Will I get hands-on practice with real tools?

<text variant="body1">Yes. You will complete guided exercises using PostgreSQL for SQL practice and dbt Core for building data models and transformations. You will work with synthetic datasets, build staging and mart models, configure sources and refs, run dbt models, and validate data quality using built-in and custom tests.

What career paths can this course help me prepare for?

<text variant="body1">This course supports roles such as Analytics Engineer, BI Developer, Data Analyst, Junior Data Engineer, Reporting Analyst, and ETL Developer. The skills taught in this course are also relevant for data modeling, metrics design, and analytical workflow management.

Do I need programming or prior analytics experience to enroll?

<text variant="body1">No. This course does not require programming or prior data engineering experience. All concepts, tools, and exercises are explained step by step. Familiarity with SQL is helpful but not required. Anyone comfortable with basic data work can follow the course successfully.

Is this course suitable for beginners?

<text variant="body1">Yes. The course is beginner friendly and introduces concepts gradually. Clear examples and exercises help you understand and apply each topic even if Analytics Engineering is new to you.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Introduction to Analytics Engineering

Gain next-level skills with Coursera Plus for $199 (regularly $399). Save now.

Introduction to Analytics Engineering

Instructor: Edureka

Included with

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

3 modules

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

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 3 modules in this course

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.

With a practical, applied approach, the course covers essential topics such as writing effective SQL queries, organizing raw, staging, and mart layers, designing fact and dimension tables, and building automated transformations using dbt. You will learn how to structure data models, implement data quality checks, manage lineage, and support scalable analytics within modern data environments. • By the end of this course, you will be able to: • Understand the role of analytics engineering in modern data workflows • Design dimensional models using facts, dimensions, keys, and grain • Build structured ELT pipelines across raw, staging, and mart layers • Create, run, test, and document dbt Core models • Apply tests and documentation to strengthen data quality and transparency This course is designed for freshers, aspiring analytics engineers, data analysts, and data engineers who want to expand their skills in SQL, data modeling, ELT processes, and dbt development. It is ideal for anyone looking to build dependable, scalable, and well-documented analytics pipelines in today’s data-driven environments.

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

13 videosTotal 60 minutes

Specialization Introduction5 minutes
Course Introduction4 minutes
Role of analytics engineering in the data lifecycle5 minutes
ELT vs. ETL in modern pipelines5 minutes
SQL Querying Essentials4 minutes
Analytical and Window Functions for Business Metrics4 minutes
Compute Total Sales and Revenue per Category in PostgreSQL5 minutes
Create Reusable SQL Views for Reporting4 minutes
OLTP vs. OLAP and Warehouse Basics 4 minutes
Normalization vs. Denormalization Trade-offs3 minutes
Create Dimension and Fact Tables in PostgreSQL4 minutes
Compare Performance: Normalized vs. Denormalized Queries - EXPLAIN ANALYZE Command5 minutes
Compare Performance: Normalized vs. Denormalized Queries - Inserting Data2 minutes

6 readingsTotal 45 minutes

Course Overview5 minutes
Setting up PostgreSQL10 minutes
How to use Discussion Prompt5 minutes
Writing Efficient Analytical SQL10 minutes
Data Warehousing Principles10 minutes
Module Summary: Introduction to Analytics Engineering5 minutes

4 assignmentsTotal 48 minutes

Knowledge Check: Modern Data Stack & SQL Foundations30 minutes
Practice Quiz: Overiew of Analytics Engineering6 minutes
Practice Quiz: SQL for Analytics6 minutes
Practice Quiz: Fundamentals of Data Warehousing6 minutes

3 discussion promptsTotal 15 minutes

Introduce Yourself5 minutes
Preventing Data Quality Issues at the Query Level5 minutes
Evolving Warehouse Needs5 minutes

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

15 videosTotal 68 minutes

Facts, Dimensions, Grain, and Keys2 minutes
Star vs. Snowflake Schema Design2 minutes
Design a Retail Star Schema - Creating Tables5 minutes
Design a Retail Star Schema - Viewing the Results2 minutes
Query KPIs using your Dimensional Model - Total Revenue5 minutes
Query KPIs using your Dimensional Model - Combining Revenue and AOV3 minutes
ELT Workflows and Data Movement3 minutes
Raw, Staging, and Mart Layers3 minutes
Create Staging Tables and Apply Basic Cleaning with SQL6 minutes
Populate a Mart Table from Staging - Creating Tables7 minutes
Populate a Mart Table from Staging - Total Revenues and AOV7 minutes
What is dbt3 minutes
Profiles and Project Structure2 minutes
Initialize a dbt project6 minutes
Run dbt Projects and verify outputs in the database6 minutes

4 readingsTotal 35 minutes

Choosing the Right Grain for Fact Tables10 minutes
Data Cleaning and Standardization in the Staging Layer10 minutes
How dbt Transforms SQL into Production-Ready Workflows10 minutes
Module Summary: Data Modeling and ELT Concepts5 minutes

4 assignmentsTotal 48 minutes

Knowledge Check: Data Modeling and ELT Concepts30 minutes
Practice Quiz: Basics of Dimensional Modeling6 minutes
Practice Quiz: ELT Process and Data Layers6 minutes
Practice Quiz: Introduction to dbt Core6 minutes

2 discussion promptsTotal 10 minutes

Scaling ELT as Data Volume Grows5 minutes
Managing Environments in dbt5 minutes

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

14 videosTotal 52 minutes

Configuring sources and refs2 minutes
Folder Structure and Naming Conventions2 minutes
Build Staging and Dimension Models from Sources - Setting up SQL and yml File5 minutes
Build Staging and Dimension Models from Sources - Running the Model2 minutes
Visualize Lineage and DAG in dbt Docs5 minutes
Table, View and Incremental Model2 minutes
Seeds for Static Data and Lookup Tables2 minutes
Add a seed and run dbt seed3 minutes
Build an Incremental Fact Orders Model - Setting up SQL and yml File4 minutes
Build an Incremental Fact Orders Model - Running the Model5 minutes
dbt tests (unique, not_null, relationships) 2 minutes
Custom SQL tests & documentation strategy 2 minutes
Run tests & analyze failures 7 minutes
Course Summary3 minutes

5 readingsTotal 95 minutes

Best Practices for dbt Model Management10 minutes
Best Practices for dbt Materializations and Seeds10 minutes
Improving Data Quality with dbt Tests10 minutes
Module Summary: Building and Testing dbt Models5 minutes
Practice Project: Building an End-to-End Analytics Pipeline for a Retail Business60 minutes

5 assignmentsTotal 93 minutes

End Course Knowledge Check:Introduction to Analytics Engineering45 minutes
Enhancing Data Reliability With SQL and dbt Practices30 minutes
Practice Quiz: Creating dbt Models6 minutes
Materializations and Seeds6 minutes
Practice Quiz: Testing and Documenting Models6 minutes

3 discussion promptsTotal 15 minutes

Improving Model Maintainability5 minutes
Detecting Upstream Data Breaks5 minutes
Describe Your Learning Journey5 minutes

Instructor

Edureka

126 Courses119,459 learners

Offered by

Edureka

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

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.

This course is ideal for aspiring analytics engineers, data analysts, BI developers, data engineers, and IT professionals who want to strengthen their skills in SQL, data modeling, and dbt. It is also suitable for learners entering the data field who want to understand how modern analytics pipelines are built. No prior analytics engineering experience is required, although basic familiarity with data or spreadsheets is helpful.

The course covers the foundations of SQL for analytics, dimensional modeling concepts, ELT workflows, raw and staging layer design, and dbt Core fundamentals. You will learn how to build fact and dimension tables, create staging models, test and document dbt models, and generate lineage using dbt Docs. The course blends theory with hands-on practice to reinforce real analytics engineering skills.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.