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How can you select the best dimensional modeling framework?

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Dimensional modeling is a data engineering technique that organizes data into tables with meaningful relationships and hierarchies. It is widely used for building data warehouses, data marts, and analytical applications. However, there are different dimensional modeling frameworks that have their own advantages and disadvantages. How can you select the best one for your project? In this article, you will learn about the main dimensional modeling frameworks, their pros and cons, and some criteria to help you choose the most suitable one.

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Ramalingam Thyagarajan

IT Strategy | Digital Transformation | Enterprise Architecture | Data Engineering & Analytics
Mudra P.

MLOps Engineer
Rahul Kumar

Pursuing Dual Degree??| BS in Data Science and Application at Indian Institute of Technology, Madras | B.Tech at…

1 Kimball Methodology

The Kimball methodology is one of the most popular and widely adopted dimensional modeling frameworks. It was developed by Ralph Kimball and his team in the 1990s. The Kimball methodology focuses on creating a bottom-up design that starts with identifying the business processes and the key performance indicators (KPIs) that measure them. Then, it creates a star schema for each business process, where a central fact table contains the measures and the surrounding dimension tables contain the attributes that describe the context of the measures. The star schemas are then integrated into a single data warehouse using conformed dimensions that have consistent definitions across the business processes. The Kimball methodology is suitable for agile and iterative development, as it allows adding new star schemas and dimensions without affecting the existing ones. However, it can also result in complex and redundant data structures, especially when there are many business processes and dimensions.

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Rahul Kumar

Pursuing Dual Degree??| BS in Data Science and Application at Indian Institute of Technology, Madras | B.Tech at National Institute of Technology, Sikkim | Fellow at NxtWave's CCBP 4.0 Academy
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Choosing a best dimensional modeling framework involves grasping differences. Star and snowflake schemas suit normalized structures for efficient querying. Data vault modeling offers scalability with hierarchical structures. Kimberball prioritizes denormalized tables for quick queries. Analyze business needs, data sources, and workload patterns to determine the ideal framework for business goals, balancing query performance, flexibility, and normalization preferences.

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Ashish Singh

Visionary Senior Leader | Data Engineering | Data Analytics | Data Governance | GenAI | Speaker | Ex Yahoo, Credit Suisse, UBS
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The Kimball Methodology aids in dimensional modeling by providing a structured approach to designing data warehouses. It emphasizes understanding business processes and key performance indicators (KPIs) to create star schemas, where a central fact table holds measures surrounded by dimension tables describing their context. This method facilitates agile development, allowing for the addition of new schemas and dimensions without disrupting existing ones. However, it can lead to complexity and redundancy in data structures, particularly with numerous business processes and dimensions.

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Sarita Patole

Azure Data Engineer @HDFC Bank | Microsoft Azure | Azure DataBricks | Azure Data Factory | SSMS | Spark | Scala | PySpark | ADLS | SQL |
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Selecting the best dimensional modeling framework involves considering various factors, including the specific requirements and characteristics of your organization, the nature of your data. 1.Understand Requirements and Use Cases 2.Evaluate Cost 3.Perform Proof of Concept (POC) 4.Assess Flexibility

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2 Inmon Methodology

The Inmon methodology is another well-known and influential dimensional modeling framework. It was developed by Bill Inmon, who is considered the father of data warehousing. The Inmon methodology follows a top-down approach that starts with creating a normalized enterprise data model that covers all the data sources and entities in the organization. Then, it creates a data warehouse that stores the data in a third normal form (3NF) structure, where the tables are normalized to avoid duplication and inconsistency. Finally, it creates data marts that extract, transform, and load (ETL) data from the data warehouse into dimensional models that are tailored for specific business units or functions. The Inmon methodology is suitable for creating a consistent and comprehensive data warehouse that can serve as a single source of truth for the organization. However, it can also be costly and time-consuming to implement, as it requires extensive data modeling and ETL processes.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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La metodología de Inmon no es un marco de trabajo dimensional, la razón es simple: no usa los bloques "fact" y "dimension" como herramientas fundamentales para estructura los datos de las entidades, además que busca representar más actividades del negocio dentro del modelo mismo. En realidad es más orientado por las tecnicas de las formas normales; por lo que implica que una tabla puede tener elementos de una tabla hecho y dimensional dentro de ella.

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Ashish Singh

Visionary Senior Leader | Data Engineering | Data Analytics | Data Governance | GenAI | Speaker | Ex Yahoo, Credit Suisse, UBS
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The Inmon Methodology aids in dimensional modeling by providing a structured top-down approach. It begins with establishing a normalized enterprise data model, ensuring comprehensive coverage of organizational data sources and entities. Data is then stored in a third normal form (3NF) within a data warehouse to prevent duplication and inconsistency. Finally, data marts are created through extract, transform, and load (ETL) processes, tailoring dimensional models to specific business units or functions. While offering a consistent and comprehensive single source of truth, this approach demands extensive data modeling and ETL efforts, making it suitable for organizations prioritizing data integrity and uniformity over rapid implementation.

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3 Data Vault Methodology

The Data Vault methodology is a relatively newer and less common dimensional modeling framework. It was developed by Dan Linstedt in the early 2000s. The Data Vault methodology aims to create a scalable and flexible data warehouse that can handle changes in data sources and business requirements. It does so by creating a hybrid model that combines the normalized structure of the Inmon methodology and the dimensional structure of the Kimball methodology. The Data Vault model consists of three types of tables: hubs, links, and satellites. Hubs store the unique identifiers of business entities, links store the associations between entities, and satellites store the attributes and histories of entities. The Data Vault model is then used to feed data marts that have dimensional models for specific analytical purposes. The Data Vault methodology is suitable for creating a resilient and adaptable data warehouse that can accommodate complex and evolving data environments. However, it can also be challenging and complex to design, implement, and maintain, as it requires advanced data engineering skills and tools.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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La metodología Data Vault es un caso interesante porque a nivel estructural se parece mucho al enfoque galaxia - por ahi posiblemente el termino hibrido - pero el concept "link" no es un concepto de alguna forma referenciado desde un marco de trabajo dimensional. En mi opinión, Data Vault no es dimensional; sino su propio paradigma; porque el concepto "satellite" no siempre garantizara que sea similar al concepto "dimension" como también aplica los mismo para el concepto "fact" con el concepto "hub"; sino que sería más un caso especial. A menos que se defina una nueva versión de los conceptos "dimension" y "fact", no creo que halla un relación entre ambos.

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Ashish Singh

Visionary Senior Leader | Data Engineering | Data Analytics | Data Governance | GenAI | Speaker | Ex Yahoo, Credit Suisse, UBS
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The Data Vault Methodology aids in dimensional modeling by offering a hybrid approach that combines the strengths of normalized structures with dimensional structures. It utilizes hubs, links, and satellites to store unique identifiers, associations between entities, and attributes/histories, respectively. This approach facilitates scalability and flexibility, enabling the data warehouse to handle evolving data sources and business requirements effectively. Although complex to design and implement, Data Vault creates resilient and adaptable data warehouses, feeding dimensional models for specific analytical purposes, making it suitable for complex and evolving data environments.

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4 How to Choose

When selecting a dimensional modeling framework, there is no definitive answer as to which one is the best. However, you can make an informed decision based on the project's goals, scope, and resources by considering factors such as the number and diversity of data sources and business processes, the level of consistency and quality in the data warehouse and data marts, the frequency and magnitude of changes in data sources and business requirements, the time and budget for design, implementation, and maintenance of the data warehouse and data marts, and the skills and tools available for data modeling and ETL tasks. By comparing frameworks based on these criteria, you can select the one that best fits your project's needs.

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Ramalingam Thyagarajan

IT Strategy | Digital Transformation | Enterprise Architecture | Data Engineering & Analytics
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It depends on the organization's and team's data maturity in these frameworks. It is recommended to start with the Kimball methodology (star schema) if you are setting up a data warehouse with minimal to no upfront expertise in building an analytics data platform. Data analysts can easily adapt from "one-big-table" (OBT) models to star schemas easily due to simplified data structures - fact and linked dimensional models. As the analytics requirements increases, the Inmon model (snowflake schema) can be explored to remove data redundancy by normalization and get towards an "integrated unified source of truth". The data vault approach is a complicated approach and needs significant up-front investment in time and team capabilities.

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Mudra P.

MLOps Engineer
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It depends on the priority of the business requirements and sources. If your data model is constantly changing according to new sources, then Data Vault would be appropriate. If the business case is to speedily do data analysis and showcase results in real time, the Kimball model would be preferred since you get fast queries. And if the analysis has to at intervals and only for a special kind of events that need a holistic view of data, then Inmon model is adopted.

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Ashish Singh

Visionary Senior Leader | Data Engineering | Data Analytics | Data Governance | GenAI | Speaker | Ex Yahoo, Credit Suisse, UBS
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To choose between Kimball, Inmon, and Data Vault methods for dimensional modeling, assess specific project requirements. Kimball is ideal for agile development with a focus on business processes and dimensional modeling simplicity. Inmon emphasizes a centralized data warehouse approach, suitable for complex, enterprise-wide integration and reporting needs. Data Vault offers flexibility for accommodating evolving business requirements and diverse data sources, making it suitable for highly agile environments with frequent changes. Evaluate factors like data complexity, integration needs, agility, and scalability to determine which method aligns best with your project's goals and resources.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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There are other options such as star, snowflake or galaxy methodologies that are more aligned to a dimensional framework and that can be compared. > Star approach is a good option for one analytical case. > Snowflake approach is a version memory optimizaed star approach. > Galaxy approach is a good option for several and cross-related analytical cases.

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How can you select the best dimensional modeling framework?

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1 Kimball Methodology

2 Inmon Methodology

3 Data Vault Methodology

4 How to Choose

5 Here’s what else to consider

Data Engineering

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How can you select the best dimensional modeling framework?

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1 Kimball Methodology

2 Inmon Methodology

3 Data Vault Methodology

4 How to Choose

5 Here’s what else to consider

Data Engineering

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