Technology

Business and Economics

Dimensional Modeling for Data Warehouses

Practical Techniques for Star Schema Design, ETL, and Managing the Data Warehouse LifecycleBy Sofia Petrova

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Length15h 17m

About this audiobook

A warehouse fails one grain statement at a time. This book makes the method explicit. Most dimensional models do not collapse in a dramatic moment. They erode through vague grains, department-shaped requirements, and dimensions that cannot answer the next question. Dimensional Modeling for Data Warehouses gives you a repeatable method for building star schemas that survive real analytical pressure. You will learn to identify business processes rather than departments, build a bus matrix that sequences delivery, and declare a grain in one sentence that holds up when the business asks something new. The book moves from architecture and requirements through the four-step dimensional design method, then into fact table types and additivity. You will choose between transaction, periodic snapshot, accumulating snapshot, and factless fact tables, handle semi-additive balances correctly, and design dimensions with surrogate keys, hierarchies, and the slowly changing approach the business actually needs. Advanced chapters cover role-playing, junk, outrigger, and mini-dimensions, bridge tables for multi-valued relationships, and complete models for retail sales, inventory, order management, and finance. Later chapters cover change data capture, quality screens, surrogate key pipelines, late-arriving data, partitioning, aggregates, semantic layers, governance, and testing on modern cloud platforms. What you will learn: • Apply the four-step dimensional design method to any business process • Build a bus matrix that sequences delivery across subject areas • Declare a grain in one sentence and defend it against real questions • Choose between transaction, periodic snapshot, accumulating snapshot, and factless fact tables • Handle semi-additive balances and additivity correctly • Design dimensions with surrogate keys, hierarchies, and slowly changing dimensions • Use advanced patterns: role-playing, junk, outrigger, mini-dimensions, and bridge tables • Model retail sales, inventory, order management, and finance end to end • Implement ETL subsystems, CDC, quality screens, surrogate key pipelines, and late-arriving data handling • Plan physical design, partitioning, aggregates, semantic layers, governance, and testing Written for data engineers, analytics engineers, and warehouse architects, this book is for practitioners who need models that stay coherent as platforms change and questions multiply. If you are responsible for the warehouse lifecycle, from requirements to evolution, this is your practical guide.

Audiobook details

GenreTechnology, Business and Economics

Length15 hrs 17 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateSep 27, 2026

LanguageEnglish

1Dimensional Modeling for Data Warehouses

2Foreword

3Preface

4About This Book

5Chapter 1: Warehouse and Analytics Architecture

Mostrar todos os capítulos

61.1 The Reporting Problem That Starts Every Warehouse

71.2 Dimensional Bus Architecture Versus Normalised Enterprise Models

81.3 Data Lakes, Lakehouses and Where Dimensional Models Live

91.4 The Roles of Staging, Presentation and Semantic Layers

101.5 Where Dimensional Models Earn Their Place

11Chapter 2: Requirements, Business Processes and the Bus Matrix

122.1 Interviewing Business Users for Dimensional Requirements

132.2 Profiling Source Data Before You Model It

142.3 Identifying Business Processes Rather Than Departments

152.4 Building the Bus Matrix

162.5 Sequencing Releases and Setting Scope

17Chapter 3: The Four-Step Dimensional Design Method

183.1 Step One: Select the Business Process

193.2 Step Two: Declare the Grain in One Sentence

203.3 Step Three: Identify the Dimensions

213.4 Step Four: Identify the Facts

223.5 Testing the Model Against Real Questions

23Chapter 4: Fact Table Types and Additivity

244.1 Transaction Fact Tables

254.2 Periodic Snapshot Fact Tables

264.3 Accumulating Snapshot Fact Tables

274.4 Factless Fact Tables

284.5 Additive, Semi-Additive and Non-Additive Measures

294.6 Degenerate Dimensions, Allocations and Multi-Currency Facts

30Chapter 5: Dimension Design and Surrogate Keys

315.1 Surrogate, Natural and Durable Keys

325.2 Attribute Completeness and Hierarchies

335.3 Date and Time Dimensions

345.4 Flags, Grouping Attributes and Null Handling

355.5 Denormalisation Versus Snowflaking

36Chapter 6: Slowly Changing Dimensions

376.1 The Business Question Behind Every SCD Choice

386.2 Type 1: Overwrite

396.3 Type 2: Row Versioning

406.4 Type 3 and Hybrid Techniques

416.5 Choosing a Type from How the Business Asks Historical Questions

42Chapter 7: Advanced Dimension Patterns

437.1 Role-Playing Dimensions

447.2 Junk Dimensions

457.3 Outrigger Dimensions

467.4 Mini-Dimensions for Rapidly Changing Attributes

477.5 Bridge Tables for Multi-Valued and Ragged Relationships

48Chapter 8: Modelling Common Business Processes

498.1 Retail Sales and Promotions

508.2 Inventory Positions and Movement

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