This course offers a comprehensive introduction to data build tools (dbt), a powerful data transformation tool that streamlines the development and management of data pipelines. You will learn to set up and configure dbt projects, create and manage models, work with data sources, and leverage advanced features like data testing, targets, and snapshots.
The course covers various topics, from the basics of dbt to more advanced concepts. You will gain hands-on experience through coding challenges and quizzes, reinforcing your understanding of the material. By the end of the course, you will be well-equipped to build efficient and maintainable data pipelines using dbt.
Whether you are a data engineer, analyst, or data scientist, this course will equip you with the skills and knowledge to leverage dbt to its full potential. You will learn to improve data quality, automate data workflows, and collaborate effectively within your team.
This course offers a comprehensive introduction to data build tools (dbt), a powerful data transformation tool that streamlines ...Show More
WHAT YOU'LL LEARN
Working knowledge of dbt fundamentals and project structure
Hands-on experience building effective dbt project architectures
An understanding of advanced dbt features for data quality and automation
Familiarity with external data sources and loading data to dbt projects
Hands-on experience creating and using dbt packages for reusability and collaboration
Working knowledge of dbt fundamentals and project structure
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TAKEAWAY SKILLS
Content
1.
Introduction to dbt and Its Setup
4 Lessons
Get familiar with dbt's role in data transformation, connecting with BigQuery, and project setup.
2.
Overview of the Project Architecture
7 Lessons
Look at configuring profiles, folder paths, model settings, and accessing compiled code in dbt projects.
3.
Models
9 Lessons
Go hands-on with creating, referencing, and organizing SQL models, configurations, schemas, and Python models in dbt.
4.
Sources
5 Lessons
Grasp the fundamentals of setting up and referencing sources, using seeds, and querying datasets.
5.
Materializations
8 Lessons
Map out the steps for materializing views, tables, incremental models, and ephemeral models in dbt.
6.
Dynamic Models with Jinja
11 Lessons
Focus on leveraging Jinja in dbt for dynamic models with variables, loops, and macros.
7.
Packages
7 Lessons
Build on the use and creation of dbt packages to streamline and enhance projects.
8.
Advanced dbt Concepts
12 Lessons
Sharpen your skills in advanced dbt concepts for data validation, environment management, and documentation.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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