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Course Outline

Introduction

  • The philosophy and core principles of dbt / What is dbt?
  • dbt compared to traditional ETL
  • An overview of dbt features and architecture
  • Beyond the basics: What is dbt Cloud?

Understanding 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

  • Setting up the Development Environment in dbt Cloud
  • Connecting dbt Cloud to your data warehouse
  • Creating a dbt project in dbt Cloud
  • Executing dbt commands in dbt Cloud
  • Collaborating with team members on a dbt project in dbt Cloud

Working with dbt Models

  • Understanding the structure of dbt models
  • Constructing a dbt model
  • Transforming data using dbt
  • Utilizing incremental models in dbt
  • Implementing macros and custom functions in dbt

Managing dbt Projects in dbt Cloud

  • Utilizing the dbt Cloud interface to manage and deploy projects
  • Creating schedules and triggering dbt jobs
  • Establishing and managing environments in dbt Cloud
  • Deploying dbt projects to production
  • Configuring notifications and alerts

Integrating dbt Cloud with Other Tools

  • Using dbt Cloud with Git and version control
  • Integrating dbt Cloud with other cloud-based data warehousing and transformation tools

Troubleshooting and Debugging

  • Techniques for debugging and troubleshooting dbt projects in dbt Cloud
  • Utilizing logs to diagnose issues
  • Best practices for maintaining dbt Cloud projects

Summary and Next Steps

Requirements

  • A solid grasp of data modeling and SQL
  • Proficiency with SQL and the command-line interface (CLI)
  • Familiarity with Python programming

Target Audience

  • Data Engineers
  • Data Analysts
  • Data Scientists
 21 Hours

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