Description

Introduction

dbt (Data Build Tool) is an open-source analytics engineering tool that helps teams transform raw data into structured datasets using SQL. It also introduces software engineering practices into analytics workflows. For example, it supports modular development, version control, testing, and documentation.

In addition, dbt integrates with cloud platforms such as Snowflake, BigQuery, Redshift, and Databricks. As a result, it enables scalable and maintainable data transformation pipelines. Therefore, teams can manage data workflows more efficiently and consistently.

Learner Prerequisites

• Basic knowledge of SQL (SELECT, JOIN, GROUP BY, CTEs)
• Understanding of data warehousing concepts
• Familiarity with analytics or BI tools
• Knowledge of relational databases
• Exposure to Git and version control (optional)
• Awareness of cloud platforms like Snowflake, BigQuery, or Redshift (optional)

Table of Contents

1. dbt Fundamentals and Core Concepts

1.1 Introduction to dbt and its architecture
1.2 Overview of dbt project structure
1.3 Understanding the dbt workflow: Develop, Test, Document, Deploy
1.4 Models, sources, and targets explained
1.5 Difference between dbt Core and dbt Cloud

2. Setting Up dbt Environment

2.1 Installing dbt Core step by step
2.2 Configuring dbt profiles
2.3 Connecting dbt to data warehouses
2.4 Creating your first dbt project
2.5 Running dbt commands: run, debug, seed, test

3. Data Modeling in dbt

3.1 Staging models and raw data preparation
3.2 Intermediate transformation models
3.3 Fact and dimension modeling
3.4 Incremental models
3.5 dbt materializations

4. Advanced dbt Modeling Techniques

4.1 Snapshots for historical tracking
4.2 Jinja templating and macros
4.3 Reusable and modular models
4.4 Advanced use of ref and source
4.5 Complex transformation patterns

5. Testing and Data Quality Management

5.1 Built-in dbt tests: unique, not null, relationships
5.2 Custom data tests creation
5.3 Data validation strategies
5.4 Source freshness testing
5.5 Debugging failed tests

6. Documentation and Lineage

6.1 Generating dbt documentation
6.2 Data lineage visualization
6.3 Writing model descriptions
6.4 Documentation best practices
6.5 Publishing dbt docs

7. dbt Deployment and Orchestration

7.1 Deployment strategies in dbt Cloud
7.2 Scheduling dbt jobs
7.3 CI/CD integration
7.4 Managing dev, test, and prod environments
7.5 Monitoring dbt runs

8. dbt with Data Warehouses

8.1 Using dbt with Snowflake
8.2 Using dbt with BigQuery
8.3 Using dbt with Redshift
8.4 Query performance optimization
8.5 Cost management

9. Advanced Analytics Engineering Practices

9.1 Data modeling best practices
9.2 Performance tuning techniques
9.3 Handling large datasets
9.4 Governance and access control
9.5 Data reliability frameworks

10. Real-World Project Implementation

10.1 End-to-end dbt workflow
10.2 Sales analytics pipeline
10.3 Customer analytics use case
10.4 Production error handling
10.5 Final deployment strategy

Conclusion

In conclusion, this dbt training program covers both fundamental and advanced concepts. It begins with core dbt principles and gradually moves into modeling, testing, and deployment. In addition, learners gain practical exposure to real-world scenarios.

As a result, participants develop the ability to build scalable and reliable data pipelines. Ultimately, they can apply dbt effectively in modern analytics environments.