dbt Cloud and Enterprise Analytics Engineering Training Course

 dbt Cloud and Enterprise Analytics Engineering Training Course 

Introduction 

dbt Cloud and Enterprise Analytics Engineering Training Course provides comprehensive practical knowledge and professional skills in modern data transformation, analytics engineering, cloud data platforms, and enterprise data management. The course focuses on leveraging dbt Cloud to build scalable, reliable, and maintainable data transformation workflows using analytics engineering best practices. Participants will explore SQL-based data modelling, automated data pipelines, version control, continuous integration and continuous deployment (CI/CD), data testing, documentation, and enterprise data governance. Through hands-on exercises, participants will learn how to transform raw data into trusted analytical datasets while improving data quality, operational efficiency, and business intelligence capabilities. 

The training also examines enterprise-scale dbt implementation, collaborative development environments, workflow orchestration, advanced testing strategies, and performance optimization across modern cloud data warehouses. Participants will gain practical experience managing dbt projects, implementing reusable data models, configuring deployment environments, and monitoring production analytics pipelines. International case studies from organizations using cloud-based analytics engineering approaches will demonstrate how modern data transformation supports financial reporting, operational intelligence, customer analytics, and data-driven decision-making. The course equips professionals with the expertise to implement enterprise-ready analytics engineering solutions that support digital transformation, data democratization, and scalable business intelligence. 

Course Objectives 

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

1. Understand dbt Cloud architecture and enterprise analytics engineering principles.
2. Develop scalable SQL-based data transformation workflows.
3. Configure dbt Cloud projects and development environments.
4. Implement advanced data modelling and modular transformation strategies.
5. Apply automated data testing and quality assurance frameworks.
6. Integrate Git-based version control and collaborative development.
7. Implement CI/CD pipelines for automated analytics deployment.
8. Manage enterprise data documentation and metadata.
9. Optimize cloud data warehouse performance and resource utilization.
10. Implement workflow orchestration and scheduling.
11. Apply enterprise data governance and access control.
12. Monitor production pipelines and troubleshoot transformation failures.
13. Design scalable analytics engineering architectures.

Organizational Benefits
 

1. Improved enterprise data quality and reliability.
2. Reduced manual data transformation processes.
3. Faster delivery of trusted business intelligence.
4. Enhanced collaboration between data teams.
5. Improved governance and regulatory compliance.
6. Optimized cloud data warehouse expenditure.
7. Increased analytics development productivity.
8. Standardized enterprise data modelling practices.
9. Improved operational reporting and decision-making.
10. Scalable data infrastructure supporting digital transformation.

Target Audience
 

1. Analytics engineers and data engineers.
2. Business intelligence developers and analysts.
3. Data architects and enterprise data managers.
4. Database administrators and SQL developers.
5. Cloud data platform specialists.
6. Data governance and quality professionals.
7. IT managers and digital transformation leaders.
8. Data science and business analytics professionals.

Course Duration: 5 days

Course Modules

Module 1: Introduction to dbt Cloud and Enterprise Analytics Engineering
 

• Analytics engineering concepts and modern data stack architecture.
• Introduction to dbt Cloud features and enterprise capabilities.
• Understanding ELT workflows and cloud data warehouses.
• Analytics engineering roles, responsibilities, and workflows.
• Enterprise implementation models and deployment strategies.
• Case study: Modern analytics transformation using dbt in a global financial services organization.

Module 2: dbt Cloud Architecture and Project Configuration
 

• dbt Cloud environments, accounts, projects, and configurations.
• Connecting Snowflake, BigQuery, Databricks, and Redshift.
• Configuring development credentials and environments.
• Managing project repositories and deployment settings.
• Understanding dbt Core and dbt Cloud integration.
• Case study: Enterprise cloud analytics infrastructure implementation in a multinational retail organization.

Module 3: Advanced SQL and Enterprise Data Modelling
 

• Advanced SQL transformations and reusable data models.
• Staging, intermediate, and mart modelling layers.
• Incremental models, snapshots, and ephemeral models.
• Jinja templating, macros, and reusable SQL components.
• Dimensional modelling and enterprise data warehouse design.
• Case study: Enterprise customer analytics modelling using dimensional data architecture.

Module 4: Data Quality, Testing, and Documentation
 

• Implementing schema tests and data integrity checks.
• Advanced data validation and custom testing strategies.
• Source freshness monitoring and anomaly detection.
• Generating model documentation and lineage graphs.
• Implementing data contracts and quality assurance.
• Case study: Improving financial reporting reliability through automated dbt testing.

Module 5: Git Integration, Collaboration, and CI/CD
 

• Integrating GitHub and GitLab with dbt Cloud.
• Branching strategies and collaborative development workflows.
• Pull requests, code reviews, and version management.
• Implementing continuous integration and automated testing.
• Managing production deployment and release workflows.
• Case study: Enterprise analytics deployment automation in a global technology company.

Module 6: Enterprise Orchestration and Production Management
 

• Configuring dbt Cloud jobs and scheduled executions.
• Managing dependencies and workflow orchestration.
• Implementing job monitoring and failure notifications.
• Managing production environments and execution logs.
• Troubleshooting pipeline failures and operational incidents.
• Case study: Automated enterprise data pipeline management in an international e-commerce organization.

Module 7: Performance Optimization, Security, and Governance
 

• Optimizing SQL queries and warehouse resource consumption.
• Managing model materializations and incremental processing.
• Implementing role-based access control and permissions.
• Applying enterprise metadata and governance standards.
• Managing sensitive data and compliance requirements.
• Case study: Cloud analytics optimization and governance in a multinational banking institution.

Module 8: Enterprise Implementation and Capstone Project
 

• Designing an end-to-end enterprise dbt Cloud architecture.
• Developing integrated transformation and analytics workflows.
• Implementing automated testing and deployment pipelines.
• Establishing monitoring, documentation, and governance.
• Evaluating scalability, reliability, and operational performance.
• Case study: Building an enterprise analytics engineering solution for a global logistics organization.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using dbt Cloud and cloud data warehouses.
• Hands-on SQL development and data transformation exercises.
• Enterprise analytics engineering simulations and group projects.
• International case study analysis and implementation workshops.
• Capstone projects, technical assessments, and feedback 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.