Course Introduction
dbt Fundamentals for Analytics Engineering Training Course provides a practical foundation for professionals who want to transform raw data into trusted, analysis-ready datasets using modern analytics engineering practices. The course introduces dbt concepts, project structures, SQL-based transformations, modular data modeling, testing, documentation, version control, and deployment workflows.
Participants learn how dbt helps data teams organize transformation logic as maintainable analytical code rather than relying on manually managed SQL scripts. The training explores models, dependencies, sources, references, macros, packages, tests, documentation, and lineage while demonstrating how these capabilities contribute to reliable and reusable data transformation workflows. Current dbt documentation positions the platform around building, documenting, testing, and collaborating on data projects.
The course emphasizes analytics engineering principles such as modularity, separation of staging and business logic, reusable transformations, naming conventions, code organization, and data quality validation. Learners develop practical SQL models and understand how ref and source relationships establish dependencies between datasets and help create transparent analytical transformation pipelines.
Data quality and observability are central components of the training. Participants learn how to define and execute tests, document models and columns, investigate failures, and use lineage information to understand relationships across a data project. These practices help teams improve confidence in analytical datasets while making transformation logic easier for analysts, engineers, and business stakeholders to understand and maintain.
The training also introduces advanced dbt capabilities including incremental models, snapshots, macros, packages, Jinja, seeds, exposures, and reusable project configurations. Learners examine how these features can support larger transformation environments where processing efficiency, historical tracking, code reuse, and consistent development practices become increasingly important.
By completing the course, participants will be able to build structured dbt projects, develop modular SQL models, manage dependencies, implement data tests, document analytical assets, troubleshoot transformation workflows, and apply collaborative development practices. The course provides a practical foundation for using dbt as part of modern cloud data warehouses, lakehouses, and analytics engineering environments.
Duration
5 days
Who Should Attend
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Analytics engineers developing and maintaining SQL-based data transformation projects.
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Data analysts who want to build reliable and reusable analytical datasets.
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Data engineers supporting transformation workflows and modern data platforms.
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BI developers preparing governed datasets for dashboards and reporting.
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SQL developers transitioning toward analytics engineering practices.
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Data scientists who need trusted, well-structured datasets for analytical and machine learning workflows.
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Data architects designing modern transformation and analytical data architectures.
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Cloud data professionals working with cloud data warehouses and dbt-based pipelines.
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Database professionals interested in modular SQL transformation and data quality practices.
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Technical consultants implementing dbt and analytics engineering solutions.
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BI and reporting professionals seeking stronger data modeling and transformation skills.
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Data professionals preparing for modern analytics engineering responsibilities.
Course Objectives
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Understand dbt architecture, project concepts, analytics engineering principles, and the role of transformation-as-code in modern data environments.
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Create structured dbt projects and organize SQL models using maintainable naming, directory, configuration, and development conventions.
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Build modular transformation workflows using staging, intermediate, and analytical models while establishing clear dependencies between datasets.
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Use dbt references and sources to create reliable model relationships, improve lineage visibility, and manage upstream and downstream dependencies.
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Apply dbt testing capabilities to validate data quality, identify transformation problems, and increase confidence in analytical datasets.
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Create comprehensive project documentation that explains models, columns, sources, business logic, and relationships for technical and business users.
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Develop incremental models, snapshots, macros, seeds, and reusable project components for more scalable and maintainable transformation workflows.
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Apply Git-based development, code review, testing, and collaborative practices to manage dbt projects across data teams.
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Troubleshoot model failures, compilation issues, dependency problems, test failures, and data-quality concerns using appropriate diagnostic techniques.
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Design production-oriented dbt projects that support reliable analytics, governance, collaboration, maintainability, performance, and continuous improvement.
Comprehensive Course Outline
Module 1: dbt and Analytics Engineering Fundamentals
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Introduction to analytics engineering and the role of dbt in modern data transformation and analytical workflows.
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Understanding dbt projects, models, transformations, dependencies, environments, and the relationship between analytics and engineering responsibilities.
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Exploring the difference between raw data, staging models, intermediate transformations, and business-ready analytical datasets.
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Understanding how dbt supports collaborative development, documentation, testing, lineage, and repeatable data transformation processes.
Module 2: dbt Projects, Configuration, and Development Workflow
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Creating and organizing dbt projects using appropriate directories, project configuration, model organization, and naming conventions.
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Understanding profiles, targets, environments, project settings, model configurations, and other foundational configuration concepts.
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Running dbt commands for development workflows and understanding how project compilation and execution operate.
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Establishing maintainable project structures that support collaboration, scalability, readability, testing, and long-term project management.
Module 3: dbt Models and SQL Transformations
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Creating SQL-based dbt models that transform raw source data into clean, reusable, analysis-ready datasets.
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Applying filtering, joins, aggregations, calculations, conditional logic, window functions, and other SQL techniques within dbt models.
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Organizing transformations into modular model layers to reduce duplicated logic and improve maintainability.
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Developing analytical models that provide consistent business-ready datasets for reporting, dashboards, analytics, and downstream applications.
Module 4: Sources, References, Dependencies, and Lineage
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Defining source datasets and establishing clear relationships between raw data sources and downstream dbt models.
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Using
refrelationships to connect models and allow dbt to understand transformation dependencies automatically. -
Exploring dependency graphs and lineage to trace data from upstream sources through transformations to analytical outputs.
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Applying source freshness, documentation, naming, and dependency practices to improve visibility and reliability across data projects.
Module 5: Data Quality Testing and Validation
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Understanding the role of automated data testing in identifying unexpected values, broken relationships, missing records, and structural issues.
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Applying generic tests to validate uniqueness, non-null requirements, accepted values, and relationships between datasets.
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Developing appropriate testing strategies for critical business metrics, analytical models, source data, and transformation logic.
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Investigating test failures and applying corrective actions to improve the quality and reliability of analytical data products.
Module 6: Documentation, Metadata, and Data Discovery
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Creating model and column documentation that explains transformation logic, dataset meaning, ownership, and business context.
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Building useful documentation for sources, models, columns, tests, and other analytical assets within a dbt project.
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Using lineage and metadata to help technical and business users discover, understand, and confidently consume data assets.
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Establishing documentation practices that keep analytical projects transparent, maintainable, and accessible as data environments evolve.
Module 7: Advanced dbt Features and Reusable Transformations
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Developing reusable macros with Jinja to reduce duplicated SQL logic and simplify complex transformation patterns.
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Using packages and reusable project components to extend functionality and standardize common analytics engineering practices.
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Working with seeds to manage small reference datasets and incorporating controlled static data into transformation workflows.
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Exploring snapshots and historical data tracking concepts for scenarios where changes to source records need to be preserved over time.
Module 8: Incremental Models and Performance Optimization
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Understanding incremental models and identifying situations where incremental processing can reduce unnecessary transformation work.
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Designing incremental transformation logic that processes new or changed records while maintaining correct analytical results.
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Applying appropriate filtering, partitioning, clustering, materialization, and warehouse-specific optimization techniques where applicable.
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Evaluating model performance, execution behavior, processing volume, and transformation design to improve scalable dbt workflows.
Module 9: Git, Collaboration, Deployment, and Operations
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Using Git-based development workflows to manage dbt code, branches, commits, pull requests, reviews, and collaborative project changes.
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Establishing development, testing, and production workflows that reduce deployment risks and improve transformation reliability.
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Understanding continuous integration and automated validation practices for checking dbt models and tests before production deployment.
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Monitoring production transformation workflows, investigating failures, maintaining documentation, and applying operational improvements to dbt projects.
Module 10: End-to-End Analytics Engineering Project with dbt
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Designing an end-to-end dbt project that transforms raw source data into tested, documented, and business-ready analytical models.
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Building a layered transformation workflow that incorporates sources, staging models, intermediate logic, analytical models, tests, and documentation.
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Applying reusable macros, references, incremental processing, version control, and performance practices to improve project quality.
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Evaluating the completed project for maintainability, data quality, lineage, scalability, collaboration, and readiness for production analytics workloads.
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.
