Policy visibility across platforms, so rules can be applied with confidence.

• Audit-ready metadata that persists even as schemas evolve.

• LLM-enabled governance agents, that can reason on a single, consistent tagging vocabulary, with greater accuracy and context.

• Predictable CI/CD-style governance workflows that scale with your data operations.

• Shared semantics across all systems.

We are also exploring deeper integrations with:

• dbt: for transformation-stage tagging.

• Google BigQuery: Where labels drive security and access control.

• AWS: Where tags power critical billing and access control decisions across data services

As we connect these systems, a bigger question emerges:

Should the data catalog become a write-back authority across the stack? Or should it reflect, but not govern, tags defined elsewhere?

We don’t claim to have the final answer. But we’re designing with care, in partnership with teams who see metadata not as documentation, but as infrastructure.

Is your organization experiencing tag drift?

Check these signals:

• Do your top 10 governance tags appear multiple times in your catalog?

• Are “missing tags” or “policy gaps” frequent in support tickets?

• Do analysts routinely skip tag filters because they can't trust them?

If these patterns look familiar, you’re likely losing governance effectiveness and wasting time on rework.

Get started with Unified Source Tags

Alation Unified Tags is available in our 2025.1.3 release. The feature works immediately with existing Snowflake and Databricks Unity Catalog implementations, with no additional setup required for basic unification.

Contact us to see what unification looks like in your specific environment and calculate the time savings for your governance team. We'll show you which tags are fragmented today and what kind of lift you can expect.

Ready to move from metadata chaos to governance clarity? Let's discuss how Unified Tags can help.