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Course Outline

Overview

  • Core concepts and foundational principles / Definition of dbt
  • Comparison between dbt and conventional ETL processes
  • Functional capabilities and architectural framework of dbt
  • dbt Cloud: Extended capabilities beyond the core compiler

Dbt Cloud Architecture and Functionality

  • Project lifecycle management within dbt Cloud
  • Integration with data warehousing and transformation workflows for government systems

Initial Configuration of dbt Cloud

  • Establishing the development environment on dbt Cloud
  • Connecting dbt Cloud to authorized data warehouses
  • Initiating a new dbt project within the platform
  • Executing dbt commands through the dbt Cloud interface
  • Facilitating team collaboration on dbt projects for government operations

Development of dbt Models

  • Definition and purpose of dbt models
  • Construction of dbt models
  • Data transformation methodologies using dbt
  • Implementation of incremental models in dbt
  • Deployment of macros and custom functions within dbt

Administration of dbt Projects in dbt Cloud

  • Utilizing the dbt Cloud interface for project management and deployment
  • Scheduling tasks and initiating dbt jobs
  • Creation and administration of environments in dbt Cloud
  • Deployment of dbt projects to production-ready states
  • Configuration of notifications and alert systems

System Integration with dbt Cloud

  • Leveraging Git and version control with dbt Cloud
  • Integration of dbt Cloud with additional cloud-based data warehousing and transformation solutions

Issue Resolution and Maintenance

  • Procedures for debugging and troubleshooting dbt projects in dbt Cloud
  • Analysis of logs to identify system anomalies
  • Operational best practices for sustaining dbt Cloud projects

Conclusion and Future Actions

Requirements

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

Target Audience

  • Data Engineers
  • Data Analysts
  • Data Scientists
 21 Hours

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