Data Analysis with Multidimensional Modelling Course Overview

Data Analysis with Multidimensional Modelling Course Overview

The Data Analysis with Multidimensional Modelling course is designed to equip learners with the skills to utilize business intelligence (BI) tools and data modelling techniques. In Module 1: Introduction to Business Intelligence and Data Modelling, students will gain foundational knowledge of BI concepts and data models, understand the differences between MS SQL databases and cubes, and learn how to connect databases with SSIS and SSAS. They'll explore the creation and connection of data sources and multidimensional databases.

Module 2: SCD and Dimensions with Cube dives into practical aspects, teaching students to work with dimensions, measures, and measure groups, and introduces MDX for cube calculations.

In Module 3: Tabular Data Model with Analysis Services, learners will create a tabular data model and employ DAX to formulate calculated columns and measures. This course will be instrumental for those seeking to enhance their data analysis capabilities and gain proficiency in multidimensional modelling and business intelligence tools.

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  • Live Online Training (Duration : 16 Hours)
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  • Guaranteed-to-Run (GTR)
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  • Live Online Training (Duration : 16 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To ensure that participants can fully benefit from the Data Analysis with Multidimensional Modelling course, the following prerequisites are recommended:


  • Basic understanding of database concepts, including tables, relationships, and keys.
  • Familiarity with the fundamentals of SQL and experience in writing SQL queries.
  • Knowledge of Microsoft SQL Server, including the ability to navigate SQL Server Management Studio (SSMS).
  • Some exposure to data warehousing concepts such as ETL (Extract, Transform, Load) processes, star and snowflake schemas.
  • An introductory level of understanding of business intelligence (BI) concepts and the role of BI in decision making.

These prerequisites are meant to provide a foundation for the technical content of the course. They are not meant to deter anyone with a genuine interest in learning. If you are new to some of these concepts, we encourage you to take introductory courses or seek out resources that can help you get up to speed. Koenig Solutions is committed to supporting you on your learning journey.


Target Audience for Data Analysis with Multidimensional Modelling

This course offers an in-depth understanding of business intelligence, data analysis, and multidimensional modeling, tailored for professionals in the data sphere.


  • Data Analysts
  • Business Intelligence Analysts
  • SQL Server Developers
  • Database Administrators
  • Data Architects
  • Business Analysts
  • IT Professionals who work with data warehousing
  • System Analysts looking to leverage BI tools
  • Data Scientists seeking to understand multidimensional data structures
  • Report Developers
  • ETL Developers
  • Professionals interested in learning about SSIS, SSAS, and MDX
  • Technical Project Managers overseeing BI projects


Learning Objectives - What you will Learn in this Data Analysis with Multidimensional Modelling?

Introduction to Course Learning Outcomes:

Gain a comprehensive understanding of business intelligence, multidimensional modeling, and data analysis. Learn to create and manipulate data models, understand cubes, and use SSIS and SSAS with SQL databases.

Learning Objectives and Outcomes:

  • Understand the principles of Business Intelligence (BI) and the role of data modeling in BI.
  • Identify the differences between traditional relational databases (MS SQL) and multidimensional databases (cubes).
  • Learn to connect and integrate MS SQL databases with SQL Server Integration Services (SSIS) and SQL Server Analysis Services (SSAS).
  • Gain knowledge about the architecture and components of cubes, including measures and dimensions.
  • Acquire practical skills in creating data sources, multidimensional databases, and establishing their connections.
  • Understand Slowly Changing Dimensions (SCD) and how to manage historical data within a cube.
  • Develop proficiency with dimensions, measures, and measure groups within a cube environment.
  • Learn to write Multidimensional Expressions (MDX) for complex calculations and data analysis within cubes.
  • Create a Tabular Data Model using Analysis Services for modern data analysis.
  • Use Data Analysis Expressions (DAX) to define calculated columns and measures in a Tabular Data Model.