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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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Equipped with examples