Working with Tags in DataHub - Presentation Outline (transcript below)

I. Introduction

• Purpose: Learn how to effectively use tags to organize, classify, and manage data assets
• Tags - Definition: Powerful metadata elements for categorization
• Benefits:
  • Improve discoverability
  • Enforce governance policies
• Goals by end of demo:
  • Create tags
  • Apply tags across data ecosystem
  • Manage tags effectively
  • Leverage tag-based search and automation
  • Enhance data governance strategy

II. Understanding Tags

• Definition: Flexible metadata labels
• Scope: Can be attached to any data asset
  • Datasets
  • Dashboards
  • Pipelines
  • And more
• Flexibility: Adaptable to various use cases

III. Creating Tags (Live Demo)

• Navigate to Tags from main navigation
• Access tag management interface
• Click "Create Tag" button
• Configuration example: "PII Data" tag
  • Enter tag name (example: "Sensitive Data" or "PII Data")
  • Provide description
  • Define properties:
    • Color selection (example: red for sensitive data)
    • Assign owner (example: Customer Success team)
• Click "Create"
• Result: New tag ready for use

IV. Applying Tags to Assets

Dataset-Level Tagging

• Navigate to homepage
• Select dataset (example: dataset containing PII data)
• On Overview page, locate Tags section
• Click "Add Tag"
• Search and select tag (example: PII Data tag)
• Add to dataset

Field/Column-Level Tagging (Granular)

• Navigate to specific field within dataset
• Click "Add Tag" at field level
• Select tag (example: PII Data tag)
• Add to field
• Result: Visual indicators throughout interface showing tags at both levels

V. Tags for Search and Discovery

• Search interface: Filter assets by tag
• Example use case: Show only datasets with PII Data tag
• Benefit: Dramatically improves data discovery and navigation

VI. Tags for Governance and Access Control

• Access policies: Use tags to control access
• Example implementation: Restrict access to all assets with sensitive data tags
• Authorization: Limit access to authorized users only
• Benefit: Enforce governance at scale

VII. Tags with Business Glossary Integration

• Capability: Link business glossary terms to related tags
• Result: Creates comprehensive metadata fabric
• Benefit: Connects business vocabulary with classification systems

VIII. Programmatic Tag Management

REST API

• Comprehensive endpoints available
• Create, update, and apply tags programmatically
• Full API functionality

SDKs

• Convenient methods for tag management
• Integration with data pipelines
• Use in automation scripts

Tag Propagation

• Configure automatic tag inheritance
• Apply through lineage relationships
• Automated tag distribution

Ingestion Integrations

• Apply tags based on naming conventions
• Use source properties for automatic tagging
• Built-in classification logic

IX. Synchronization Capabilities

• Cross-system sync: Tags can synchronize with other metadata systems
• Scope: Integration with other data catalogs
• Benefit: Consistent tagging across entire data ecosystem
• Result: Classification standards maintained as data landscape evolves
• Extensibility: Implement organization-wide tagging standards

X. Recap & Conclusion

What Was Covered:

1. Created simple but powerful tag for PII data classification
2. Applied tags at both dataset and field levels
3. Used tags for discovery and filtering
4. Leveraged tags for governance and access control
5. Explored automation through APIs and integrations

Key Takeaways:

• Use cases supported:
  • Improving data discovery
  • Implementing governance controls
  • Building comprehensive data quality framework
• Foundation: Tagging capabilities provide essential infrastructure
• Outcome: Effectively organize and manage data assets
• Flexibility: Manual UI or programmatic automation

Value Proposition:

• Scalable metadata management
• Enhanced data governance
• Improved discoverability
• Automated compliance and classification

=== TIMESTAMPED TRANSCRIPT ===

[0.00s - 14.00s]: Hello everyone. Today I'll demonstrate how to effectively use tags and data to organize, classify, and manage your data assets.
[14.00s - 22.00s]: Tags are powerful metadata elements that help you categorize your data, improve discoverability, and enforce governance policies.
[22.00s - 33.00s]: By the end of this demo, you'll know how to create, apply, and manage tags across your data ecosystem, as well as leverage tag-based search and automation to enhance your data governance strategy.
[33.00s - 42.00s]: Tags and data hub are flexible metadata labels that can be attached to any data asset, including datasets, dashboards, pipelines, and more.
[42.00s - 46.00s]: Let's walk through creating and managing tags and data hub.
[46.00s - 51.00s]: From the main navigation, I'll click on tags access the tag management interface.
[52.00s - 56.00s]: From here, I'll click the create tag button to add any tag.
[56.00s - 64.00s]: I'll enter a tag name for sensitive data, like PII data, and give it a description.
[66.00s - 73.00s]: I can also define tag properties. I can give it the color red to indicate sensitive data. I can also add an owner.
[73.00s - 80.00s]: For now, I'll select the customer success team, and click create.
[81.00s - 84.00s]: Now we have our new tag.
[84.00s - 87.00s]: Now, let's apply these tags to a dataset.
[87.00s - 93.00s]: I can go to the homepage and navigate to any dataset that may contain PII data.
[93.00s - 99.00s]: On the overview page, I can click add tag in the tag section.
[99.00s - 106.00s]: I can search and find my new tag, and add this to the dataset.
[106.00s - 112.00s]: I can also apply tags at the column or field level, both for different granularity.
[112.00s - 117.00s]: Here, I can click add tag and find my PII data tag, and add this.
[117.00s - 124.00s]: The dataset and specific field are now tagged accordingly, with visual indicators throughout the interface.
[124.00s - 129.00s]: Tags dramatically improve how you can work with your data and data hub.
[129.00s - 132.00s]: In the search interface, I can filter assets by tag.
[132.00s - 137.00s]: For example, showing only datasets with the PII data tag.
[146.00s - 149.00s]: Tags can also be used in access policies.
[149.00s - 154.00s]: For example, restricting access to all assets with sensitive data tags to only authorized users.
[154.00s - 161.00s]: Tags can also be used with the business glossary, as terms can be linked to related tags, creating a comprehensive metadata fabric.
[163.00s - 171.00s]: While the UI is great for manual operations, data hub offers powerful options for programmatic tag management.
[171.00s - 178.00s]: Our REST API provides comprehensive endpoints to create, update, and apply tags programmatically.
[178.00s - 189.00s]: Our SDKs offer convenient methods for managing tags from data pipelines or automation scripts.
[190.00s - 198.00s]: Tag propagation allows you to configure automatic tag inheritance and linear relationships.
[198.00s - 206.00s]: And our ingestion integrations allow you to apply tags based on naming conventions or source properties.
[206.00s - 210.00s]: Tags can also be synchronized with other metadata systems or data catalogs.
[210.00s - 218.00s]: This sixth-ensibility allows you to implement consistent tagging across your entire data ecosystem, ensuring classification standards are maintained,
[218.00s - 223.00s]: even as your data landscape evolves.
[223.00s - 225.00s]: That concludes our demo of tags and data hub.
[225.00s - 245.00s]: We've covered creating a simple but powerful tag for PII data classification, applying tags at both dataset and field levels, using tags for discovery and filtering, leveraging tags for governance and access control, and automating tag management through APIs and integrations.
[245.00s - 259.00s]: Whether you're focused on improving data discovery, implementing governance controls, or building a comprehensive data quality framework, data hubs, tagging capabilities, provide the foundation you need to effectively organize and manage your data assets.
[259.00s - 260.00s]: Thanks for watching.