Analytics Engineering with dbt Training Course

 Analytics Engineering with dbt Training Course 

Introduction 

Analytics Engineering with dbt Training Course provides comprehensive practical knowledge and technical skills for transforming raw data into reliable, structured, analytics-ready datasets using modern data engineering technologies. The course focuses on analytics engineering principles, data transformation workflows, dbt (Data Build Tool), SQL development, data modelling, automated testing, data documentation, version control, and cloud data warehouse integration. Participants will explore modern ELT (Extract, Load, Transform) architectures, modular data pipelines, reusable SQL models, data quality frameworks, and analytics automation to develop scalable and maintainable data transformation solutions. The training incorporates industry-leading tools and emerging best practices in analytics engineering, enabling organizations to improve data reliability, operational efficiency, and business intelligence capabilities. 

The course also examines advanced dbt development, incremental modelling, snapshot management, dependency resolution, CI/CD integration, workflow orchestration, and collaborative analytics development. Participants will gain practical experience designing production-ready transformation pipelines, implementing data governance, managing metadata, and optimizing warehouse performance. Through hands-on exercises and international industry case studies, the training demonstrates how organizations leverage dbt and modern data stack technologies to accelerate data-driven decision-making. By the end of the programme, participants will be equipped to implement enterprise-grade analytics engineering solutions that support business intelligence, predictive analytics, data democratization, and digital transformation initiatives. 

Course Objectives 

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

1. Understand modern analytics engineering principles and dbt architecture.
2. Develop SQL-based data transformation models using dbt.
3. Implement scalable ELT pipelines and modern data stack workflows.
4. Design modular, reusable, and maintainable data models.
5. Apply automated data testing and data quality assurance.
6. Implement dbt documentation and metadata management.
7. Integrate Git and version control into analytics workflows.
8. Develop incremental models and manage dbt snapshots.
9. Optimize data warehouse queries and transformation performance.
10. Implement CI/CD automation for dbt projects.
11. Integrate dbt with cloud data warehouses and orchestration tools.
12. Apply data governance, security, and compliance frameworks.
13. Build production-ready analytics engineering solutions.

Organizational Benefits
 

1. Improved enterprise data quality and reliability.
2. Reduced data transformation and maintenance costs.
3. Faster business intelligence and reporting processes.
4. Enhanced collaboration between data engineers and analysts.
5. Improved scalability of enterprise data infrastructure.
6. Stronger data governance and regulatory compliance.
7. Increased automation of data transformation workflows.
8. More efficient data warehouse utilization.
9. Reliable decision-making through trusted datasets.
10. Accelerated digital transformation and analytics innovation.

Target Audiences
 

1. Analytics engineers and data engineers.
2. Business intelligence developers and analysts.
3. Data scientists and machine learning professionals.
4. Database administrators and SQL developers.
5. Data architects and warehouse specialists.
6. Information technology managers and consultants.
7. Business analysts and reporting specialists.
8. Data platform engineers and technology professionals.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Analytics Engineering and dbt
 

• Analytics engineering concepts and industry applications.
• Understanding dbt architecture and core components.
• Modern data stack and ELT pipeline fundamentals.
• Analytics engineering workflows and project structures.
• dbt adoption case study: Airbnb's analytics engineering practices.
• Hands-on introduction to dbt Core and dbt Cloud.

Module 2: SQL Development and Data Transformation
 

• Advanced SQL queries and transformation techniques.
• Building staging, intermediate, and mart models.
• SQL modularization and reusable transformation logic.
• Managing source definitions and data dependencies.
• Case study: Transforming retail datasets for Walmart-style analytics.
• Practical development of SQL-based dbt models.

Module 3: Advanced dbt Data Modelling
 

• Designing dimensional models and star schemas.
• Implementing fact and dimension tables.
• Managing model materializations and configurations.
• Developing incremental models and snapshots.
• Case study: Snowflake-based enterprise data modelling.
• Practical implementation of reusable analytics models.

Module 4: dbt Testing and Data Quality Management
 

• Implementing generic and singular data tests.
• Managing uniqueness, relationships, and null constraints.
• Developing custom data quality validation frameworks.
• Monitoring pipeline failures and data anomalies.
• Case study: Ensuring financial data accuracy in banking systems.
• Practical automated testing of dbt transformation models.

Module 5: Documentation, Governance and Metadata Management
 

• Generating automated dbt project documentation.
• Managing data lineage and dependency graphs.
• Implementing metadata and data cataloguing.
• Applying access controls and data governance principles.
• Case study: Netflix-inspired data governance practices.
• Developing documented and auditable analytics workflows.

Module 6: Git, CI/CD and Workflow Automation
 

• Git fundamentals and collaborative development.
• Managing branches, commits, and pull requests.
• Implementing dbt CI/CD deployment pipelines.
• Automating testing and production deployments.
• Case study: GitHub-based analytics engineering workflows.
• Practical integration of dbt with automated deployment tools.

Module 7: Cloud Data Warehouses and Performance Optimization
 

• Integrating dbt with Snowflake, BigQuery, and Redshift.
• Optimizing SQL queries and warehouse performance.
• Managing incremental processing and partitioning.
• Monitoring compute costs and resource utilization.
• Case study: Google BigQuery analytics transformation.
• Practical cloud warehouse performance optimization.

Module 8: Enterprise dbt Deployment and Capstone Project
 

• Designing production-ready dbt architectures.
• Integrating dbt with Apache Airflow and orchestration tools.
• Implementing monitoring, logging, and error management.
• Managing enterprise deployment and scalability.
• Case study: Spotify-inspired scalable analytics pipelines.
• Capstone project: Building an end-to-end analytics engineering pipeline.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using dbt Core and dbt Cloud.
• Hands-on SQL development and data transformation exercises.
• Real-world enterprise analytics engineering case studies.
• Group projects and collaborative problem-solving sessions.
• Practical assessments and capstone project implementation.

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.