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Data Warehouse Design & Dimensional Modeling Training Course

Builds the skills to translate reporting needs into warehouse designs, define the grain of fact tables and model dimensions for consistent business analysis.

DWH · Data Warehousing & Data EngineeringAll Levels#2734ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data Warehousing & Data Engineering

Course Overview

This course develops practical skills in designing data warehouses that consolidate information from multiple operational systems into a reliable structure for reporting, business intelligence, and strategic analysis. Participants learn how to translate business reporting requirements into warehouse architectures, design fact and dimension tables, and apply dimensional modeling techniques that support accurate and consistent analysis. The programme covers star and snowflake schemas, grain definition, measures, dimension attributes, surrogate keys, slowly changing dimensions, and fact table design. Participants also explore ETL/ELT processes, data integration, data quality, historical data management, and performance considerations. Through practical design exercises, they learn to build scalable dimensional models that support analytical queries, management dashboards, and tools such as Power BI and Tableau.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Explain data warehouse architecture and its role in enterprise analytics.
  • Translate business reporting requirements into dimensional design specifications.
  • Define the grain of fact tables and select appropriate measures and dimensions.
  • Design star and snowflake schemas for analytical workloads.
  • Apply appropriate techniques for managing historical dimension changes.
  • Select suitable fact table types for transactions, periodic snapshots, and accumulating processes.
  • Design surrogate keys and manage relationships between facts and dimensions.
  • Evaluate ETL/ELT processes for integrating data from multiple source systems.
  • Identify data quality, consistency, and historical tracking requirements.
  • Assess dimensional models for scalability, query performance, and reporting accuracy.

Who Should Attend

This course is designed for data warehouse developers, data engineers, database administrators, data architects, business intelligence developers, and data analysts involved in designing or maintaining analytical data platforms. It is also suitable for BI managers, reporting specialists, analytics consultants, and IT professionals responsible for data integration, enterprise reporting, and decision-support systems. Participants from government entities, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations will benefit from learning how to structure complex data for reliable analysis and consistent management reporting. Familiarity with relational databases and basic SQL is recommended.

Learning Outcomes

  • Upon completing the course, participants will be able to:
  • Interpret data warehouse components and their relationships within an analytical architecture.
  • Convert reporting requirements into fact and dimension structures.
  • Define fact table grain to ensure consistent and meaningful measures.
  • Develop dimensional models using star and snowflake schemas.
  • Distinguish transaction, periodic snapshot, and accumulating snapshot fact tables.
  • Implement surrogate keys and manage dimension relationships.
  • Apply slowly changing dimension techniques to preserve historical context.
  • Identify integration and transformation requirements across source systems.
  • Evaluate data quality controls and reconciliation procedures for warehouse loading.
  • Review dimensional models for analytical usability, scalability, and performance.

Course Outline

Day 1

Data Warehouse Architecture and Dimensional Modeling Fundamentals

  • Data warehouse concepts and their role in business intelligence and enterprise reporting.
  • Differences between operational databases, analytical databases, data warehouses, and data marts.
  • Overview of enterprise data warehouse architectures and analytical data flows.
  • Understanding source systems, staging areas, integration layers, and presentation layers.
  • Principles of dimensional modeling and business-oriented data organization.
  • Translating business questions and reporting requirements into data design requirements.
  • Identifying business processes, analytical subjects, and required reporting dimensions.
  • Understanding the relationship between dimensional models, semantic layers, and BI tools.
  • Practical Exercise: Analyze reporting requirements and define the initial scope and architecture of a data warehouse solution.
Day 2

Fact Tables, Dimension Tables, and Schema Design

  • Identifying business events and selecting the appropriate fact table structure.
  • Defining fact table grain before selecting measures and dimensions.
  • Designing fact tables for transactions, periodic snapshots, and accumulating processes.
  • Distinguishing additive, semi-additive, and non-additive measures.
  • Designing dimension tables with descriptive attributes and business hierarchies.
  • Developing star schemas and evaluating snowflake schema alternatives.
  • Managing surrogate keys, natural keys, and referential relationships.
  • Applying role-playing dimensions, degenerate dimensions, and conformed dimensions.
  • Practical Exercise: Design a dimensional model for a sales, finance, procurement, or operational reporting scenario, including fact tables, dimensions, grain, and measures.
Day 3

Historical Data Management and Advanced Dimensional Techniques

  • Understanding the need to preserve historical context in analytical systems.
  • Applying Slowly Changing Dimensions (SCD) Types 1, 2, and 3.
  • Selecting historical tracking methods according to business and reporting requirements.
  • Managing effective dates, expiration dates, current-record indicators, and version history.
  • Handling late-arriving facts and dimension records.
  • Addressing inferred members and missing dimension references.
  • Designing bridge tables for selected many-to-many relationships.
  • Managing hierarchies, changing classifications, and shared dimensions across business processes.
  • Practical Exercise: Design a customer or organizational dimension that preserves historical changes and supports accurate reporting across different time periods.
Day 4

Data Integration, ETL/ELT, and Warehouse Quality

  • Understanding ETL and ELT workflows for populating dimensional models.
  • Mapping source fields to target fact and dimension structures.
  • Applying data cleansing, standardization, transformation, and validation rules.
  • Managing incremental loads, change data capture, and data refresh schedules.
  • Handling duplicate records, missing values, invalid keys, and inconsistent reference data.
  • Establishing data reconciliation controls between source systems and warehouse tables.
  • Managing load dependencies, error handling, recovery, and audit logging.
  • Understanding data lineage, metadata, and documentation requirements.
  • Practical Exercise: Develop an ETL/ELT mapping and validation plan for loading source data into a dimensional warehouse, including reconciliation and error-handling rules.
Day 5

Performance Optimization, Model Validation, and Practical Implementation

  • Reviewing dimensional model design for analytical usability and maintainability.
  • Understanding how schema structure and table grain influence query accuracy.
  • Evaluating indexing, partitioning, aggregation, and materialized views where supported.
  • Identifying common performance issues caused by joins, excessive data retrieval, and poorly structured models.
  • Optimizing dimensional structures for reporting tools and analytical workloads.
  • Validating measures, relationships, historical records, and reconciliation results.
  • Preparing dimensional datasets for Power BI, Tableau, and other business intelligence platforms.
  • Documenting design decisions, assumptions, business definitions, and model limitations.
  • Final Workshop: Develop and present an end-to-end dimensional warehouse design, including business requirements, architecture, fact and dimension tables, historical tracking, data integration controls, and performance considerations.

Upcoming Dates

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Erbil · 21 – 25 Mar 2027 · Classroom

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2 – 6 Nov 2026€6,100—
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21 – 25 Dec 2026€5,600€5,040until 30 Nov
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ParisScheduled
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22 – 26 Feb 2027€4,200€3,780until 1 Feb
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