Course Introduction
dbt CI/CD and Deployment Training Course provides a practical and structured learning path for professionals who want to automate, control, and optimize the delivery of dbt data transformation projects. The course focuses on applying software engineering principles to analytics workflows, including version control, code review, automated testing, environment management, continuous integration, continuous deployment, release management, and production operations.
This comprehensive dbt deployment course explains how development changes can move safely from individual developer environments into testing, staging, and production environments. Participants learn how to structure dbt projects for collaborative development, establish controlled deployment processes, validate transformation changes before release, and reduce operational risks associated with deploying analytics code to production data platforms.
Participants gain practical knowledge of Git-based workflows, branching strategies, pull requests, merge processes, automated validation, and deployment pipelines. The training examines how CI/CD practices can connect source-code management with dbt execution so that changes to models, macros, tests, configurations, and project dependencies can be evaluated consistently before they become part of production data workflows.
The course also covers dbt environments, jobs, scheduling, commands, artifacts, logs, test execution, and deployment configuration. Learners explore how to separate development and production concerns, manage credentials and configuration securely, establish appropriate deployment triggers, and design repeatable workflows that support reliable data transformation operations across modern analytical platforms.
Advanced topics include state-aware execution, selective builds, deployment optimization, failure recovery, monitoring, release governance, and production troubleshooting. Current dbt documentation describes dbt State as a capability for avoiding unnecessary rebuilds when logic and data have not changed, while dbt Orchestrator can use state-aware orchestration to determine which models need to be built based on changes in code or data.
By completing this dbt CI/CD and Deployment Training Course, participants will be prepared to design and operate controlled deployment pipelines for analytics engineering teams. The course combines practical implementation with engineering standards, quality controls, automation, troubleshooting, governance, and operational best practices to help organizations deliver dependable dbt transformations efficiently and consistently.
Duration
5 days
Who Should Attend
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Analytics engineers responsible for developing and deploying dbt transformation projects.
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Data engineers managing automated ELT pipelines and production data transformation workflows.
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DevOps engineers supporting CI/CD infrastructure for modern analytics and data platforms.
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Data platform engineers responsible for reliable deployment and operational management of dbt projects.
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BI developers who need to understand controlled deployment of analytical transformations.
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SQL developers transitioning toward analytics engineering and automated data deployment practices.
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Data quality engineers integrating automated testing into dbt development and release workflows.
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Technical leads establishing development, testing, deployment, and governance standards for data teams.
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Cloud data engineers working with modern warehouses and production-oriented dbt environments.
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Data operations professionals responsible for monitoring, troubleshooting, and maintaining scheduled transformations.
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Engineering managers overseeing collaborative analytics development and production release processes.
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Data governance professionals interested in controlled, traceable, and auditable transformation deployments.
Course Objectives
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Develop a comprehensive understanding of CI/CD principles and how they can be applied to dbt analytics engineering and data transformation workflows.
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Configure structured development, testing, staging, and production environments that support controlled and repeatable dbt deployment processes.
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Apply Git-based branching, pull request, review, merge, and release practices to collaborative dbt development and transformation management.
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Build automated CI workflows that validate dbt model changes through compilation, testing, dependency analysis, and targeted transformation execution.
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Design continuous deployment processes that safely promote validated dbt changes into production environments while reducing manual operational intervention.
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Configure dbt jobs, schedules, commands, environment variables, credentials, and execution settings for dependable production transformation workflows.
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Implement state-aware and selective execution strategies that reduce unnecessary model rebuilding and improve deployment and operational efficiency.
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Establish monitoring, logging, troubleshooting, rollback, and incident-response practices for managing failed or unexpected production deployments.
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Integrate data quality testing, documentation, artifacts, and governance controls into CI/CD pipelines to improve release confidence and traceability.
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Design an enterprise-ready dbt deployment framework that supports scalability, collaboration, security, reliability, maintainability, and continuous improvement.
Comprehensive Course Outline
Module 1: dbt CI/CD Fundamentals and Deployment Architecture
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Understanding CI/CD principles and how software engineering delivery practices translate into modern analytics engineering and dbt workflows.
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Exploring the complete dbt development lifecycle from local development and source control through validation, deployment, scheduling, and production operations.
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Examining the roles of developers, reviewers, CI systems, deployment environments, orchestration tools, warehouses, and production data consumers.
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Designing an effective dbt deployment architecture that balances development flexibility, automated validation, operational reliability, security, and governance.
Module 2: Git, Version Control, and Collaborative dbt Development
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Establishing Git-based workflows for managing dbt models, macros, tests, configurations, documentation, packages, and other project components.
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Applying branching strategies that allow multiple analytics engineers to develop features, fixes, and transformations without disrupting production workloads.
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Using pull requests, code reviews, merge processes, and commit practices to improve quality, traceability, collaboration, and controlled change management.
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Managing conflicts, release branches, hotfixes, and development standards while maintaining a clean and reliable dbt project history.
Module 3: dbt Environments and Configuration Management
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Designing separate development, testing, staging, and production environments to support safe transformation development and controlled data delivery.
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Managing environment-specific configurations, profiles, targets, variables, schemas, database connections, and execution settings across deployment stages.
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Applying secure configuration practices for credentials, secrets, service accounts, environment variables, and warehouse access permissions.
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Establishing consistent environment management standards that reduce configuration drift and improve reliability between development and production systems.
Module 4: Continuous Integration and Automated Validation
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Designing CI workflows that automatically validate dbt changes when developers submit pull requests or modify transformation project code.
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Integrating compilation, dependency validation, model execution, data tests, linting, and other quality checks into automated CI processes.
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Selecting appropriate models and tests for efficient CI execution while maintaining sufficient coverage for high-risk and affected transformation logic.
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Establishing CI failure-handling practices that help developers quickly identify issues before changes are merged into production branches.
Module 5: Continuous Deployment and Release Management
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Designing deployment workflows that automatically promote validated dbt changes from controlled source repositories into production environments.
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Configuring deployment commands, job execution, scheduling, dependencies, environment settings, and production warehouse connections.
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Establishing release management practices for planned changes, urgent fixes, versioned releases, approvals, and controlled production transformations.
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Developing deployment gates and quality controls that prevent unvalidated, incomplete, or potentially damaging transformation changes from reaching production.
Module 6: dbt Jobs, Scheduling, and Orchestration
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Understanding dbt job concepts and configuring automated execution workflows for scheduled, event-driven, or operationally controlled transformation processes.
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Designing job sequences that coordinate model builds, tests, snapshots, documentation generation, and other required production transformation activities.
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Applying appropriate scheduling strategies based on data freshness, business requirements, upstream dependencies, warehouse capacity, and downstream reporting needs.
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Exploring state-aware orchestration concepts that can identify relevant changes and avoid unnecessary rebuilding of unaffected project components.
Module 7: State-Aware Deployment and Performance Optimization
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Understanding state comparison concepts and how deployment workflows can use project state to identify changes between environments or releases.
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Applying selective execution strategies to focus transformation processing on changed or affected models while maintaining appropriate dependency and quality coverage.
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Optimizing CI/CD execution time by reducing unnecessary builds, selecting relevant tests, and improving transformation workflow efficiency.
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Evaluating trade-offs between deployment speed, validation coverage, data freshness, warehouse resource consumption, and operational reliability.
Module 8: Testing, Artifacts, Monitoring, and Deployment Troubleshooting
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Integrating dbt data tests into deployment pipelines to detect transformation defects and data quality problems before and after production releases.
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Understanding logs, run results, artifacts, test failures, compilation errors, dependency problems, and warehouse execution issues during deployment operations.
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Developing systematic troubleshooting approaches for failed jobs, broken models, configuration errors, unexpected data changes, and production incidents.
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Establishing monitoring and alerting practices that help teams detect failed deployments, missed schedules, quality problems, and other operational exceptions.
Module 9: Production Governance, Security, and Reliability
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Establishing governance controls for production deployments, including approvals, access management, ownership, documentation, auditability, and change tracking.
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Applying least-privilege principles and secure configuration practices to protect production data platforms, credentials, transformation code, and deployment infrastructure.
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Designing reliable release processes with appropriate validation, failure handling, rollback planning, incident response, and recovery procedures.
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Integrating CI/CD practices with broader analytics engineering governance to create repeatable, transparent, and accountable production operations.
Module 10: Enterprise dbt CI/CD Capstone and Deployment Strategy
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Designing an end-to-end dbt CI/CD pipeline that integrates source control, development environments, automated testing, deployment jobs, monitoring, and production execution.
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Implementing a practical deployment scenario that moves transformation changes from development through validation and controlled production release.
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Troubleshooting realistic CI/CD failures involving Git workflows, SQL transformations, tests, configuration, dependencies, environment settings, and production execution.
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Developing an enterprise deployment roadmap covering automation, scalability, performance, security, governance, observability, and continuous improvement of dbt delivery practices.
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.
