15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the principles and value of metadata management
- Learn to implement enterprise metadata standards and models
- Gain skills in selecting and deploying data catalog platforms
- Establish metadata-driven data governance frameworks
- Automate metadata ingestion and lineage capture
- Create and manage business glossaries and taxonomies
- Enhance data discoverability and classification
- Enable collaborative metadata stewardship workflows
- Integrate metadata into analytics and ML environments
- Enforce data access and usage policies via metadata
- Monitor metadata quality, versioning, and impact analysis
About the Course
This advanced training equips professionals with the knowledge and practical skills required to manage enterprise metadata, design effective data catalogs, improve data discoverability, and strengthen data governance. Participants will explore how organizations can create structured inventories of data assets, document data meaning and lineage, establish ownership, and improve trust in enterprise data.
The course covers metadata management frameworks, data catalog architecture, business and technical metadata, data lineage, classification, stewardship, data quality, governance, access management, and catalog implementation. Participants will learn how to establish sustainable metadata practices that support analytics, compliance, artificial intelligence, and organization-wide data-driven decision-making.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
What is metadata and why it matters
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Workshop 1.2:
Types of metadata: technical, business, operational
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Workshop 1.3:
Metadata lifecycle stages
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Workshop 1.4:
Benefits to governance, analytics, and compliance
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Workshop 1.5:
Common metadata management challenges
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Workshop 2.1:
Metadata modeling concepts
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Workshop 2.2:
Standards like ISO 11179 and Dublin Core
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Workshop 2.3:
Entity relationship modeling for metadata
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Workshop 2.4:
Aligning metadata models with business needs
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Workshop 2.5:
Mapping technical assets to business terms
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Workshop 3.1:
What is a data catalog and how it works
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Workshop 3.2:
Catalog components: search, lineage, tagging
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Workshop 3.3:
Comparing open-source and commercial catalogs
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Workshop 3.4:
Use cases across industries
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Workshop 3.5:
Cataloging structured and unstructured data
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Workshop 4.1:
Automated metadata discovery techniques
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Workshop 4.2:
Tagging, classification, and categorization
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Workshop 4.3:
Sensitivity detection and PII tagging
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Workshop 4.4:
Leveraging AI/ML for smart classification
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Workshop 4.5:
Organizing metadata using taxonomy
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Workshop 5.1:
Techniques for ingesting metadata from sources
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Workshop 5.2:
APIs, connectors, and crawlers
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Workshop 5.3:
Scheduling metadata synchronization
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Workshop 5.4:
Metadata harvesting from lakes and warehouses
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Workshop 5.5:
Event-driven ingestion workflows
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Workshop 6.1:
Difference between glossary and catalog
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Workshop 6.2:
Business term definition and approval
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Workshop 6.3:
Relationships and synonyms between terms
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Workshop 6.4:
Integrating glossary with catalog search
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Workshop 6.5:
Governance of glossary changes
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Workshop 7.1:
Capturing and visualizing data lineage
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Workshop 7.2:
Lineage at schema, table, and column levels
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Workshop 7.3:
Upstream and downstream dependency analysis
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Workshop 7.4:
Using lineage for impact and root cause analysis
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Workshop 7.5:
Change propagation and notification
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Workshop 8.1:
Roles and responsibilities in metadata governance
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Workshop 8.2:
Setting policies and stewardship workflows
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Workshop 8.3:
Governance models: centralized, federated, hybrid
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Workshop 8.4:
Stewardship tools and collaboration
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Workshop 8.5:
Ensuring metadata quality and accuracy
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Workshop 9.1:
Data catalog components and deployment models
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Workshop 9.2:
On-premise, cloud, and hybrid deployments
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Workshop 9.3:
Integration with security and identity systems
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Workshop 9.4:
Metadata storage and scalability
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Workshop 9.5:
Access control and role-based views
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Workshop 10.1:
Open-source options: Amundsen, DataHub, Apache Atlas
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Workshop 10.2:
Commercial platforms: Alation, Collibra, Informatica
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Workshop 10.3:
Feature comparison and selection criteria
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Workshop 10.4:
Licensing and pricing considerations
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Workshop 10.5:
Vendor support and extensibility
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Workshop 11.1:
Faceted search, autocomplete, and filtering
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Workshop 11.2:
Metadata enrichment for better UX
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Workshop 11.3:
Recommendation engines in data catalogs
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Workshop 11.4:
Bookmarking, annotations, and feedback
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Workshop 11.5:
Personalization and usage tracking
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Workshop 12.1:
Metadata pipeline automation using CI/CD
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Workshop 12.2:
Policy triggers and automated validation
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Workshop 12.3:
Scheduling lineage refresh and quality scans
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Workshop 12.4:
Workflow orchestration with Airflow or Prefect
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Workshop 12.5:
Alerting on stale or incomplete metadata
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Workshop 13.1:
Linking metadata to access control systems
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Workshop 13.2:
Tag-based access and masking rules
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Workshop 13.3:
Conditional access based on sensitivity
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Workshop 13.4:
Auditing metadata usage
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Workshop 13.5:
Policy enforcement and compliance reporting
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Workshop 14.1:
Using metadata in feature engineering
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Workshop 14.2:
Discovering reusable data assets
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Workshop 14.3:
Tracking model input/output lineage
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Workshop 14.4:
Ensuring explainability and transparency
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Workshop 14.5:
Metadata in MLOps pipelines
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Workshop 15.1:
Trends: active metadata, knowledge graphs
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Workshop 15.2:
Metadata interoperability and APIs
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Workshop 15.3:
AI/ML enhancements for catalogs
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Workshop 15.4:
Self-service and crowdsourced metadata
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Workshop 15.5:
Building an enterprise metadata strategy
