DBT
dbt
Create effective data management workflows with dbt
✓ Official training SFEIR InstituteLevel Fundamentals⏱️ 2 days (14h)
What you will learn
- Understand the key concepts, advantages, and architecture of dbt as a data transformation and modeling tool.
- Create structured data models with dbt, and perform transformations to process and prepare data for analysis.
- Master advanced features such as macros, Jinja models, variables, and flow control.
- Use dbt snapshots to track changes over time and manage historical data, facilitating the analysis of historical trends and slowly changing dimensions.
- Implement tests to ensure data quality and integrity, allowing for the validation of transformation results and detection of anomalies.
Prerequisites
- Practical SQL knowledge equivalent to the SQL fundamentals course
Target audience
- Data Analysts, Data Engineers, Anyone interested in data transformation
Training Program
13 modules to master the fundamentals
Topics covered
- →Evolution of the data stack
- →Understanding the differences between Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) data integration approaches
- →Introduction to the modern data stack
Topics covered
- →Overview of dbt
- →Installing dbt and configuring the development environment
- →Creating a dbt project
- →Connecting to data sources
Activities
Setting up a dbt project
Topics covered
- →Understanding dbt models
- →How do dbt models work?
- →Materialization options
- →Configuring materialization
- →Introduction to the tagging feature for metadata organization
Activities
Creating data models with dbt
Topics covered
- →Introduction to dbt sources
- →Configuring dbt sources
- →Working with dbt references
Activities
Configuring dbt sources, referencing external data, and managing model dependencies
Topics covered
- →Introduction to dbt seeds
- →Creating and populating seed data
- →Advantages of using seeds for data initialization
- →Integrating seeds into your dbt models
Activities
Creating and integrating seeds into your dbt projects
Topics covered
- →Understanding snapshots in dbt
- →Configuring and defining snapshots
- →Running and managing snapshots
Activities
Implementing a snapshot strategy
Topics covered
- →Understanding macros
- →Jinja, a templating language
- →Using variables to manage data pipeline configuration
Activities
Advanced data transformation and control
Topics covered
- →Introduction to dbt packages
- →Exploring the dbt hub
- →Installing and using a dbt package
Activities
Exploring dbt packages
Topics covered
- →Highlighting potential risks in the code
- →Setting up automated tests
- →Choosing the appropriate test
- →Implementing data tests
Activities
Implementing data tests
Topics covered
- →Documenting data models
- →Using dbt's built-in documentation features to generate and maintain accessible and up-to-date model documentation
- →The importance of lineage
Activities
Documenting DBT models
Topics covered
- →Performing data analysis
- →Running custom code before and after dbt execution
- →Creating shareable and accessible data assets
Activities
Creating an exposure
Topics covered
- →Understanding manifest.json
- →Introduction to run_result.json
Topics covered
- →Resources on dbt best practices
- →About the DBT certification exam
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote