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 Duration 21 hours

Course Outline

Introduction

  • dbt philosophy and core principles / What is dbt?
  • dbt compared to traditional ETL
  • Overview of dbt features and architecture
  • Beyond dbt: An introduction to dbt Cloud

Comprehending dbt Cloud

  • The lifecycle of a dbt project within dbt Cloud
  • The role of dbt Cloud in data warehousing and transformation workflows

Getting Started with dbt Cloud

  • Configuring the Development Environment on dbt Cloud
  • Linking dbt Cloud to your data warehouse
  • Initiating a dbt project in dbt Cloud
  • Executing dbt commands via dbt Cloud
  • Collaborating with team members on a dbt project in dbt Cloud

Managing dbt Models

  • Concepts behind dbt models
  • Constructing a dbt model
  • Transforming data utilizing dbt
  • Utilizing incremental models in dbt
  • Applying macros and custom functions in dbt

Administering dbt Projects in dbt Cloud

  • Leveraging the dbt Cloud interface for project management and deployment
  • Establishing schedules and initiating dbt jobs
  • Creating and managing environments in dbt Cloud
  • Releasing dbt projects to production
  • Configuring notifications and alerts

Integrating dbt Cloud with External Tools

  • Using dbt Cloud alongside Git and version control systems
  • Connecting dbt Cloud with other cloud-based data warehousing and transformation platforms

Troubleshooting and Debugging

  • Strategies for debugging and troubleshooting dbt projects in dbt Cloud
  • Diagnostics using logs
  • Best practices for maintaining dbt Cloud projects

Conclusion and Next Steps

Requirements

  • Familiarity with data modeling and SQL
  • Proficiency in SQL and command-line interfaces (CLI)
  • Background in Python programming

Target Audience

  • Data Engineers
  • Data Analysts
  • Data Scientists

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