Description

Introduction

dbt Core is an open-source analytics engineering framework used to transform raw warehouse data into clean, structured, and tested datasets. Moreover, it supports modular SQL development, version control, and automated documentation.

As a result, teams can build reliable and maintainable data pipelines. Therefore, dbt is widely used in modern data engineering workflows.

Learner Prerequisites

• Basic understanding of SQL, including SELECT, JOIN, and GROUP BY
• Familiarity with data warehouse concepts such as tables, views, and schemas
• Awareness of ETL or ELT data pipeline workflows
• Basic knowledge of Git and version control (recommended, not mandatory)
• Exposure to reporting or BI tools for better understanding of analytics flow

Table of Contents

1. Introduction to dbt Core

1.1 Understanding dbt Core and its role in analytics engineering
1.2 Key features such as modular SQL, testing, and documentation
1.3 dbt workflow including development, build, and deployment stages
1.4 Benefits of using dbt in modern data stacks
1.5 Overview of dbt ecosystem and tool integrations

2. dbt Project Structure and Setup

2.1 Initializing a dbt project and setting up the environment
2.2 Understanding core directories like models, macros, tests, and seeds
2.3 Configuring profiles.yml and managing database connections
2.4 Managing environments such as development, testing, and production
2.5 Following best practices for organizing dbt projects

3. Data Modeling in dbt

3.1 Designing staging, intermediate, and mart layers
3.2 Building modular and reusable data models
3.3 Using consistent naming conventions and standards
3.4 Applying SQL best practices for efficient transformations
3.5 Managing dependencies between models effectively

4. Sources and Seeds in dbt

4.1 Defining and configuring data sources in dbt
4.2 Using source freshness checks to ensure data reliability
4.3 Loading static datasets using seeds
4.4 Referencing sources and seeds within models
4.5 Validating and maintaining source data quality

5. dbt Models and Materializations

5.1 Understanding dbt models and their structure
5.2 Exploring materialization types such as view, table, incremental, and ephemeral
5.3 Choosing the right materialization strategy for performance
5.4 Optimizing performance for large datasets
5.5 Improving build efficiency and reducing costs

6. Testing and Data Quality Assurance

6.1 Using built-in schema tests such as unique, not null, and relationships
6.2 Writing custom SQL-based data tests
6.3 Applying test-driven development for data pipelines
6.4 Automating test execution in dbt workflows
6.5 Monitoring test results and handling failures

7. Macros and Jinja in dbt

7.1 Understanding Jinja templating in dbt
7.2 Creating reusable macros for SQL automation
7.3 Using variables and control structures in Jinja templates
7.4 Building advanced macros for dynamic SQL generation
7.5 Improving code maintainability using macro libraries

8. Documentation and Data Lineage

8.1 Generating dbt documentation site
8.2 Visualizing data lineage and model dependencies
8.3 Adding descriptions and metadata to models
8.4 Maintaining structured data catalog standards
8.5 Improving collaboration through clear documentation

9. Deployment, Scheduling, and CI/CD

9.1 Running dbt in production environments effectively
9.2 Scheduling dbt jobs using orchestration tools
9.3 Implementing CI/CD pipelines for dbt projects
9.4 Using Git-based workflows for collaboration
9.5 Monitoring and maintaining production deployments

Conclusion

This training provides a structured and practical understanding of dbt Core. In addition, it helps learners build strong skills in designing, developing, and managing data transformation pipelines.

As a result, participants can apply best practices in testing, documentation, and deployment. Therefore, they can build reliable and scalable analytics solutions using dbt.