15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers and Data Architects
- Data Governance Officers
- Compliance and Risk Management Professionals
- Cloud Data Engineers
- Metadata Managers and Data Stewards
- DevOps and DataOps Teams
- Machine Learning and AI Practitioners
- Business Intelligence Developers
Session Objectives
- Understand the foundational principles of data governance
- Implement automated data lineage tracking across environments
- Manage metadata using data catalogs and governance tools
- Integrate data governance into CI/CD and DevOps workflows
- Ensure data privacy and regulatory compliance
- Monitor and improve data quality and policy enforcement
- Build collaborative governance frameworks
- Design architectures for scalable governance
- Apply governance to ML and AI data pipelines
- Visualize governance KPIs and audit metrics
- Enable real-time and hybrid data governance
About the Course
Operationalize data trust and regulatory compliance with our Data Governance and Lineage Training Course. Tailored for data engineers, platform architects, and governance leads, this practical program equips technical teams to architect auditable, privacy-compliant data ecosystems across hybrid and multi-cloud environments. Guided by governance and architecture experts, you will master automated metadata extraction, end-to-end data lineage tracking, policy enforcement in DevOps pipelines, and enterprise tooling—including Apache Atlas, Collibra, and DataHub—to eliminate compliance risks and ensure data quality at scale.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Core concepts and business importance of data governance
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Subtopic 1.2:
Governance frameworks and maturity models
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Subtopic 1.3:
Key governance roles and accountability structures
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Subtopic 1.4:
Policies, stewardship, and compliance alignment
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Subtopic 1.5:
Aligning data strategy with business goals
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Subtopic 2.1:
Types of lineages: business, technical, operational
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Subtopic 2.2:
Capturing data flow from source to destination
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Subtopic 2.3:
Visualizing and mapping transformations
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Subtopic 2.4:
Tools for automated lineage capture
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Subtopic 2.5:
Practical use cases: audit, debugging, reporting
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Subtopic 3.1:
Role of metadata in governance
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Subtopic 3.2:
Enterprise metadata harvesting techniques
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Subtopic 3.3:
Evaluating and deploying data catalogs
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Subtopic 3.4:
Metadata synchronization across tools
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Subtopic 3.5:
Organizing metadata taxonomies and glossaries
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Subtopic 4.1:
Dimensions of data quality
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Subtopic 4.2:
Techniques for profiling and validation
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Subtopic 4.3:
Automating quality monitoring and alerts
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Subtopic 4.4:
Root cause analysis of quality issues
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Subtopic 4.5:
Reporting data health and metrics
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Subtopic 5.1:
Regulatory frameworks: GDPR, CCPA, HIPAA, etc.
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Subtopic 5.2:
Access control and data masking
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Subtopic 5.3:
Creating and managing policy rules
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Subtopic 5.4:
Auditing and forensic tracing
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Subtopic 5.5:
Data retention and deletion policies
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Subtopic 6.1:
Reference architecture for governance tooling
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Subtopic 6.2:
Centralized vs federated governance
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Subtopic 6.3:
APIs, connectors, and orchestration tools
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Subtopic 6.4:
Governance across cloud-native and legacy systems
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Subtopic 6.5:
Event-driven governance design
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Subtopic 7.1:
Inserting governance checks in data flows
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Subtopic 7.2:
Documenting ETL/ELT transformations
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Subtopic 7.3:
Integration with Apache Airflow and dbt
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Subtopic 7.4:
Tracking schema evolution and data changes
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Subtopic 7.5:
Versioning pipelines for compliance
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Subtopic 8.1:
Open-source tools: Apache Atlas, DataHub, Amundsen
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Subtopic 8.2:
Commercial platforms: Collibra, Alation, Informatica
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Subtopic 8.3:
Integration with cloud providers and data lakes
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Subtopic 8.4:
Plugin architectures and extensibility
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Subtopic 8.5:
Tool comparison matrix and best fit
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Subtopic 9.1:
Defining governance rules in YAML/JSON
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Subtopic 9.2:
Integrating with Git, Jenkins, and GitHub Actions
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Subtopic 9.3:
Approval gates for metadata and access
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Subtopic 9.4:
Infrastructure as code for policy automation
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Subtopic 9.5:
CI/CD pipelines for governance assets
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Subtopic 10.1:
Roles and responsibilities of data stewards
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Subtopic 10.2:
Workflows for stewardship tasks
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Subtopic 10.3:
Engaging business teams in governance
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Subtopic 10.4:
Resolving data ownership conflicts
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Subtopic 10.5:
Promoting a governance culture
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Subtopic 11.1:
Governance in model training and deployment
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Subtopic 11.2:
Capturing lineage in ML features and datasets
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Subtopic 11.3:
Managing bias, drift, and explainability
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Subtopic 11.4:
Versioning models and audit trails
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Subtopic 11.5:
Securing synthetic and sensitive datasets
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Subtopic 12.1:
Discovery and classification across cloud environments
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Subtopic 12.2:
Unified policy enforcement
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Subtopic 12.3:
Metadata sharing between platforms
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Subtopic 12.4:
Avoiding silos in hybrid architectures
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Subtopic 12.5:
Data fabric for consistent governance
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Subtopic 13.1:
Defining governance performance metrics
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Subtopic 13.2:
Building dashboards for lineage and quality
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Subtopic 13.3:
SLA tracking for data pipelines
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Subtopic 13.4:
Reporting for audits and regulators
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Subtopic 13.5:
Alerting and notification strategies
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Subtopic 14.1:
Designing data producer-consumer agreements
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Subtopic 14.2:
Schema enforcement and compatibility testing
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Subtopic 14.3:
Breaking change detection and rollback
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Subtopic 14.4:
Automating SLAs in data flows
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Subtopic 14.5:
Enabling reliable data sharing
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Subtopic 15.1:
Establishing short- and long-term governance goals
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Subtopic 15.2:
Prioritizing based on risk and value
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Subtopic 15.3:
Change management and stakeholder buy-in
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Subtopic 15.4:
Scaling governance programs
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Subtopic 15.5:
Staying current with industry trends and tools
