Introduction
I'm preparing for the DBT exam and I used the "dbt Analytics Engineering Certification Exam Study Guide" for searching for more information about the topics covered in the exam. I stored the links that I used in this blogpost, just helping you too. I hope you enjoy it.
I'll frequently update this blogpost. I just started with a couple of topics.
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
- Business logic in DBT
- Refactoring legacy SQL to dbt
- How dbt Can Help Solve 4 Common Data Engineering Pain Points
- Using commands such as run, test, docs and seed
- Creating a logical flow of models and building clean DAGs
- Data modeling techniques for more modularity
- How we structure our dbt projects
- dbt DAG: Definition, Usage, and Examples
- Defining configurations in dbt_project.yml
- Configuring sources in dbt
- Using dbt Packages
Topic 2: Debugging data modeling errors
- Understanding logged error messages
- Troubleshooting using compiled code
- Troubleshooting .yml compilation errors
- Distinguishing between a pure SQL and a dbt issue that presents itself as a SQL issue
- Developing and implementing a fix and testing it prior to merging
Topic 3: Monitoring data pipelines
- Understanding and testing the warehouse-level implications of a model run failing at different points in the DAG
- Understanding the general landscape of tooling
Topic 4: Implementing dbt tests
- Using generic, singular and custom tests on a wide variety of models and sources
- Understanding assumptions specific to the datasets being generated in models and to the raw data in the warehouse
- Implementing various testing steps in the workflow.
- Ensuring data is being piped into the warehouse and validating accuracy against baselines
Topic 5: Deploying dbt jobs
- Understanding the differences between deployment and development environments
- Configuring development and deployment environments
- Configuring the appropriate tasks, settings and triggers for the job
- Understanding how a dbt job utilizes an environment in order to build database objects and artifacts
- Using dbt commands to execute specific models
Topic 6: Creating and Maintaining dbt documentation
- Updating dbt docs
- Implementing source, table, and column descriptions in .yml files
- Using dbt commands to generate a documentation site
- Using macros to show model and data lineage on the DAG
Topic 7: Promoting code through version control
- Understanding concepts and working with Git branches and functionalities
- Creating clean commits and pull requests
- Merging code to the main branch
Topic 8: Establishing environments in data warehouse for dbt
- Understanding environment’s connections
- Understanding the differences between production data, development data, and raw data
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