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 Duration 21 hours

Course Outline

Introduction

  • Foundational philosophy and core principles: Defining dbt
  • Comparative analysis: dbt versus traditional ETL methodologies
  • Architectural overview and key functional features of dbt
  • Expanding the scope: An introduction to dbt Cloud

Understanding dbt Cloud

  • The complete lifecycle of a dbt project within the dbt Cloud environment
  • Integration of dbt Cloud into data warehousing and transformation workflows for government

Getting Started with dbt Cloud

  • Configuring the development environment in dbt Cloud
  • Establishing secure connections between dbt Cloud and organizational data warehouses
  • Initiating and structuring a new dbt project in dbt Cloud
  • Executing dbt commands through the dbt Cloud interface
  • Fostering team collaboration on dbt projects within dbt Cloud

Working with dbt Models

  • Conceptual understanding of dbt data models
  • Constructing and defining a dbt model
  • Executing data transformations using dbt logic
  • Utilizing incremental models for efficient data processing
  • Developing macros and custom functions to enhance dbt capabilities

Managing dbt Projects in dbt Cloud

  • Leveraging the dbt Cloud interface for project management and deployment
  • Configuring automated schedules and triggering dbt job executions
  • Establishing and managing distinct operational environments in dbt Cloud
  • Deploying dbt projects to production environments for government use
  • Implementing notification systems and alert mechanisms for monitoring

Integrating dbt Cloud with Other Tools

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

Troubleshooting and Debugging

  • Methodologies for debugging and resolving issues in dbt Cloud projects
  • Utilizing log analysis to diagnose operational anomalies
  • Best practices for maintaining the integrity of dbt Cloud projects

Summary and Next Steps

Requirements

  • Foundational knowledge of data modeling 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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