The Analytics Engineering Certification exam of dbt validates your ability to design, build, and manage scalable data models, implement transformation workflows and apply governance, certifying you as a dbt Analytics Engineer. 🧑🎓
The exam covers essential skills and key components in data modeling using dbt. The key skills required for the Analytics Engineering Certification exam include:
- Topic 1: Developing dbt models
- Topic 2: Understanding dbt models governance
- Topic 3: Debugging data modeling errors
- Topic 4: Managing data pipelines
- Topic 5: Implementing dbt tests
- Topic 6: Creating and Maintaining dbt documentation
- Topic 7: Implementing and maintaining external dependencies
- Topic 8: Leveraging the dbt state
This guide provides a comprehensive overview of the key skills covered in the dbt Analytics Engineering Certification exam. We provide step-by-step tutorials to each exam topic, helping you to prepare for the exam and master the skills needed to become a certified dbt Analytics Engineer. 🚀
Topic 1: Developing dbt models
- Identifying and verifying any raw object dependencies
- Understanding core dbt materializations
- Conceptualizing modularity and how to incorporate DRY principles
- Converting business logic into performant SQL queries
- Using commands such as
run, test, docsandseed - Creating a logical flow of models and building clean DAGs
- Defining configurations in
dbt_project.yml - Configuring sources in dbt
- Using dbt Packages
- Utilizing git functionality within the development lifecycle
- Creating Python Models
- Providing access to users to models with the “grants” config
Topic 2: Understanding dbt models governance
- Adding contracts to models to ensure the shape of models
- Creating different versions of our models and deprecating the old ones
- Configuring Model Access
Topic 3: Debugging data modeling errors
- Understanding logged error messages
- Troubleshooting using compiled code
- Troubleshooting .yml compilation errors
- Distinguishing between dbt core or data platform error responses
- Developing and implementing a fix and testing it prior to merging
Topic 4: Managing data pipelines
- Troubleshooting and managing failure points in the DAG
- Using dbt clone
- Troubleshooting errors from integrated tools
Topic 5: Implementing dbt tests
- Using generic, singular, custom, and custom generic tests on a wide variety of models and sources
- Testing assumptions for dbt models and sources
- Implementing various testing steps in the workflow
Topic 6: Creating and Maintaining dbt documentation
- Updating dbt docs
- Implementing source, table, and column descriptions in yml files
- Using macros to show model and data lineage on the DAG
Topic 7: Implementing and maintaining external dependencies
- Implementing dbt exposures
- Implementing source freshness
Topic 8: Leveraging the dbt state
- Understanding state
- Using dbt retry
- Combining state and result selectors
Further Study Resources
dbt - Deep Learning Nerds | The ultimate Learning Platform for AI and Data Science
This page contains dbt (Data Build Tool) tutorials. dbt is a powerful open-source technology that enables teams to transform raw data into valuable insights through SQL-based workflows. Here you will find hands-on tutorials and best practices for using dbt effectively. Our dbt tutorials also serve as a resource for anyone preparing for dbt Analytics Engineering Certification Exam.
Data Engineer
dbt Analytics Engineering Certification Exam | dbt Labs
dbt is a data transformation tool that enables data analysts and engineers to transform, test and document data in the cloud data warehouse.
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