Data Quality Testing with dbt Training Course

 Data Quality Testing with dbt Training Course 

Introduction 

Data Quality Testing with dbt Training Course equips participants with practical skills in modern data quality engineering, automated data validation, analytics engineering, and data transformation testing. As organizations increasingly adopt cloud data warehouses, business intelligence platforms, and data-driven decision-making, maintaining accurate, consistent, complete, and reliable data has become essential. This course explores dbt (data build tool) testing capabilities, including schema tests, generic tests, singular tests, unit tests, source freshness monitoring, data assertions, and automated testing frameworks. Participants will gain hands-on experience designing scalable data quality strategies that identify data anomalies, prevent transformation errors, and improve data integrity across complex data pipelines. 

The course further examines advanced data observability, continuous integration and continuous deployment (CI/CD), test-driven development, metadata management, data lineage, and automated quality monitoring. Through practical exercises and international industry case studies, participants will learn how to implement reliable testing workflows, manage data dependencies, troubleshoot transformation failures, and establish enterprise-wide data quality standards. The training emphasizes modern analytics engineering practices using dbt Core, dbt Cloud, SQL, Git, and cloud data warehouse technologies, enabling organizations to strengthen data governance, improve reporting accuracy, and build trustworthy data products. 

Course Objectives 

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

1. Understand data quality engineering principles and modern dbt testing frameworks.
2. Implement automated data validation and transformation testing.
3. Configure dbt schema tests and reusable generic tests.
4. Develop singular tests for complex business requirements.
5. Apply dbt unit testing to validate transformation logic.
6. Implement source freshness checks and monitoring.
7. Identify data anomalies, inconsistencies, and integrity issues.
8. Integrate automated testing into CI/CD pipelines.
9. Apply SQL-based data quality validation techniques.
10. Implement data observability and quality monitoring strategies.
11. Manage test documentation, metadata, and data lineage.
12. Troubleshoot failed tests and optimize testing performance.
13. Establish enterprise data quality standards and governance frameworks.

Organizational Benefits
 

1. Improved enterprise data accuracy, completeness, and consistency.
2. Reduced reporting errors and unreliable business intelligence.
3. Lower data maintenance and operational troubleshooting costs.
4. Enhanced confidence in analytics and business decisions.
5. Improved regulatory compliance and data governance.
6. Faster identification of data pipeline failures.
7. Increased automation of data quality assurance.
8. Improved collaboration between data engineers and analysts.
9. More reliable cloud data warehouse operations.
10. Stronger enterprise data observability and scalability.

Target Audiences
 

1. Data engineers and analytics engineers.
2. Database administrators and SQL developers.
3. Business intelligence and reporting professionals.
4. Data quality analysts and testing specialists.
5. Data architects and warehouse developers.
6. Data governance and compliance professionals.
7. Cloud data platform administrators.
8. IT managers and data science professionals.

Course Duration: 5 days
 
Course Modules

Module 1: Fundamentals of Data Quality Testing and dbt
 

• Introduction to data quality dimensions, frameworks, and validation principles.
• Understanding dbt architecture and modern analytics engineering workflows.
• Data accuracy, completeness, consistency, uniqueness, and validity.
• Introduction to automated testing in cloud data warehouses.
• Establishing data quality rules and enterprise validation standards.
• Case study: Amazon's data-driven operations and the importance of reliable enterprise data.

Module 2: dbt Schema Testing and Data Validation
 

• Configuring built-in dbt schema tests.
• Implementing unique, not-null, accepted-values, and relationships tests.
• Defining reusable YAML-based testing configurations.
• Applying referential integrity and primary key validation.
• Managing schema test failures and troubleshooting.
• Case study: Netflix-style streaming analytics and customer data validation.

Module 3: Generic Tests and Reusable Testing Frameworks
 

• Developing custom generic tests using Jinja and SQL.
• Creating reusable data quality validation macros.
• Parameterizing tests for multiple data models.
• Implementing scalable testing frameworks across datasets.
• Managing test configurations and reusable testing packages.
• Case study: Airbnb-style booking analytics and reusable validation rules.

Module 4: Singular Tests and Advanced SQL Validation
 

• Developing singular tests for complex business requirements.
• Writing SQL assertions for business logic validation.
• Identifying duplicate records and inconsistent transactions.
• Testing financial calculations and aggregation accuracy.
• Validating transformation outputs against expected results.
• Case study: Financial transaction reconciliation in global banking institutions.

Module 5: dbt Unit Testing and Transformation Verification
 

• Understanding unit testing principles in analytics engineering.
• Configuring dbt unit tests for SQL transformation logic.
• Defining input fixtures and expected outputs.
• Testing joins, calculations, filters, and conditional statements.
• Troubleshooting transformation defects and unexpected results.
• Case study: Retail sales forecasting and inventory transformation validation.

Module 6: Source Freshness, Data Observability and Monitoring
 

• Configuring dbt source freshness checks.
• Monitoring data ingestion delays and pipeline reliability.
• Implementing data observability and anomaly detection.
• Tracking data quality metrics and performance indicators.
• Developing automated alerts and incident response procedures.
• Case study: Global e-commerce data pipelines and real-time monitoring.

Module 7: CI/CD Automation and Data Quality Governance
 

• Integrating dbt testing into CI/CD workflows.
• Automating test execution using Git and GitHub Actions.
• Implementing pull request validation and deployment controls.
• Establishing data quality documentation and governance standards.
• Managing testing environments and version control.
• Case study: Google Cloud-based analytics engineering and automated deployment practices.

Module 8: Advanced Data Quality Engineering and Capstone Project
 

• Designing enterprise-wide data quality testing strategies.
• Optimizing test execution and warehouse resource utilization.
• Implementing data lineage and metadata-driven quality management.
• Developing comprehensive data quality dashboards and reports.
• Building an end-to-end automated dbt testing framework.
• Case study: A multinational organization implementing automated data quality controls across distributed data warehouses.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using dbt Core and dbt Cloud.
• Hands-on SQL programming and automated testing exercises.
• Real-world international industry case studies.
• Group assignments and collaborative problem-solving.
• Capstone projects involving enterprise data quality testing.
• Practical assessments, feedback, 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.