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

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Introduction to Analytics Engineering

This course is part of Analytics Engineering with dbt Specialization

Instructor: Edureka

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

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

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Build your subject-matter expertise

This course is part of the Analytics Engineering with dbt Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videos Total 61 minutes

Specialization Introduction 5 minutes
Course Introduction 4 minutes
Role of analytics engineering in the data lifecycle 5 minutes
ELT vs. ETL in modern pipelines 5 minutes
SQL Querying Essentials 5 minutes
Analytical and Window Functions for Business Metrics 5 minutes
Compute Total Sales and Revenue per Category in PostgreSQL 5 minutes
Create Reusable SQL Views for Reporting 5 minutes
OLTP vs. OLAP and Warehouse Basics 4 minutes
Normalization vs. Denormalization Trade-offs 3 minutes
Create Dimension and Fact Tables in PostgreSQL 4 minutes
Compare Performance: Normalized vs. Denormalized Queries - EXPLAIN ANALYZE Command 6 minutes
Compare Performance: Normalized vs. Denormalized Queries - Inserting Data 3 minutes

6 readings Total 45 minutes

Course Overview 5 minutes
Setting up PostgreSQL 10 minutes
How to use Discussion Prompt 5 minutes
Writing Efficient Analytical SQL 10 minutes
Data Warehousing Principles 10 minutes
Module Summary: Introduction to Analytics Engineering 5 minutes

4 assignments Total 48 minutes

Practice Quiz: Overiew of Analytics Engineering 6 minutes
Practice Quiz: SQL for Analytics 6 minutes
Practice Quiz: Fundamentals of Data Warehousing 6 minutes
Knowledge Check: Modern Data Stack & SQL Foundations 30 minutes

3 discussion prompts Total 15 minutes

Introduce Yourself 5 minutes
Preventing Data Quality Issues at the Query Level 5 minutes
Evolving Warehouse Needs 5 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 videos Total 68 minutes

Facts, Dimensions, Grain, and Keys 3 minutes
Star vs. Snowflake Schema Design 3 minutes
Design a Retail Star Schema - Creating Tables 6 minutes
Design a Retail Star Schema - Viewing the Results 2 minutes
Query KPIs using your Dimensional Model - Total Revenue 6 minutes
Query KPIs using your Dimensional Model - Combining Revenue and AOV 3 minutes
ELT Workflows and Data Movement 3 minutes
Raw, Staging, and Mart Layers 3 minutes
Create Staging Tables and Apply Basic Cleaning with SQL 6 minutes
Populate a Mart Table from Staging - Creating Tables 7 minutes
Populate a Mart Table from Staging - Total Revenues and AOV 7 minutes
What is dbt 3 minutes
Profiles and Project Structure 3 minutes
Initialize a dbt project 7 minutes
Run dbt Projects and verify outputs in the database 6 minutes

4 readings Total 35 minutes

Choosing the Right Grain for Fact Tables 10 minutes
Data Cleaning and Standardization in the Staging Layer 10 minutes
How dbt Transforms SQL into Production-Ready Workflows 10 minutes
Module Summary: Data Modeling and ELT Concepts 5 minutes

4 assignments Total 48 minutes

Practice Quiz: Basics of Dimensional Modeling 6 minutes
Practice Quiz: ELT Process and Data Layers 6 minutes
Practice Quiz: Introduction to dbt Core 6 minutes
Knowledge Check: Data Modeling and ELT Concepts 30 minutes

2 discussion prompts Total 10 minutes

Scaling ELT as Data Volume Grows 5 minutes
Managing Environments in dbt 5 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 videos Total 52 minutes

Configuring sources and refs 2 minutes
Folder Structure and Naming Conventions 3 minutes
Build Staging and Dimension Models from Sources - Setting up SQL and yml File 5 minutes
Build Staging and Dimension Models from Sources - Running the Model 2 minutes
Visualize Lineage and DAG in dbt Docs 5 minutes
Table, View and Incremental Model 3 minutes
Seeds for Static Data and Lookup Tables 2 minutes
Add a seed and run dbt seed 4 minutes
Build an Incremental Fact Orders Model - Setting up SQL and yml File 4 minutes
Build an Incremental Fact Orders Model - Running the Model 6 minutes
dbt tests (unique, not_null, relationships) 3 minutes
Custom SQL tests & documentation strategy 3 minutes
Run tests & analyze failures 7 minutes
Course Summary 4 minutes

5 readings Total 95 minutes

Best Practices for dbt Model Management 10 minutes
Best Practices for dbt Materializations and Seeds 10 minutes
Improving Data Quality with dbt Tests 10 minutes
Module Summary: Building and Testing dbt Models 5 minutes
Practice Project: Building an End-to-End Analytics Pipeline for a Retail Business 60 minutes

5 assignments Total 93 minutes

Practice Quiz: Creating dbt Models 6 minutes
Materializations and Seeds 6 minutes
Practice Quiz: Testing and Documenting Models 6 minutes
End Course Knowledge Check:Introduction to Analytics Engineering 45 minutes
Enhancing Data Reliability With SQL and dbt Practices 30 minutes

3 discussion prompts Total 15 minutes

Improving Model Maintainability 5 minutes
Detecting Upstream Data Breaks 5 minutes
Describe Your Learning Journey 5 minutes

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Edureka

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Edureka

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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.

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.