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Course Description
Master dbt Workflows
Take your dbt skills to the next level and learn how to implement robust, scalable data transformation workflows in a production environment. This course is designed for data engineers, analysts, and analytics engineers who want to move beyond the basics and gain hands-on experience with advanced dbt functionality.
Ensure Data Quality with Advanced Testing
Building reliable data pipelines starts with validation. You'll explore advanced testing techniques to catch data inconsistencies, create custom reusable tests to standardize validation across models, and apply tests to sources and seeds for better governance and data lineage tracking.
Leverage dbt Sources, Seeds, and Snapshots
Discover how dbt sources can improve documentation and lineage while ensuring traceability of raw data. Learn to use dbt seeds for managing small, static datasets efficiently. Then, master slowly changing dimensions (SCD2) with dbt snapshots, allowing you to track historical changes in your data warehouse with minimal effort.
Automate and Optimize with dbt Build
Efficiency is key in production environments. You’ll learn how to streamline workflows with dbt build, automating model execution, tests, and snapshots to ensure reliable transformations. By optimizing your pipeline, you'll enhance performance, maintainability, and scalability of your dbt projects.
Apply Your Skills in Real-World Scenarios
Through interactive exercises and hands-on practice, you’ll reinforce your knowledge and gain the confidence to apply dbt in real-world settings. By the end of the course, you'll be equipped to design, test, and automate production-ready dbt workflows, ensuring high-quality and well-documented transformations at scale.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.- 1
Testing & Documentation
FreeLearn how to ensure data quality with advanced testing techniques in dbt. Explore built-in, singular, and reusable tests to validate models, sources, and seeds. Understand how to define custom tests using Jinja, troubleshoot failures, and optimize your validation workflow to catch inconsistencies before they impact downstream analysis.
Introduction to testing in dbt50 xpBuilt-in tests100 xpDefining tests on a model100 xpFinding bad data100 xpCreating singular tests50 xpSteps to develop a singular test100 xpVerifying trip duration100 xpVerifying test queries100 xpCreating custom reusable tests50 xpTesting, testing, testing100 xpImplementing a reusable test100 xpUpdating from singular to reusable test100 xp - 2
Implementing dbt in production
Take your dbt skills to the next level by implementing scalable, production-ready workflows. Learn how to use dbt sources and seeds to improve data lineage, implement snapshots for tracking historical changes, and automate your transformations with dbt build. By the end, you’ll be equipped to manage large-scale data pipelines with confidence.
dbt sources50 xpModels, sources, or both?100 xpAdding a source100 xpdbt seeds50 xpKernels of truth50 xpZIP is the code100 xpSCD2 with dbt snapshots old50 xpSnapshot process100 xpSnapshot issue50 xpAdding a snapshot100 xpAutomating with dbt build50 xpWhat can't dbt build do?100 xpHelping the intern!100 xpPutting it all together100 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.collaborators

prerequisites
Introduction to dbt
Data Engineer Consultant @ Flexible Creations
Mike is a consultant focusing on data engineering and analysis using SQL, Python, and Apache Spark among other technologies. He has a 20+ year history of working with various technologies in the data, networking, and security space.
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