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Course Outline
Introduction
- The philosophy and principles of dbt / Understanding what dbt is
- dbt compared to traditional ETL methods
- Overview of dbt features and architecture
- Exploring beyond dbt: Introduction to dbt Cloud
Understanding dbt Cloud
- The lifecycle of a dbt project within dbt Cloud
- How dbt Cloud integrates into 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
- Executing dbt commands in dbt Cloud
- Collaborating with team members on a dbt project in dbt Cloud
Working with dbt Models
- Understanding the concept of 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
- A solid grasp 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
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
