Can I customize the classification rules?

Yes, the skill provides templates for classification rules (YAML) and tag taxonomies (SQL) that can be easily customized to fit your organization's specific data policies.

Is this skill useful for regulatory compliance?

Absolutely. It automates the identification and tagging of regulated data, making it significantly easier to adhere to frameworks such as GDPR, HIPAA, and CCPA.

How does this skill detect PII?

It uses a combination of schema-based analysis (checking column names and types) and content-based analysis using regex patterns to identify sensitive data like emails, phone numbers, or tax IDs.

Does this work with Unity Catalog?

Yes, the skill is specifically designed to work with Databricks Unity Catalog, utilizing SQL commands and Python APIs to apply tags at the catalog, table, and column levels.

What is the Databricks Data Classification skill?

It is a specialized capability for Claude Code designed to help users implement data governance, PII detection, and sensitivity tagging within the Databricks platform.

Databricks Data Classification

Name: Databricks Data Classification
Author: vivekgana

byvivekgana

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Database Management

Automates data classification and tagging within Databricks Unity Catalog to ensure regulatory compliance and data governance.

About

This skill provides a comprehensive framework for identifying, tagging, and managing sensitive data within Databricks. It leverages both schema-based and content-based detection patterns to categorize data by sensitivity levels—from Public to Restricted—and specific PII types. By integrating automated classification workflows and SQL-based tag application, it helps data engineers and governance teams maintain a robust compliance posture and implement fine-grained access controls based on data sensitivity and business context.

Key Features

Unity Catalog integration for seamless tag application via SQL and Python
Hierarchical sensitivity leveling including Public, Internal, Confidential, and Restricted
Automated PII detection using regex patterns for emails, SSNs, and credit cards
2 GitHub stars
Schema-based inference for rapid classification of large-scale datasets
Domain-specific classification for Financial, Customer, Employee, and Product data

Use Cases

Implementing a standardized data sensitivity framework across a Databricks workspace
Automating PII discovery and tagging for GDPR, CCPA, or HIPAA compliance
Auditing existing Unity Catalog tables to identify and tag unmasked sensitive information

About

Key Features

Unity Catalog integration for seamless tag application via SQL and Python
Hierarchical sensitivity leveling including Public, Internal, Confidential, and Restricted
Automated PII detection using regex patterns for emails, SSNs, and credit cards
2 GitHub stars
Schema-based inference for rapid classification of large-scale datasets
Domain-specific classification for Financial, Customer, Employee, and Product data

Use Cases

Implementing a standardized data sensitivity framework across a Databricks workspace
Automating PII discovery and tagging for GDPR, CCPA, or HIPAA compliance
Auditing existing Unity Catalog tables to identify and tag unmasked sensitive information