Description

Introduction

dbt (data build tool) is a modern analytics engineering framework. It transforms raw data into structured datasets in a warehouse. In addition, it uses SQL-based transformations.It follows software engineering practices such as version control, modular design, testing, and documentation. Moreover, it supports CI/CD workflows.

dbt works with Snowflake, BigQuery, and Redshift. Therefore, it improves scalability and reliability in analytics.

Learner Prerequisites

• Participants should have basic SQL knowledge.
• They should understand relational databases.
• In addition, they should know data warehousing basics.
• Familiarity with cloud platforms is recommended.
• Basic ETL or ELT understanding is helpful.
• However, Git knowledge is optional but useful.

Table of Contents

1. Introduction to dbt and Analytics Engineering

1.1 Overview of analytics engineering evolution
1.2 Introduction to dbt and its purpose
1.3 Core components of dbt ecosystem
1.4 Overview of modern data stack
1.5 Role of dbt in ELT workflows

2. dbt Architecture and Project Structure

2.1 Setting up dbt project structure
2.2 Understanding folder organization
2.3 Models, sources, seeds, and snapshots
2.4 Types of materializations
2.5 Managing dependencies using ref()

3. Setting Up dbt Environment

3.1 Installing dbt Core and dbt Cloud
3.2 Configuring profiles and credentials
3.3 Connecting dbt with data warehouses
3.4 Managing development, test, and production environments
3.5 Running a first dbt project

4. Building dbt Models

4.1 Writing SQL-based dbt models
4.2 Designing staging layers
4.3 Building intermediate layers
4.4 Creating mart layers
4.5 Using incremental models for optimization

5. Testing and Data Quality in dbt

5.1 Built-in dbt tests
5.2 Creating custom tests
5.3 Data validation strategies
5.4 Handling data quality issues
5.5 Improving test coverage

6. dbt Documentation and Lineage

6.1 Auto-generated documentation
6.2 Understanding lineage graphs
6.3 Adding model metadata
6.4 Sharing documentation
6.5 Data governance practices

7. Advanced dbt Features

7.1 Macros for reusable logic
7.2 Jinja templating in dbt
7.3 Snapshots for historical tracking
7.4 dbt packages and reuse
7.5 Advanced configuration techniques

8. dbt Deployment and Orchestration

8.1 dbt Core vs dbt Cloud comparison
8.2 Scheduling dbt jobs
8.3 CI/CD integration using Git
8.4 Production deployment strategies
8.5 Monitoring and logging dbt runs

Conclusion

This training builds strong dbt analytics engineering skills. First, it covers fundamentals. Then, it moves into advanced concepts.Moreover, learners gain practical modeling experience. Therefore, they can build scalable data pipelines. In addition, they can test and deploy dbt workflows confidently.