Multidimensional Models vs Tabular

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Ravi Rathore

Priya Pedamkar

What Are Multidimensional Models and Tabular?

In information study and reporting, 2 salient models person gained nickname for their expertise to grip analyzable information structures and supply insights for decision-making: nan Multidimensional Model and Tabular Model. These 2 models person their unsocial strengths and applications, and knowing nan differences betwixt them is important for making informed choices successful various analytical scenarios.

Multidimensional Model vs Tabular

Table of Contents
  • What Are Multidimensional Models vs Tabular
  • What is Multidimensional Models
  • What is nan Tabular Model
  • Comparative Table
  • Comparison of Data Modeling Types
  • Case Studies
  • Choosing nan Right Model

What is Multidimensional Models?

The Multidimensional Model, often associated pinch Online Analytical Processing (OLAP) systems, organizes information into multidimensional cubes. These cubes person dimensions, hierarchies, and measures, making them well-suited for analyzable information study and reporting. Unlike accepted level databases, multidimensional models shape information successful a multidimensional space, providing a holistic accusation view.

Key Concepts

  1. Dimensions: These are nan categorical aspects of data, specified arsenic time, geography, aliases merchandise categories. Dimensions supply nan discourse for measures.
  2. Measures: Measures are nan numeric information points that are analyzed, aggregated, and summarized. Examples see income figures, quantities, aliases revenue.
  3. Hierarchies: Dimensions often person hierarchies, allowing information to beryllium organized successful a system mode and facilitating drill-down and roll-up operations.

Use Cases

  1. Data Warehouses: Multidimensional models are commonly utilized successful data warehouses, wherever ample volumes of information from different sources are stored and analyzed for business intelligence purposes.
  2. OLAP (Online Analytical Processing): Multidimensional databases alteration OLAP, allowing users to analyse information interactively from aggregate dimensions, providing a rich | analytical experience.


  • Complex Analysis: Enables complex, ad-hoc study owed to its multidimensional nature.
  • Efficient Aggregation: Facilitates speedy aggregation and summarization of information crossed various dimensions.
  • Intuitive Representation: Provides an intuitive and user-friendly measurement to understand analyzable relationships wrong nan data.


  • Data Redundancy: This whitethorn lead to redundant data storage owed to aggregate dimensions, perchance impacting retention requirements.
  • Complexity: Building and maintaining multidimensional models requires profoundly knowing nan underlying information structure.
  • Limited Scalability: Large-scale multidimensional databases mightiness look challenges successful position of scalability and performance.

What is nan Tabular Model?

The Tabular Model, connected nan different hand, is often associated pinch in-memory databases and is portion of nan Microsoft SQL Server Analysis Services (SSAS) offering. It represents information successful a flat, relational structure, resembling a accepted SQL database. Tabular models correspond information successful a structured, two-dimensional format utilizing rows and columns. This type of information building is prevalent successful relational databases and spreadsheet applications, making it a celebrated prime for various information guidance and study tasks.

Key Concepts

  1. Rows: In tabular models, individual records aliases information entries are represented by rows. Each statement usually corresponds to a unsocial information entity, and nan information wrong rows is organized hierarchically.
  2. Columns: Columns correspond attributes aliases fields of nan data. Each file defines a circumstantial type of information, specified arsenic names, dates, aliases numeric values.
  3. Relationships: In relational databases, you tin nexus tables utilizing keys and relationships. This linking enables nan relationship of information successful different tables, creating a much complete and system dataset.

Use Cases

  1. Relational Databases: Tabular models shape nan instauration of wide utilized relational database systems for information storage, management, and retrieval. Tables pinch predefined schemas shape data, and relationships found information consistency and integrity successful relational databases.
  2. Spreadsheets: Popular spreadsheet package for illustration Microsoft Excel uses a tabular information model. Users tin input, organize, and analyse information successful a grid-like format, making it accessible to a wide scope of users for tasks for illustration budgeting, information tracking, and analysis.


  • Structured Data: Tabular models supply a system and organized measurement to shop data, making it easy to support and query.
  • Data Integrity: Relational databases enforce data integrity done defined relationships and constraints.
  • Widely Adopted: Tabular models are prevalent and supported by galore database systems and package applications.


  • Limited Complexity: Tabular models whitethorn struggle pinch analyzable information structures and relationships, which could require extended normalization.
  • Performance Limitations: Large datasets and analyzable queries tin effect capacity successful relational databases.
  • Less Intuitive for Some Data: For information pinch complex, multidimensional relationships, tabular models whitethorn not supply an perfect practice for analysis.

Multidimensional Models vs Tabular Models – Comparative Table

The array beneath compares tabular differences based connected hierarchy, data, design, complexity, redundancy, throws, and columns.

Section Multidimensional Models Tabular Models
Data Representation Organized successful aggregate dimensions, forming a cube. Structured successful tables pinch rows and columns.
Data Structure Dimensions, measures, and hierarchies specify nan data. Rows correspond records, and columns correspond attributes.
Complexity Complex owed to multidimensional relationships. Simpler structure, little analyzable relationships.
Query Performance Efficient for analyzable ad-hoc analysis. Efficient for system queries but whitethorn struggle pinch complexity.
Data Redundancy May lead to information redundancy owed to aggregate dimensions. Reduces redundancy pinch normalization.
Scalability Challenges successful scaling ample databases owed to complexity. Scalable, particularly pinch due database optimization.
Maintenance Complex creation and attraction require expertise. Easier to creation and maintain, suitable for various users.
Flexibility Highly elastic for analyzable information study and reporting. Flexible for wide information retention and study tasks.
Suitable Use Cases Best for OLAP, information warehousing, and analyzable analytics. Well-suited for relational databases, spreadsheets, and simpler information needs.

Data Source Layer:

  • Both Tabular and Multidimensional models tin import immoderate information root that tin beryllium imported into Multi-Dimensional.
  • Direct Query mode successful Tabular is constricted to SQL Server relational databases.

Data Access Layer:

  • Tabular models support Cached mode and DirectQuery mode.
  • Multidimensional models support MOLAP and ROLAP modes.
  • DirectQuery mode successful Tabular is akin to ROLAP mode successful Multidimensional.
  • DirectQuery mode has limitations for illustration DAX support only from Power View, nary clip calculations, and nary calculated columns.


  • Tabular models springiness awesome capacity without unsocial tuning.
  • Multidimensional models springiness amended capacity for existing aggregations.
  • Tabular models publication information straight from nan cache utilizing file shop indexes.
  • Multidimensional models publication pre-aggregated aliases atomic information from disk.
  • Tabular models execute good astatine nan lowest granularity level, while Multidimensional models struggle pinch cross-joining large dimensions.

Data Language Layer:

  • Multidimensional models usage MDX (Multidimensional Expression) arsenic their information language.
  • Tabular models usage DAX (Data Analysis Expressions) arsenic their information language.
  • Complexity of DAX:
  • DAX is perceived arsenic analyzable and difficult to learn
  • Advanced computations successful DAX tin beryllium complex.

MDX Specificities:

  • MDX book has features for illustration SCOPE assignments and Named Sets that cannot beryllium done successful DAX.
  • The tabular task does not require defining relationships betwixt attributes for illustration successful Multidimensional models.

Comparison of Data Modeling Types successful SQL Server and BI Tools

Let’s talk nan apical comparisons betwixt nan SQL Server and BI Tools:

Modeling Type Description Initial Release Supported Compatibility Levels
Multidimensional Utilizes OLAP modeling constructs, including cubes, dimensions, and measures. SQL Server 2000 1050, 1100
Power Pivot Originally an add-in, it is now afloat integrated into Excel. It employs a Tabular exemplary infrastructure and does not support APIs aliases scripting. SQL Server 2008 R2 N/A
Tabular Employs relational modeling constructs specified arsenic models, tables, and columns. Internally, it inherits metadata from OLAP modeling constructs for illustration cubes, dimensions, and measures. It supports codification and scripting based connected OLAP metadata. SQL Server 2012 1050, 1103
Tabular successful SQL Server 2016 and later Utilizes relational modeling constructs (models, tables, columns) and is defined utilizing Tabular Model Scripting Language (TMSL) and Tabular Object Model (TOM) code. SQL Server 2016, 2014, 2019, 2022 1200, 1400, 1500, 1600
Tabular successful Azure Analysis Services Like Tabular successful SQL Server, it uses relational modeling constructs defined successful Tabular Model Scripting Language (TMSL) and Tabular Object Model (TOM) code. 2016 1200 and higher
Tabular successful Power BI Premium Like Tabular successful SQL Server, this type employs relational modeling constructs defined successful Tabular Model Scripting Language (TMSL) and Tabular Object Model (TOM) code. 2020 1500 and higher

Case Studies

Here are immoderate real-world lawsuit studies of organizations utilizing some multidimensional models and tabular models:

A. Real-world Examples of Organizations Using Multidimensional Models

  1. Retail Giant – Walmart: Walmart employs multidimensional models successful its information warehousing and business intelligence systems. With immense amounts of sales, inventory, and customer demand, nan institution relies connected multidimensional cubes to supply in-depth insights into income patterns, inventory management, and customer behavior.
  2. Healthcare Provider – Mayo Clinic: The Mayo Clinic, a renowned healthcare organization, uses multidimensional models to analyse diligent information for investigation and objective determination support. Multidimensional models alteration them to analyse diligent records from various angles, aiding test and curen planning.
  3. Financial Services – JPMorgan Chase: JPMorgan Chase leverages multidimensional models successful its consequence study and portfolio management. By utilizing OLAP and multidimensional cubes, nan statement tin execute intricate financial study to mitigate risks and optimize finance strategies.

B. Real-world Examples of Organizations Using Tabular Data Structures

  1. Social Media – Facebook: Facebook uses tabular information structures to negociate personification profiles and nan immense magnitude of user-generated content. It relies connected relational databases to shop and retrieve information efficiently, ensuring personification acquisition and consistency.
  2. E-commerce – Amazon: Amazon, 1 of nan largest e-commerce platforms, uses tabular information structures wrong its extended database systems. Product information, personification data, and transaction records are organized successful system tables, facilitating businesslike inventory guidance and bid processing.
  3. Manufacturing – Toyota: In nan manufacturing sector, Toyota employs tabular information structures to support inventory, production, and value power data. Relational databases thief streamline manufacturing processes and supply concatenation management.

Choosing nan Right Model

Here’s a little guideline connected really to take nan correct model:

  1. Consider Data Complexity: If your information has aggregate dimensions and hierarchies and requires intricate analysis, a multidimensional model, for illustration OLAP, is suitable. For simpler, system data, tabular models, specified arsenic relational databases aliases spreadsheets, activity well.
  2. Analytical Requirements: If your business demands in-depth, ad-hoc study crossed various dimensions, a multidimensional exemplary is preferable. Tabular models are apt for straightforward queries and modular reporting needs.
  3. Scalability: For large-scale information retention and accelerated query performance, tabular models pinch due indexing and optimization are scalable. Multidimensional models look challenges pinch scalability owed to their complexity.
  4. Budget and Resources: Consider your organization’s fund and nan expertise of your team. Tabular models are mostly easier and much cost-effective to instrumentality and maintain, making them suitable for smaller businesses aliases teams pinch constricted resources.
  5. Future Growth: Anticipate your organization’s early information needs. If your information complexity is expected to summation significantly, investing successful a multidimensional exemplary mightiness beryllium a much future-proof choice, allowing for analyzable study arsenic your business expands.


When it comes to choosing betwixt multidimensional models and tabular information structures, it each depends connected an organization’s circumstantial needs. Multidimensional models are awesome for analyzable and ad-hoc study scenarios, providing a broad position of multidimensional data. On nan different hand, tabular models connection simplicity, efficiency, and scalability, making them perfect for system information and modular analytical tasks. Understanding nan strengths of these models will thief make informed decisions optimizing information guidance strategies for divers business requirements.

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