Advanced dbt for Data Transformation Training Course

 Advanced dbt for Data Transformation Training Course 

Introduction 

Advanced dbt for Data Transformation Training Course is designed to equip data professionals with advanced technical competencies in modern data transformation, analytics engineering, cloud data warehousing, and enterprise data pipeline development. The course explores advanced dbt (data build tool) capabilities, including modular SQL development, reusable macros, incremental models, snapshot management, data testing, documentation automation, and dependency management. Participants gain practical experience designing scalable, maintainable, and high-performance data transformation workflows using modern analytics engineering practices, version control, continuous integration and deployment, and automated data quality frameworks. 

The training emphasizes enterprise data architecture, workflow orchestration, performance optimization, data governance, and reliable analytics infrastructure. Through practical exercises and global industry case studies, participants learn how to implement advanced dbt projects, manage complex data transformations, optimize warehouse workloads, and establish production-ready analytics engineering environments. The course also examines applications of dbt with Snowflake, Google BigQuery, Amazon Redshift, Databricks, and cloud-native data platforms to support business intelligence, predictive analytics, and data-driven decision-making. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Implement advanced dbt architectures for enterprise data transformation.
2. Develop reusable SQL models, macros, and Jinja templates.
3. Design scalable incremental models and snapshot strategies.
4. Optimize data warehouse performance and transformation workloads.
5. Implement automated data testing and quality assurance.
6. Manage complex model dependencies and data lineage.
7. Integrate dbt with modern cloud data warehouses.
8. Apply version control using Git and collaborative development workflows.
9. Configure continuous integration and continuous deployment pipelines.
10. Implement advanced documentation and metadata management.
11. Strengthen data governance, security, and compliance.
12. Troubleshoot complex transformation failures and pipeline dependencies.
13. Establish production-ready analytics engineering frameworks.

Organizational Benefits
 

1. Improved enterprise data transformation efficiency.
2. Reduced operational costs through optimized SQL workloads.
3. Enhanced data quality and reporting accuracy.
4. Improved scalability of analytics infrastructure.
5. Faster deployment of reliable data products.
6. Stronger data governance and regulatory compliance.
7. Reduced manual intervention through automation.
8. Improved collaboration between data engineering teams.
9. Greater visibility through automated documentation and lineage.
10. Enhanced organizational decision-making through trusted data.

Target Audiences
 

1. Data engineers and analytics engineers.
2. Business intelligence developers and analysts.
3. Database administrators and architects.
4. Cloud data platform specialists.
5. Data warehouse developers.
6. Data architects and technical consultants.
7. Machine learning and data science professionals.
8. IT managers and enterprise data transformation leaders.

Course Duration: 10 days
 
Course Modules

Module 1: Advanced dbt Architecture and Project Design
 

• Enterprise dbt architecture and project structures.
• Advanced model organization and modular development.
• Environment-specific configurations and deployment strategies.
• Managing dependencies and reusable components.
• Scalable analytics engineering architecture.
• Case study: Enterprise analytics architecture implementation at Airbnb.

Module 2: Advanced SQL Modeling and Transformation
 

• Complex SQL transformations and analytical queries.
• Common table expressions and window functions.
• Modular SQL development and model abstraction.
• Advanced joins, aggregations, and optimization.
• Reusable transformation patterns.
• Case study: Large-scale retail analytics transformation at Walmart.

Module 3: Jinja Templating and Advanced Macros
 

• Advanced Jinja syntax and dynamic SQL generation.
• Developing reusable custom macros.
• Macro arguments, variables, and conditional logic.
• Creating reusable database functions.
• Package development and macro documentation.
• Case study: Standardizing transformation workflows across enterprise datasets.

Module 4: Incremental Models and Snapshot Management
 

• Advanced incremental model configurations.
• Unique keys and incremental strategies.
• Managing slowly changing dimensions.
• Snapshot strategies and historical data tracking.
• Handling late-arriving and changing records.
• Case study: Historical customer data management in financial services.

Module 5: Advanced dbt Testing and Data Quality
 

• Generic and singular data tests.
• Custom test development and reusable assertions.
• Data contracts and schema validation.
• Freshness monitoring and anomaly detection.
• Automated data quality reporting.
• Case study: Enterprise data quality assurance in banking.

Module 6: Data Warehouse Performance Optimization
 

• Query execution plans and performance profiling.
• Optimizing incremental transformations.
• Warehouse resource allocation and workload management.
• Partitioning, clustering, and materialization strategies.
• Reducing computational costs and processing times.
• Case study: Snowflake transformation optimization in large-scale analytics.

Module 7: dbt with Cloud Data Warehouses
 

• Integrating dbt with Snowflake and BigQuery.
• Amazon Redshift configuration and connectivity.
• Databricks integration and lakehouse transformations.
• Managing credentials and warehouse environments.
• Cross-platform transformation architecture.
• Case study: Cloud data modernization using Google BigQuery.

Module 8: Advanced Documentation and Data Lineage
 

• Automated documentation generation.
• Model descriptions and column-level metadata.
• Understanding dependency graphs and lineage.
• Documentation testing and maintenance.
• Metadata governance and discoverability.
• Case study: Enterprise data catalog implementation at a multinational organization.

Module 9: Git, Collaboration and Development Workflows
 

• Git branching and repository management.
• Pull requests and collaborative code reviews.
• Managing development and production environments.
• Resolving conflicts and maintaining code quality.
• dbt project collaboration standards.
• Case study: Distributed analytics engineering team collaboration.

Module 10: Continuous Integration and Continuous Deployment
 

• Automated dbt build and deployment workflows.
• GitHub Actions and CI/CD configuration.
• Automated testing before production deployment.
• Environment promotion and release management.
• Deployment monitoring and rollback strategies.
• Case study: Automated analytics deployment in a cloud-native enterprise.

Module 11: Advanced dbt Packages and Extensions
 

• Installing and configuring dbt packages.
• Using community-maintained packages.
• Developing custom organizational packages.
• Package versioning and dependency management.
• Extending transformation capabilities.
• Case study: Reusable transformation libraries for multinational reporting.

Module 12: dbt Cloud and Workflow Orchestration
 

• Advanced dbt Cloud configuration.
• Job scheduling and execution management.
• Integrating Airflow and other orchestration platforms.
• Managing pipeline dependencies and retries.
• Monitoring automated transformation workflows.
• Case study: Enterprise pipeline orchestration in global e-commerce.

Module 13: Data Governance, Security and Compliance
 

• Role-based access control and permissions.
• Managing sensitive data and credentials.
• Data governance and audit trails.
• Implementing regulatory compliance controls.
• Secure development and deployment practices.
• Case study: Financial data governance under international compliance frameworks.

Module 14: Troubleshooting and Production Reliability
 

• Diagnosing model execution failures.
• Debugging SQL and Jinja compilation errors.
• Managing failed tests and broken dependencies.
• Pipeline observability and operational monitoring.
• Recovery strategies and production reliability.
• Case study: Resolving transformation failures in high-volume data environments.

Module 15: Enterprise dbt Implementation and Capstone Project
 

• Designing an end-to-end enterprise dbt project.
• Building modular transformation pipelines.
• Implementing automated testing and documentation.
• Configuring CI/CD and production deployment.
• Evaluating scalability, performance, and reliability.
• Case study: Developing an enterprise analytics platform inspired by Airbnb's modern data architecture.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using advanced dbt features.
• Hands-on SQL transformation and macro development exercises.
• Real-world enterprise data engineering case studies.
• Collaborative group projects and technical problem-solving.
• Capstone implementation, performance evaluation, and knowledge assessment.

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.