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Chapter 1: Introduction to dbt (Data Build Tool)

Chapter 2: Installing and Setting Up dbt

Chapter 3: Understanding dbt Core Concepts

Chapter 4: Creating and Managing dbt Models

Chapter 5: SQL-Based Transformations in dbt

Chapter 6: dbt Project Structure and Best Practices

Chapter 7: Working with Seeds, Snapshots, and Sources

Chapter 8: Incremental Models and Performance Optimization

Chapter 9: Testing and Validating Data in dbt

Chapter 10: Debugging and Logging in dbt

Chapter 11: Using Macros and Jinja for Advanced SQL

Chapter 12: dbt Packages and Modular Development

Chapter 13: Version Control and Collaboration with dbt

Chapter 14: dbt Cloud vs dbt Core: Key Differences

Chapter 15: Configuring dbt for Different Data Warehouses (BigQuery, Snowflake, Redshift, etc.)

Chapter 16: Data Documentation and Governance in dbt

Chapter 17: Scheduling and Automating dbt Runs

Chapter 18: CI/CD Workflows with dbt

Chapter 19: Handling Dependencies and dbt Artifacts

Chapter 20: Monitoring and Observability in dbt

Chapter 21: Scaling dbt for Large Data Projects

Chapter 22: Security and Access Control in dbt

Chapter 23: Real-World Use Cases and Case Studies

Chapter 24: Common Challenges and Troubleshooting in dbt

Chapter 25: Future Trends and Innovations in dbt

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