Data Lineage and Documentation with dbt Training Course

 Data Lineage and Documentation with dbt Training Course 

Introduction 

Data Lineage and Documentation with dbt Training Course is designed to equip data professionals with practical skills in modern data transformation, data governance, metadata management, and analytics engineering. As organizations increasingly adopt cloud data warehouses, automated data pipelines, and data-driven decision-making frameworks, understanding how data moves across complex systems has become essential. This course explores dbt (data build tool) documentation, data lineage visualization, metadata management, model dependencies, source tracking, and automated documentation generation. Participants will gain practical experience building transparent, reliable, scalable, and maintainable data transformation workflows while applying industry-standard data quality and governance practices. 

The training emphasizes advanced dbt documentation techniques, dependency management, data cataloging, SQL transformation workflows, and end-to-end data traceability. Through practical exercises and global industry case studies, participants will learn to document data models, identify upstream and downstream dependencies, troubleshoot transformation pipelines, and improve collaboration between data engineers, analytics engineers, and business intelligence teams. The course also covers automated documentation deployment, version control, testing integration, and enterprise data governance strategies that support trustworthy analytics, regulatory compliance, and modern data platform optimization. 

Course Objectives 

• Understand data lineage concepts and modern analytics engineering practices.
• Configure dbt projects for automated documentation generation.
• Build comprehensive data model documentation using dbt.
• Visualize upstream and downstream data dependencies.
• Implement metadata management and data cataloging strategies.
• Track source-to-target data transformations.
• Apply SQL documentation and annotation best practices.
• Integrate dbt testing with data documentation workflows.
• Implement automated lineage monitoring and validation.
• Manage documentation through Git version control.
• Improve data governance, transparency, and traceability.
• Troubleshoot complex data transformation dependencies.
• Develop scalable enterprise data documentation frameworks.

Organizational Benefits
 

• Improved enterprise data transparency and accountability.
• Stronger data governance and regulatory compliance.
• Reduced data discovery and troubleshooting time.
• Increased trust in business intelligence reporting.
• Better collaboration between technical and business teams.
• Improved data quality and transformation reliability.
• Faster onboarding of analytics engineering professionals.
• Reduced operational risks in data pipelines.
• Standardized metadata and documentation practices.
• More efficient data platform management.

Target Audiences
 

• Data engineers and analytics engineers.
• Database administrators and SQL developers.
• Business intelligence and reporting analysts.
• Data architects and platform engineers.
• Data governance and quality professionals.
• Cloud data warehouse specialists.
• Technical documentation and metadata managers.
• IT managers and data transformation consultants.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of Data Lineage and dbt Documentation
 

• Introduction to data lineage and analytics engineering.
• Understanding dbt architecture and transformation workflows.
• Data sources, models, seeds, and snapshots.
• Metadata management and documentation principles.
• Data lineage applications in enterprise environments.
• Case study: Enterprise data traceability in Walmart's analytics ecosystem.

Module 2: dbt Project Documentation and Configuration
 

• Configuring dbt project documentation settings.
• Writing model descriptions and column-level metadata.
• Creating and maintaining YAML documentation files.
• Documenting sources, tests, and transformation logic.
• Managing documentation standards across projects.
• Case study: Standardized documentation practices in modern cloud data platforms.

Module 3: Data Lineage Visualization and Dependency Management
 

• Understanding dbt Directed Acyclic Graphs (DAGs).
• Visualizing upstream and downstream dependencies.
• Exploring model relationships using dbt documentation.
• Identifying source-to-target transformation paths.
• Managing complex dependencies and model references.
• Case study: Data dependency mapping in Netflix's analytics infrastructure.

Module 4: Advanced Metadata Management and Data Cataloging
 

• Implementing structured metadata management.
• Documenting business definitions and technical attributes.
• Managing database schemas and source descriptions.
• Integrating dbt metadata with data catalog platforms.
• Establishing enterprise metadata standards.
• Case study: Enterprise data cataloging practices at Airbnb.

Module 5: Automated Documentation and Data Quality Testing
 

• Generating documentation using dbt commands.
• Implementing schema tests and data validation.
• Integrating documentation with continuous integration.
• Automating documentation updates and deployment.
• Monitoring documentation completeness and accuracy.
• Case study: Automated data quality practices in modern financial institutions.

Module 6: Version Control and Collaborative Documentation
 

• Managing dbt documentation through Git.
• Implementing collaborative documentation workflows.
• Reviewing and approving documentation changes.
• Maintaining documentation across development environments.
• Applying documentation versioning standards.
• Case study: Collaborative analytics engineering using Git-based workflows.

Module 7: Data Governance, Observability and Troubleshooting
 

• Implementing data lineage governance frameworks.
• Tracking transformation failures and dependencies.
• Identifying undocumented and unused data models.
• Integrating lineage with data observability platforms.
• Improving auditability and regulatory compliance.
• Case study: Data governance and traceability in global banking operations.

Module 8: Enterprise Implementation and Capstone Project
 

• Designing enterprise-wide dbt documentation frameworks.
• Building end-to-end data lineage documentation.
• Implementing automated documentation publishing.
• Evaluating documentation quality and operational performance.
• Developing sustainable documentation maintenance strategies.
• Case study: Implementing an enterprise dbt documentation framework for a multinational retail organization.

Training Methodology
 

• Instructor-led presentations and interactive discussions.
• Practical dbt configuration and SQL documentation exercises.
• Hands-on data lineage visualization workshops.
• Real-world enterprise case studies and group assignments.
• Collaborative documentation and Git version control projects.
• Capstone implementation and practical assessments.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.