naming-tagging-standards

Enforcing naming conventions and tagging standards in Databricks involves applying snake_case rules, approved prefixes like dim_ and fact_, and config-aware tags. This ensures enterprise-wide consistency for data governance and discoverability across all project assets.

To automatically classify PII columns in Databricks, use Databricks Data Classification system governed tags. PII is always inferred directly from column names combined with any customer declarations, ensuring accurate compliance tagging without manual intervention.

The best way to apply cost_center and team tags to new tables is using config-aware tagging standards. The system scans context for customer tagging standards in formats like YAML, CSV, or JSON, and derives smart defaults to ensure accurate cost allocation.

Yes, you can use custom YAML, CSV, Markdown, JSON, or TXT files to define data governance tagging rules. The system scans your context directory for these formats and derives meaningful smart defaults automatically when no standards are supplied.

Standardize comments for tables and columns by implementing dual-purpose comment formats that provide both business and technical context. This applies to tables, columns, table-valued functions, and metric views, ensuring clarity and effective governance.

If you don't supply customer tagging standards for your data assets, the system derives meaningful smart defaults automatically. It scans the context directory for any existing formats and ensures tag consistency across all project assets using inferred rules.