Course Outline

Introduction

  • dbt philosophy and principles / What is dbt?
  • dbt vs traditional ETL
  • Overview of dbt features and architecture
  • Beyond dbt: What is dbt Cloud?

Understanding dbt Cloud

  • The lifecycle of a dbt project in dbt Cloud
  • How dbt Cloud fits in with data warehousing and transformation workflows

Getting Started with dbt Cloud

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

Working with dbt Models

  • Understanding dbt models
  • Building a dbt model
  • Transforming data using dbt
  • Working with incremental models in dbt
  • Implementing macros and custom functions in dbt

Managing dbt Projects in dbt Cloud

  • Using the dbt Cloud interface to manage and deploy projects
  • Creating schedules and triggering dbt jobs
  • Creating and managing environments in dbt Cloud
  • Deploying dbt projects to production
  • Setting up 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

  • How to debug and troubleshoot dbt projects in dbt Cloud
  • Using logs to diagnose issues
  • Best practices for maintaining dbt Cloud projects

Summary and Next Steps

Requirements

  • An understanding of data modeling and SQL
  • Experience with SQL and command-line interface (CLI)
  • Python programming experience

Audience

  • Data Engineers
  • Data Analysts
  • Data Scientists
  21 Hours
 

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Dates are subject to availability and take place between 9:30 am and 4:30 pm.
Open Training Courses require 5+ participants.

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