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 Duration 21 hours

Course Outline

Introduction

  • Core dbt philosophy and principles / An introduction to dbt
  • dbt compared to conventional ETL methodologies
  • A summary of dbt features and underlying architecture
  • Looking beyond the basics: An overview of dbt Cloud

Gaining Insight into dbt Cloud

  • The lifecycle of a dbt project when using dbt Cloud
  • How dbt Cloud integrates with data warehousing and transformation pipelines

Getting Started with dbt Cloud

  • Configuring the Development Environment on dbt Cloud
  • Establishing the connection between dbt Cloud and your data warehouse
  • Initiating a dbt project within dbt Cloud
  • Executing dbt commands via dbt Cloud
  • Facilitating team collaboration on a dbt project in dbt Cloud

Managing dbt Models

  • Comprehending the structure of dbt models
  • Constructing a dbt model
  • Performing data transformations using dbt
  • Utilizing incremental models within dbt
  • Developing macros and custom functions in dbt

Overseeing dbt Projects in dbt Cloud

  • Utilizing the dbt Cloud interface for project management and deployment
  • Defining schedules and initiating dbt jobs
  • Establishing and maintaining environments in dbt Cloud
  • Releasing dbt projects to production
  • Configuring notification and alert systems

Connecting dbt Cloud with External Tools

  • Integrating dbt Cloud with Git and version control systems
  • Linking dbt Cloud with other cloud-based data warehousing and transformation platforms

Diagnosis and Resolution

  • Strategies for debugging and troubleshooting dbt projects in dbt Cloud
  • Leveraging logs to identify and resolve issues
  • Best practices for sustaining dbt Cloud projects

Summary and Future Directions

Requirements

  • A solid grasp of data modeling concepts and SQL
  • Practical experience with SQL and command-line interfaces (CLI)
  • Proficiency in Python programming

Target Audience

  • Data Engineers
  • Data Analysts
  • Data Scientists

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