Dimensional Modelling Azerbaijan Eğitimi

Dimensional modelling is a core component of modern business intelligence solutions, providing the foundation for effective reporting, analytics, and decision-making. This two-day instructor-led course introduces the principles of dimensional modelling and explains how it supports data warehouses and data marts.

Designed for learners with no previous experience of dimensional modelling, the course explores data warehousing concepts, analytical requirements gathering, dimensional design techniques, and physical implementation considerations. You will learn how to design fact and dimension tables, understand modern data movement approaches, and explore how online analytical processing (OLAP) technologies support analytical workloads.

This course is vendor and product independent, allowing learners to apply the concepts across a wide range of business intelligence platforms.

Prerequisites

There are no formal prerequisites for this course. A basic understanding of business intelligence or data concepts may be beneficial but is not required.

Target audience

This course is designed for:

• Business analysts responsible for gathering analytical requirements and designing dimensional models
• Data professionals working with business intelligence and analytics solutions
• IT professionals responsible for implementing dimensional models and OLAP solutions
• Anyone looking to build a strong foundation in dimensional modelling and data warehouse design

Boyutsal Modelleme Eğitimi programına Online Instructor-Led (canlı sanal sınıf) veya Classroom Based (sınıf eğitimi) formatlarında doğrudan katılım sağlayabilirsiniz. Ayrıca bu eğitimi kurumunuza özel olarak Online Instructor-Led, Classroom Based veya Onsite (yerinde eğitim) formatlarında, tercih ettiğiniz tarih ve lokasyonda organize edebiliriz. Detaylı bilgi ve kurumunuza özel planlama için bizimle iletişime geçebilirsiniz.

By the end of this course, learners will be able to:

• Describe data warehousing concepts and the role of dimensional modelling
• Explain the process of gathering analytical requirements for dimensional models
• Distinguish between facts, measures, and dimensions when designing analytical solutions
• Design dimensional models using industry-recognised modelling techniques
• Describe the physical implementation of dimensional models
• Explain how OLAP technologies support analytical reporting and business intelligence

Introduction to data

• Understand the role of data in business intelligence
• Compare online transaction processing and online analytical processing
• Explore the principles of data warehouse design
• Understand the fundamentals of dimensional modelling
• Explain cardinality and data grain

Introduction to modelling

• Gather analytical business requirements
• Explore conceptual, logical, and physical modelling approaches
• Understand facts and dimensions
• Compare star and snowflake schemas
• Explore relationships between fact and dimension tables
• Consider security requirements within dimensional models

Designing dimension tables

• Design attributes and hierarchies
• Understand business keys and surrogate keys
• Create conformed dimensions
• Design time dimensions
• Implement role-playing dimensions
• Explore slowly changing and rapidly changing dimensions

Designing fact tables

• Define facts and measures
• Compare different types of measures
• Understand additive, semi-additive, and non-additive measures
• Explore different fact table designs
• Understand the role of foreign keys

Advanced dimension techniques

• Design degenerate dimensions
• Explore parent-child dimensions
• Understand bridge tables
• Apply snowflaking where appropriate
• Explore synonym dimensions
• Design mini dimensions
• Understand hot-swappable dimensions
• Explore multi-valued dimensions
• Understand bitmap dimensions
• Design junk dimensions
• Explore step dimensions
• Perform first and last analysis

Data movement

• Introduce extract, transform, and load processes
• Understand staging areas
• Explore the medallion architecture
• Apply data transformation and cleansing techniques
• Understand how metadata supports data quality and governance

Physical storage and aggregation

• Understand aggregation storage techniques
• Explore table partitioning
• Select appropriate data types
• Understand index design considerations
• Explore compression techniques
• Design aggregations for analytical performance
• Compare multidimensional, relational, and hybrid OLAP approaches
• Understand modern OLAP terminology
• Optimise fact table performance
• Apply indexing strategies within star schemas

Common dimensional modelling mistakes

• Review the twelve most common dimensional modelling mistakes
• Identify approaches for avoiding common design issues

Exams and assessments

There are no formal exams included in this course. Learners reinforce their understanding through instructor-led discussions and practical activities delivered throughout the course.

Hands-on learning

This course includes:

• Scenario-based exercises to apply dimensional modelling concepts
• Guided discussions based on real-world business intelligence scenarios
• Practical activities covering dimensional design techniques and analytical modelling