Course Introduction
Data Transformation Testing with dbt Training Course provides a practical and comprehensive approach to validating transformed data throughout modern analytics engineering workflows. The course focuses on using dbt testing capabilities to identify transformation errors, validate business rules, detect unexpected data conditions, and establish reliable quality controls across analytical datasets and production data pipelines.
This dbt testing course helps participants understand how testing should be integrated into the complete data transformation lifecycle rather than treated as a final validation activity. Learners explore how source validation, model testing, business-rule verification, relationship checks, schema validation, and transformation-level quality controls can work together to create dependable analytical data products.
Participants learn how to design and implement effective tests for common transformation requirements, including uniqueness, completeness, referential integrity, accepted values, data relationships, and business-specific conditions. The training also emphasizes interpreting test failures, tracing problems through model dependencies, identifying root causes, and distinguishing between genuine transformation defects and expected changes in source data.
The course provides hands-on guidance for testing staging models, intermediate transformations, dimensional models, fact tables, metrics, and downstream analytical datasets. Learners examine how testing strategies can be incorporated into modular dbt projects while maintaining readable SQL, meaningful documentation, reusable testing logic, and development standards that support collaboration across analytics engineering and data teams.
Advanced testing practices are also covered, including custom tests, reusable validation logic, test configuration, selective execution, failure investigation, CI/CD integration, and production-oriented quality workflows. Participants explore how modern dbt development environments and capabilities can support building, testing, documenting, and collaborating on data projects, while state-aware features can help teams focus execution on relevant project changes.
By completing this Data Transformation Testing with dbt Training Course, participants will be equipped to develop structured data validation frameworks that improve confidence in transformed datasets and reduce the risk of inaccurate analytics. The course combines practical dbt techniques with data quality principles, troubleshooting methods, automation concepts, and governance practices suitable for modern enterprise data environments.
Duration
5 days
Who Should Attend
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Analytics engineers responsible for developing and validating dbt transformation projects.
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Data engineers who need to implement automated quality controls within ELT pipelines.
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Data analysts who want to validate analytical models and improve confidence in reporting datasets.
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BI developers responsible for consuming and validating transformed warehouse data.
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Data quality specialists working with automated testing and transformation validation frameworks.
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SQL developers seeking practical experience with dbt testing and data transformation quality.
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Data warehouse developers responsible for validating fact, dimension, staging, and intermediate models.
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Data platform engineers supporting reliable and scalable analytical data environments.
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Data scientists who depend on trustworthy transformed datasets for analytics and machine learning.
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Technical leads establishing testing standards and quality practices for modern data teams.
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DevOps and data operations professionals supporting CI/CD and automated data validation workflows.
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Project and data governance professionals interested in improving reliability and traceability of analytical data.
Course Objectives
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Develop a strong understanding of data transformation testing principles and how dbt can be used to validate analytical models throughout the transformation lifecycle.
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Design comprehensive testing strategies that address data completeness, uniqueness, consistency, referential integrity, accepted values, and business-specific transformation requirements.
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Implement effective dbt tests for staging, intermediate, dimensional, fact, and analytical models while maintaining clear and maintainable project structures.
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Create custom validation logic for complex business rules and transformation conditions that cannot be adequately addressed through standard testing patterns.
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Diagnose failed transformation tests systematically by tracing dependencies, reviewing SQL logic, examining source data, and identifying the underlying cause of data quality problems.
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Apply appropriate testing configurations and execution strategies to improve validation efficiency while avoiding unnecessary processing across large dbt projects.
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Integrate data transformation testing into collaborative development, code review, CI/CD, deployment, and production monitoring workflows to improve release confidence.
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Establish documentation and naming practices that make tests understandable, maintainable, discoverable, and useful to analytics engineers and downstream data consumers.
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Optimize testing approaches for large-scale data environments by balancing coverage, execution performance, business risk, maintainability, and operational requirements.
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Develop an enterprise-oriented dbt testing framework that supports continuous data quality improvement, reliable analytics, governance, and trustworthy business reporting.
Comprehensive Course Outline
Module 1: Fundamentals of Data Transformation Testing with dbt
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Understanding the role of transformation testing in modern analytics engineering and how testing supports trustworthy analytical data products.
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Exploring dbt project architecture, transformation workflows, model dependencies, and the points where testing can be introduced throughout development.
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Distinguishing source validation, transformation testing, model testing, business-rule validation, and downstream analytical quality assurance activities.
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Establishing practical testing principles covering coverage, maintainability, reliability, execution efficiency, documentation, and business relevance.
Module 2: dbt Testing Fundamentals and Test Design
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Understanding standard dbt testing concepts and how assertions can be applied to transformed datasets to identify unexpected data conditions.
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Designing meaningful tests for uniqueness, completeness, accepted values, relationships, and other common analytical data quality requirements.
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Applying testing strategies to different types of dbt models while considering the purpose, consumers, risk, and expected behavior of each dataset.
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Developing clear testing conventions that help teams consistently define, organize, maintain, execute, and review transformation validation logic.
Module 3: Testing Sources and Staging Transformations
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Establishing validation approaches for raw sources to identify missing records, unexpected values, structural changes, and other upstream data quality concerns.
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Testing staging transformations to verify that standardization, renaming, type conversion, filtering, and cleansing logic produce the intended results.
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Identifying how source-system changes can affect downstream models and designing tests that provide early visibility into potentially damaging data changes.
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Developing source and staging test coverage that creates a reliable foundation for more complex intermediate and business transformation models.
Module 4: Testing Business Transformations and Data Models
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Validating intermediate models to ensure joins, calculations, filtering rules, aggregations, and derived fields produce expected transformation outcomes.
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Testing fact and dimension models for uniqueness, completeness, referential integrity, dimensional consistency, and expected business relationships.
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Translating business requirements into measurable data validation conditions that can be incorporated into repeatable dbt testing workflows.
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Identifying transformation logic that requires additional validation because errors could significantly affect financial, operational, customer, or management reporting.
Module 5: Custom Tests and Advanced Validation Logic
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Designing custom data tests for complex business requirements that cannot be adequately represented through basic standard validation patterns.
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Creating reusable testing logic that reduces duplication and allows consistent validation rules to be applied across multiple models and transformation domains.
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Developing SQL-based assertions that validate calculations, thresholds, conditional relationships, temporal conditions, and other specialized transformation requirements.
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Managing custom tests for readability, maintainability, documentation, reuse, and compatibility with broader enterprise analytics engineering standards.
Module 6: Test Failures, Debugging, and Root Cause Analysis
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Developing systematic techniques for investigating failed dbt tests and determining whether problems originate in source data, transformation logic, configuration, or business requirements.
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Tracing failed validations through model dependencies and transformation lineage to identify upstream conditions that may have caused downstream quality issues.
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Reviewing SQL transformations, source records, joins, filters, aggregations, and assumptions when diagnosing unexpected test results.
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Establishing repeatable incident and remediation workflows that help analytics teams resolve data quality failures while preventing recurring transformation defects.
Module 7: Testing Performance and Selective Execution
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Understanding how extensive test coverage can affect execution time and designing validation strategies that balance quality assurance with efficient warehouse utilization.
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Applying model and test selection techniques to focus validation on changed, affected, high-risk, or production-critical areas of a dbt project.
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Evaluating testing frequency and execution scope across development, pull-request validation, scheduled workflows, and production deployment processes.
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Exploring state-aware development concepts that can help teams avoid unnecessary rebuilds and focus processing on relevant project changes.
Module 8: Documentation, Lineage, and Data Quality Governance
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Documenting transformation tests and business validation rules so developers and data consumers understand what each quality control is designed to protect.
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Using lineage and dependency information to understand how test failures can affect downstream models, dashboards, reports, and analytical data products.
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Establishing data quality ownership, naming conventions, test documentation, severity practices, and maintenance standards across collaborative dbt projects.
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Integrating transformation testing with broader data governance practices to improve accountability, discoverability, transparency, and confidence in analytical datasets.
Module 9: CI/CD, Automation, and Production Testing
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Integrating dbt testing into development and deployment workflows so transformation changes can be validated before they reach production environments.
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Designing automated validation stages for code changes, pull requests, scheduled transformations, releases, and other controlled data delivery processes.
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Establishing practical approaches for managing test failures in production while distinguishing blocking defects from known, expected, or non-critical conditions.
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Exploring collaborative dbt development capabilities that support building, testing, documenting, version-controlling, and deploying transformation projects.
Module 10: Enterprise Data Transformation Testing and Capstone
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Designing an end-to-end dbt testing framework covering sources, staging models, business transformations, analytical models, documentation, and deployment validation.
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Applying risk-based testing principles to prioritize critical datasets, high-impact transformations, sensitive business logic, and important downstream analytical dependencies.
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Developing a practical capstone project that combines standard tests, custom validation logic, debugging, documentation, selective execution, and automated workflows.
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Reviewing enterprise testing practices and developing a continuous improvement roadmap for maintaining reliable, scalable, and trusted dbt transformation environments.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
