Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Data Modeling for Analytics Engineering

Name: Data Modeling for Analytics Engineering
Rating: 4.9 (45 reviews)

Learn how analytics engineers transform OLTP databases into OLAP models that are optimized for reporting and analysis

Highest Rated

Hot & New

Created byMaven Analytics (2,000,000+ Learners), Alice Zhao

Last updated 7/2026

English

English [Auto],

What you'll learn

Understand what a data model is and identify the difference between modeling for OLTP and OLAP databases (dimensional modeling)
Learn about popular schema designs like star and snowflake, along with their fact and dimension table structures and level of detail
Apply strategies for handling data changes in a database, with a focus on slowly changing dimensions (SCD)
Walk through a step-by-step modern data modeling process, from scoping to transformation

Course content

10 sections • 61 lectures • 2h 0m total length

Course Introduction2:06
Meet the Instructor0:20
Course Outline2:13
READ ME: Important Notes for New Students2:18
DOWNLOAD: Course Resources0:09
Setting Expectations1:37
AE 101 Recap: Overview2:38
AE 101 Recap: Data Terms3:10

Section Intro0:08
Table Fundamentals2:09
Grain / Level of Detail2:49
Grain FAQs1:54
Aggregate data to speed analytics and dashboards by selecting the right grain; start from the questions users ask, and remember multiple tables may have different grains.
Primary Keys1:36
Learn how primary keys uniquely identify each row, as a single column or a composite key of multiple columns, with uniqueness and non-null constraints in every table.
Natural vs Surrogate Keys2:06
Foreign Keys1:55
Table Design FAQs3:16
Table Fundamentals

Section Intro0:11
Schema Designs0:55
Normalization3:56
Transactional Schema2:53
Explore a transactional schema designed for oltp, optimized for inserts, updates, and deletes; highly normalized to avoid duplication, capturing sales across multiple tables and demanding multiple joins for queries.
Star Schema2:42
Snowflake Schema2:57
Schema Design FAQs2:30
Explore schema design for analytics engineering by comparing star and snowflake schemas, understanding fact and dimension tables, and handling multiple fact tables with shared or separate dimensions.
Schema Designs

Section Intro0:14
Handling Data Changes1:58
Learn to handle changing data with slowly changing dimensions in dimension tables, choosing between overriding values or preserving history. Maintain fact tables as immutable; use adjustment rows to reflect corrections.
Slowly Changing Dimensions4:05
Explore slowly changing dimensions in analytics engineering, comparing SCD type 1 (overwrite) and SCD type 2 (preserve history with dates), and learn to query current vs historical data.
Fact Table Changes2:30
Explains how fact tables are immutable and how to handle changes by adding new records to preserve a historical log, including adjustments and returns, and avoiding deletions or overwrites.
Source Data Changes3:26
Learn how source system data changes are captured for analytics, using updated_at to reflect current state or a CDC log to preserve history, shaping OLAP fact and dimension modeling.
Handling Data Changes FAQs2:28
Handling Data Changes

Section Intro0:18
Apply the theory of fact and dimension tables and data changes to a high-level overview of the modern data workflow, then focus on the data modeling piece.
Modern Data Workflow2:46
Data Analyst Role1:12
Data Engineer Role0:40
The data engineer leads the ingestion process, pulling data from external sources into the company, and orchestrates scheduled data loads in order. They monitor pipelines to ensure reliable, error-free loading.
Analytics Engineer Role1:42
Modern Data Workflow

Section Intro0:17
Data Modeling Process Overview2:02
Data Modeling Process Steps2:35
Explore the six steps of the data modeling process for analytics engineering, from gathering requirements and examining OLTP data to defining fact and dimension tables, surrogate keys, and SCD types.
Project Introduction1:29
Step 1: Gather Requirements3:49
Gather requirements with business and data analysts to define metrics such as sales and revenue, and map four OLTP tables (customers, products, orders, order items) with date information.
Step 2: Design Fact Table3:08
Design a single fact table by merging orders and order items to capture orders as events with sales and revenue measures, using row-level granularity from order items.
Step 3: Design Dimension Tables1:14
Step 4: Add Surrogate Keys1:17
Step 5: Decide on Columns2:46
Step 6: Handle Data Changes2:20
Final Project Summary2:20
Data Modeling Process

Requirements

We strongly recommend taking our Analytics Engineering for Beginners course first to get a basic understanding of core analytics engineering concepts (data warehouses, OLTP vs OLAP, etc.)

Description

This course is designed to give you a solid foundation in data modeling, from core concepts to a step-by-step process that you can apply on the job.

If analytics engineering is about transforming raw data into clean, reliable datasets, then data modeling is the blueprint. It's how you decide what to build, how to structure it, and how to make sure it holds up as your data changes over time.

We'll start by introducing the key building blocks — data models, entities, and relationships — and draw a clear line between data modeling in general and dimensional modeling specifically.

From there, we'll break down the core unit of any data model: the table. You'll learn the main characteristics of tables in a database, then explore the two types you'll find in dimensional modeling — fact tables and dimension tables — and what makes each one unique.

Next, we'll zoom out and look at how those tables fit together. We'll introduce popular schema designs, like the star schema, and talk about how to arrange your facts and dimensions in a way that's clean and easy to query.

But a data model isn't static — data changes, and your model needs to handle that. We'll cover strategies for managing those changes, including slowly changing dimensions, so your models stay accurate without breaking downstream reports.

Last but not least, we'll put it all into practice. You'll see how data flows through a modern analytics workflow, and then walk through the full data modeling process — from scoping requirements to designing and transforming tables — just like an analytics engineer would on the job.

Whether you're building your first dimensional model or looking to formalize your approach, this course will give you the theory and process to do so with confidence.

Who this course is for:

Aspiring analytics engineers looking to transition or break into the field
Data professionals who want a better understanding of data modeling theory and steps
Anyone who wants to learn how modern data teams organize data for analysis

Data Modeling for Analytics Engineering

What you'll learn

Explore related topics

Course content

Getting Started8 lectures • 15min

Data Modeling Overview6 lectures • 10min

Table Fundamentals8 lectures • 16min

Fact & Dimension Tables6 lectures • 12min

Schema Designs7 lectures • 16min

Handling Data Changes6 lectures • 15min

Modern Data Workflow5 lectures • 7min

Data Modeling Process11 lectures • 23min

Key Takeaways3 lectures • 5min

Next Steps1 lecture • 2min

Requirements

Description

Who this course is for: