This course helps you build a strong foundation in analytics engineering and gives you the practical skills needed to work with modern data systems. You will begin by learning the core components of the modern data stack and the responsibilities of analytics engineers. From there, you will move into analytical SQL, dimensional modeling concepts, and the structure of ELT pipelines. The course concludes with hands-on development in dbt Core, where you will create, test, and document high-quality data models.
Introduction to Analytics Engineering
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
9 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Write analytical SQL queries to prepare, explore, and analyze data effectively.
Design facts, dimensions, and star schemas to structure data for accurate and efficient analysis.
Build organized raw, staging, and mart layers to support reliable and scalable data transformations.
Create, test, and document dbt models to automate transformations and ensure data quality and transparency.
Skills you'll gain
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Recently updated!
December 2025
Taught in English
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There are 3 modules in this course
This module introduces analytics engineering and the modern data stack. It explains ELT vs. ETL, essential analytical SQL skills, and core warehousing concepts. Learners work with PostgreSQL and dbt Docs to understand how modern data pipelines are structured.
What's included
13 videos6 readings4 assignments3 discussion prompts
This module covers dimensional modeling and how ELT pipelines are organized across raw, staging, and mart layers. It introduces dbt Core, its project structure, and how it streamlines SQL transformations in modern analytics environments.
What's included
15 videos4 readings4 assignments2 discussion prompts
This module explores building dbt models using sources, refs, and layered transformations. Learners practice using materializations and seeds, and implement testing and documentation to improve data quality and model transparency.
What's included
14 videos5 readings5 assignments3 discussion prompts
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Frequently asked questions
dbt, or data build tool, is a transformation framework used in analytics that applies software engineering practices such as version control, testing, and modular development. It enables analysts and engineers to use simple SQL SELECT statements to transform raw data inside a data warehouse, helping create faster, more reliable, and well-structured data pipelines.
dbt is primarily focused on the T in ELT, meaning it handles the transformation step inside the data warehouse. It allows data engineers and analysts to define tests and validation rules within dbt models, which helps ensure data quality during transformation. Using dbt, teams can verify completeness, accuracy, and consistency of data, making the overall ELT process more reliable and well-governed.
dbt is primarily SQL based, since its core purpose is to manage and run SQL transformations inside a data warehouse. It does not natively support non-SQL transformations. However, dbt is flexible enough to work alongside external tools, and teams can incorporate custom scripts when more advanced processing is required.
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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.