15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Stewards and Metadata Managers
- Data Engineers and Architects
- Data Governance Professionals
- Business Intelligence Developers
- Data Scientists and Analysts
- IT Compliance Officers
- Enterprise Architects
- Information Management Officers
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
Transform your enterprise data assets into discoverable, governed resources with our 10-day Managing Metadata and Data Catalogs Training Course. Gain practical experience using enterprise-grade data catalog platforms to automate data lineage, enforce governance policies, and streamline glossaries. Build the essential skills needed to ensure your organization’s metadata supports transparent, compliant, and data-driven decision-making.
Curriculum & Topics
15 Topics | 10 Days
-
Subtopic 1.1:
What is metadata and why it matters
-
Subtopic 1.2:
Types of metadata: technical, business, operational
-
Subtopic 1.3:
Metadata lifecycle stages
-
Subtopic 1.4:
Benefits to governance, analytics, and compliance
-
Subtopic 1.5:
Common metadata management challenges
-
Subtopic 2.1:
Metadata modeling concepts
-
Subtopic 2.2:
Standards like ISO 11179 and Dublin Core
-
Subtopic 2.3:
Entity relationship modeling for metadata
-
Subtopic 2.4:
Aligning metadata models with business needs
-
Subtopic 2.5:
Mapping technical assets to business terms
-
Subtopic 3.1:
What is a data catalog and how it works
-
Subtopic 3.2:
Catalog components: search, lineage, tagging
-
Subtopic 3.3:
Comparing open-source and commercial catalogs
-
Subtopic 3.4:
Use cases across industries
-
Subtopic 3.5:
Cataloging structured and unstructured data
-
Subtopic 4.1:
Automated metadata discovery techniques
-
Subtopic 4.2:
Tagging, classification, and categorization
-
Subtopic 4.3:
Sensitivity detection and PII tagging
-
Subtopic 4.4:
Leveraging AI/ML for smart classification
-
Subtopic 4.5:
Organizing metadata using taxonomy
-
Subtopic 5.1:
Techniques for ingesting metadata from sources
-
Subtopic 5.2:
APIs, connectors, and crawlers
-
Subtopic 5.3:
Scheduling metadata synchronization
-
Subtopic 5.4:
Metadata harvesting from lakes and warehouses
-
Subtopic 5.5:
Event-driven ingestion workflows
-
Subtopic 6.1:
Difference between glossary and catalog
-
Subtopic 6.2:
Business term definition and approval
-
Subtopic 6.3:
Relationships and synonyms between terms
-
Subtopic 6.4:
Integrating glossary with catalog search
-
Subtopic 6.5:
Governance of glossary changes
-
Subtopic 7.1:
Capturing and visualizing data lineage
-
Subtopic 7.2:
Lineage at schema, table, and column levels
-
Subtopic 7.3:
Upstream and downstream dependency analysis
-
Subtopic 7.4:
Using lineage for impact and root cause analysis
-
Subtopic 7.5:
Change propagation and notification
-
Subtopic 8.1:
Roles and responsibilities in metadata governance
-
Subtopic 8.2:
Setting policies and stewardship workflows
-
Subtopic 8.3:
Governance models: centralized, federated, hybrid
-
Subtopic 8.4:
Stewardship tools and collaboration
-
Subtopic 8.5:
Ensuring metadata quality and accuracy
-
Subtopic 9.1:
Data catalog components and deployment models
-
Subtopic 9.2:
On-premise, cloud, and hybrid deployments
-
Subtopic 9.3:
Integration with security and identity systems
-
Subtopic 9.4:
Metadata storage and scalability
-
Subtopic 9.5:
Access control and role-based views
-
Subtopic 10.1:
Open-source options: Amundsen, DataHub, Apache Atlas
-
Subtopic 10.2:
Commercial platforms: Alation, Collibra, Informatica
-
Subtopic 10.3:
Feature comparison and selection criteria
-
Subtopic 10.4:
Licensing and pricing considerations
-
Subtopic 10.5:
Vendor support and extensibility
-
Subtopic 11.1:
Faceted search, autocomplete, and filtering
-
Subtopic 11.2:
Metadata enrichment for better UX
-
Subtopic 11.3:
Recommendation engines in data catalogs
-
Subtopic 11.4:
Bookmarking, annotations, and feedback
-
Subtopic 11.5:
Personalization and usage tracking
-
Subtopic 12.1:
Metadata pipeline automation using CI/CD
-
Subtopic 12.2:
Policy triggers and automated validation
-
Subtopic 12.3:
Scheduling lineage refresh and quality scans
-
Subtopic 12.4:
Workflow orchestration with Airflow or Prefect
-
Subtopic 12.5:
Alerting on stale or incomplete metadata
-
Subtopic 13.1:
Linking metadata to access control systems
-
Subtopic 13.2:
Tag-based access and masking rules
-
Subtopic 13.3:
Conditional access based on sensitivity
-
Subtopic 13.4:
Auditing metadata usage
-
Subtopic 13.5:
Policy enforcement and compliance reporting
-
Subtopic 14.1:
Using metadata in feature engineering
-
Subtopic 14.2:
Discovering reusable data assets
-
Subtopic 14.3:
Tracking model input/output lineage
-
Subtopic 14.4:
Ensuring explainability and transparency
-
Subtopic 14.5:
Metadata in MLOps pipelines
-
Subtopic 15.1:
Trends: active metadata, knowledge graphs
-
Subtopic 15.2:
Metadata interoperability and APIs
-
Subtopic 15.3:
AI/ML enhancements for catalogs
-
Subtopic 15.4:
Self-service and crowdsourced metadata
-
Subtopic 15.5:
Building an enterprise metadata strategy
