Modern Data Transformation with dbt Training Course

 Modern Data Transformation with dbt Training Course 

Introduction 

Modern Data Transformation with dbt Training Course is designed to equip participants with practical skills and advanced knowledge of modern data transformation, analytics engineering, and cloud-based data management. As organizations increasingly adopt cloud data warehouses, big data platforms, and automated analytics pipelines, dbt (Data Build Tool) has become an essential technology for building reliable, scalable, and maintainable data transformation workflows. This course explores modern data transformation architecture, SQL-based data modeling, modular transformation techniques, data pipeline automation, version control, testing, documentation, and continuous integration. Participants will learn how to transform raw datasets into trusted analytical models using industry-standard analytics engineering practices. 

The course also focuses on developing enterprise-ready data transformation solutions through practical exercises, real-world projects, and international case studies. Participants will gain experience implementing dbt projects, managing dependencies, optimizing transformation performance, enforcing data quality standards, and integrating dbt with cloud data platforms such as Snowflake, Google BigQuery, and Amazon Redshift. Emphasis is placed on scalable data architecture, metadata management, automated testing, governance, and collaborative development. By the end of the training, participants will be able to design, implement, and maintain modern data transformation pipelines that support business intelligence, artificial intelligence, machine learning, and data-driven decision-making. 

Course Objectives 

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

1. Understand modern data transformation and analytics engineering principles.
2. Develop scalable data transformation workflows using dbt.
3. Apply advanced SQL techniques for analytical data modeling.
4. Build modular, reusable, and maintainable transformation pipelines.
5. Implement automated data testing and quality assurance.
6. Manage data dependencies using dbt DAGs.
7. Integrate dbt with cloud data warehouses.
8. Apply Git version control and collaborative development.
9. Automate data pipeline deployment and execution.
10. Develop comprehensive data documentation and metadata.
11. Optimize transformation performance and warehouse costs.
12. Implement data governance, security, and compliance practices.
13. Apply continuous integration and continuous deployment (CI/CD) to analytics engineering.

Organizational Benefits
 

1. Improved enterprise data quality and reliability.
2. Reduced manual data transformation workloads.
3. Faster delivery of business intelligence solutions.
4. Enhanced analytics engineering productivity.
5. Improved data governance and transparency.
6. Reduced cloud data warehouse operating costs.
7. Standardized enterprise data transformation processes.
8. Better collaboration between data engineers and analysts.
9. Increased scalability of analytical infrastructure.
10. Improved data-driven strategic decision-making.

Target Audiences
 

1. Data engineers and analytics engineers.
2. Business intelligence developers and analysts.
3. Database administrators and data architects.
4. Data scientists and machine learning engineers.
5. Cloud computing and data warehouse professionals.
6. SQL developers and database specialists.
7. Information technology managers and consultants.
8. Data governance and enterprise analytics professionals.

Course Duration: 5 days
 
Course Modules

Module 1: Fundamentals of Modern Data Transformation
 

• Introduction to modern data transformation and analytics engineering.
• Evolution of traditional ETL and ELT architectures.
• Introduction to dbt architecture and ecosystem.
• Understanding cloud data warehouses and transformation workflows.
• Data transformation case study: Netflix analytical data architecture.
• Global case study: Spotify's data-driven analytics ecosystem.

Module 2: dbt Project Setup and Configuration
 

• Installing and configuring dbt Core and dbt Cloud.
• Creating and managing dbt projects.
• Understanding project structures and configuration files.
• Connecting dbt to Snowflake, BigQuery, and Redshift.
• Managing profiles, environments, and project dependencies.
• Practical case study: Building a retail analytics transformation project.

Module 3: SQL-Based Data Modeling and Transformation
 

• Developing SQL models for analytical datasets.
• Implementing staging, intermediate, and mart layers.
• Creating reusable transformations and modular SQL models.
• Applying incremental models and materialization strategies.
• Global case study: Walmart's enterprise retail data modeling.
• Practical exercise: Developing customer and sales analytical models.

Module 4: Data Testing and Quality Management
 

• Implementing schema tests and data validation rules.
• Developing custom data tests using SQL.
• Applying uniqueness, relationships, and accepted-values tests.
• Managing data quality monitoring and error detection.
• Case study: Financial services data quality assurance.
• Practical exercise: Building automated data quality checks.

Module 5: Advanced dbt Features and Data Documentation
 

• Managing model dependencies and DAG visualizations.
• Implementing macros, packages, and reusable Jinja templates.
• Developing snapshots and historical data tracking.
• Generating automated documentation and data lineage.
• Case study: E-commerce customer analytics and historical tracking.
• Practical exercise: Creating a documented enterprise dbt project.

Module 6: Version Control and Collaborative Development
 

• Introduction to Git and repository management.
• Managing branches, commits, and pull requests.
• Implementing collaborative analytics engineering workflows.
• Applying code reviews and development standards.
• Case study: Collaborative data transformation in a multinational enterprise.
• Practical exercise: Managing dbt projects through GitHub.

Module 7: Automation, CI/CD, and Performance Optimization
 

• Automating dbt workflows and scheduled executions.
• Implementing continuous integration and continuous deployment.
• Optimizing SQL queries and model execution performance.
• Managing incremental processing and warehouse resource utilization.
• Case study: Cloud data warehouse optimization at enterprise scale.
• Practical exercise: Building an automated dbt deployment pipeline.

Module 8: Enterprise Deployment, Governance, and Capstone Project
 

• Implementing production-ready dbt transformation architectures.
• Applying access controls, security, and data governance.
• Managing deployment environments and production monitoring.
• Integrating dbt workflows with enterprise data platforms.
• Global case study: Airbnb's analytics engineering practices.
• Capstone project: Designing an end-to-end modern data transformation solution.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical exercises using dbt and SQL development environments.
• Hands-on cloud data warehouse integration projects.
• Group discussions and collaborative problem-solving activities.
• International case studies and real-world business scenarios.
• Capstone projects, practical assessments, and knowledge-sharing sessions.

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.