Introduction to dbt
This course introduces dbt for data modeling, transformations, testing, and building documentation.
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Course Description
dbt, or the data build tool, has taken the data world by storm. This course introduces you to the concepts, terminology, and methods of using dbt to implement an example data warehouse. You'll gain an understanding of what dbt is, when it should be used, and best practices when implementing data warehousing. You will work with real datasets and perform extract, load, and transform operations as implemented in data analyst, data engineering, and analytics engineering roles.
Learners will develop the skills to define a data warehouse from scratch, model and transform data, and build tests and documentation! This course will provide you with a solid foundation to build upon in your dbt journey, regardless of the type of data warehouse you intend to implement. Gain confidence about how and when to use dbt by working through exercises using SQL and shell commands.
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Professional Data Engineer in Python
Go To Track- 1
Welcome to dbt
FreeLearn about the reasons behind dbt's existence and how it's used to create data transformation projects.
- 2
dbt models
Get into the true substance of dbt by working with data models and creating SQL based models within dbt. Learn about basic SQL models, define dependencies, update, and troubleshoot various model issues.
What is a dbt model?50 xpFeatures of a data model100 xpdbt model statements50 xpCreating a dbt model100 xpUpdating dbt models50 xpConfig files100 xpUpdating a dbt model100 xpHierarchical models in dbt50 xpNo hierarchy model creation100 xpHierarchical model creation100 xpUpdating model hierarchies100 xpModel troubleshooting50 xpError classification100 xpProcess of troubleshooting100 xpTroubleshooting model errors100 xp - 3
Testing & Documentation
Learn to take advantage of data validation in dbt using tests. Apply default tests to various models and properties, then create custom tests to handle specialized logic validation. Learn to document various details of dbt models and generate lineage information automatically.
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 xpCreating and generating dbt documentation50 xpdbt docs Command Options50 xpdbt documentation flow100 xpCreating dbt documentation100 xp - 4
Implementing dbt in production
Utilize what you've learned about dbt with some added details to implement dbt in a production environment. We'll cover dbt seeds and snapshots, along with how to automate builds. Finally review what you've learned throughout the course and implement a dbt pipeline.
dbt sources50 xpOrderly YML100 xpModels, sources, or both?100 xpAdding a source100 xpdbt seeds50 xpKernels of truth50 xpZIP is the code100 xpSCD2 with dbt snapshots50 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 xpCourse review50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Professional Data Engineer in Python
Go To Trackcollaborators
Mike MetzgerData 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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