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

kurs ist nicht verfügbar in Deutsch (Deutschland)

Wir übersetzen es in weitere Sprachen.

Introduction to Analytics Engineering

Dieser Kurs ist Teil von Spezialisierung „Analytics Engineering with dbt“

Dozent: Edureka

Bei enthalten

Mehr erfahren

3 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

3 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Anfänger

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Data Quality
Kategorie: Version Control
Kategorie: Data Management
Kategorie: SQL
Kategorie: Data Warehousing
Kategorie: Extract, Transform, Load
Kategorie: Data Validation
Kategorie: Database Design
Kategorie: Data Transformation
Kategorie: Data Pipelines
Kategorie: Data Mart
Kategorie: Data Modeling
Kategorie: Data Analysis
Kategorie: Star Schema

Wichtige Details

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Analytics Engineering with dbt“

Wenn Sie sich für diesen Kurs anmelden, werden Sie auch für diese Spezialisierung angemeldet.

Lernen Sie neue Konzepte von Branchenexperten
Gewinnen Sie ein Grundverständnis bestimmter Themen oder Tools
Erwerben Sie berufsrelevante Kompetenzen durch praktische Projekte
Erwerben Sie ein Berufszertifikat zur Vorlage

In diesem Kurs gibt es 3 Module

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.

Das ist alles enthalten

13 Videos6 Lektüren4 Aufgaben3 Diskussionsthemen

13 Videos Insgesamt 61 Minuten

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

6 Lektüren Insgesamt 45 Minuten

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

4 Aufgaben Insgesamt 48 Minuten

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

3 Diskussionsthemen Insgesamt 15 Minuten

Introduce Yourself 5 Minuten
Preventing Data Quality Issues at the Query Level 5 Minuten
Evolving Warehouse Needs 5 Minuten

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.

Das ist alles enthalten

15 Videos4 Lektüren4 Aufgaben2 Diskussionsthemen

15 Videos Insgesamt 68 Minuten

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

4 Lektüren Insgesamt 35 Minuten

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

4 Aufgaben Insgesamt 48 Minuten

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

2 Diskussionsthemen Insgesamt 10 Minuten

Scaling ELT as Data Volume Grows 5 Minuten
Managing Environments in dbt 5 Minuten

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.

Das ist alles enthalten

14 Videos5 Lektüren5 Aufgaben3 Diskussionsthemen

14 Videos Insgesamt 52 Minuten

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

5 Lektüren Insgesamt 95 Minuten

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

5 Aufgaben Insgesamt 93 Minuten

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

3 Diskussionsthemen Insgesamt 15 Minuten

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

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Dozent

Edureka

157 Kurse 146.599 Lernende

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Häufig gestellte Fragen

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.

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¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.