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# Data Catalog

The Data Catalog serves as the central governance hub for the data platform. It provides visibility into all data assets, enabling discovery, trust, and compliance.

# Purpose

The catalog addresses key organizational challenges:

  1. Discovery: "What data do we have and where is it?"
  2. Trust: "Can I rely on this data?"
  3. Ownership: "Who should I ask about this data?"
  4. Compliance: "Is this data being handled appropriately?"

A well-maintained catalog acts as a distributed DBA, helping users find existing data instead of creating duplicates.

# Core Capabilities

# Data Source Discovery

The catalog indexes all data assets across the platform:

Indexed assets:

  • Tables and views
  • Columns and data types
  • Data dictionaries and descriptions
  • Sample data and statistics

Search capabilities:

  • Full-text search across metadata
  • Filter by domain, owner, or tier
  • Semantic search for related concepts

Example: Searching for "RFM" (Recency, Frequency, Monetary) should surface all tables containing customer value metrics, even if column names vary.

# Schema Validation

Automated schema management:

  • Schema registration: Document expected structure
  • Validation: Compare actual vs. expected schemas
  • Evolution tracking: History of schema changes
  • Alerting: Notify on unexpected changes

# Lineage Tracking

End-to-end visibility of data flow:

Scope:

  • All storage tiers (Bronze → Silver → Gold)
  • Downstream artifacts (reports, models, dashboards)
  • Cross-system dependencies

Visualization:

  • Graph-based lineage explorer
  • Impact analysis for proposed changes
  • Root cause investigation for data issues

Benefits:

  • Understand data origins
  • Assess change impact
  • Debug data quality issues
  • Satisfy audit requirements

# Data Stewardship

Assign clear ownership and accountability:

Ownership model:

  • Data Owner: Business accountable for the data
  • Data Steward: Day-to-day quality and governance
  • Technical Owner: Pipeline and infrastructure

Steward responsibilities:

  • Define and document data definitions
  • Validate data quality
  • Approve access requests
  • Respond to data questions

Governance workflows:

  • Certification process for Gold data
  • Change approval for critical datasets
  • Regular review cycles

# Compliance

Support regulatory and policy requirements:

PII Identification:

  • Automated scanning for personal data
  • Classification and tagging
  • Policy enforcement based on classification

Compliance Reporting:

  • Data inventory reports
  • Access audit reports
  • Retention compliance status
  • PII location inventory

# Building Trust

The catalog is central to building trust in data:

# Trust Indicators

Communicate data reliability through:

Indicator Meaning
Certified Validated by data steward, ready for critical use
Standard Documented and tested, suitable for general use
Exploratory Available but not validated, use with caution
Deprecated Scheduled for removal, do not use for new work

# Quality Metrics

Surface data quality information:

  • Freshness (last update time)
  • Completeness (null rates)
  • Consistency (cross-reference checks)
  • Test results (pass/fail history)

# Usage Signals

Help users find reliable data:

  • Query frequency
  • Number of downstream dependencies
  • User ratings and feedback

# Integration with Dataplex

The platform leverages Google Dataplex for catalog capabilities:

# Current State

  • Automatic discovery of BigQuery assets
  • LLM-assisted metadata generation (column descriptions)
  • Search across all cataloged data

# Roadmap

  • Enhanced lineage visualization
  • Integration with ETL for automated lineage
  • Custom classification policies
  • Access request workflows

# User Experience

# Self-Service Discovery

Enable users to find data without asking the data team:

  1. Search for concepts or keywords
  2. Browse by domain or use case
  3. View documentation and examples
  4. Understand quality and ownership

# Feedback Mechanisms

Allow users to contribute:

  • Comments and questions on datasets
  • Quality issue reports
  • Suggestions for improvements
  • Rating data usefulness

# Support Integration

Direct channel to data stewards:

  • "Ask a question" functionality
  • Escalation for unresolved issues
  • Request for new data onboarding

# Best Practices

# For Data Producers

  1. Document before publishing: Add descriptions before data goes live
  2. Assign ownership: Every dataset needs an accountable owner
  3. Include examples: Show how data should be queried
  4. Maintain freshness: Keep documentation current

# For Data Consumers

  1. Search first: Check the catalog before requesting new data
  2. Verify certification: Prefer certified datasets for critical work
  3. Provide feedback: Report issues and suggestions
  4. Respect policies: Follow access and usage guidelines

# Metrics

Track catalog health and adoption:

Metric Description
Coverage % of tables with documentation
Ownership % of tables with assigned stewards
Freshness % of documentation updated recently
Adoption Search queries and views per month
Quality % of certified datasets

# Related Sections