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DataVault, Kimball
Databricks  

Data modeling strategies

for mid-sized business growth

Data modeling strategies for mid-sized business growth - DataVault, Kimball, and Databricks

Growing companies need data architectures that support rapid expansion. As data volumes increase, business leaders must choose modeling approaches that balance immediate needs with future flexibility. 

Business challenges 

Mid-sized companies face mounting data complexity. Multiple systems generate information in different formats. Teams need quick access to insights for decision-making. Regulatory requirements grow stricter each year. The cost of poor data architecture rises exponentially with scale. 

These challenges create concrete business impacts. Teams waste time reconciling conflicting reports. Decision-makers lack trusted data. Compliance risks increase. Technical debt accumulates. Growth opportunities get missed due to data limitations. 

Three approaches to data modeling 

The kimball method 

Kimball organizes data to match business thinking. It uses fact tables for measurements and dimension tables for context. This structure mirrors how teams analyze information and deliver reports.  

To better understand the advantages and limitations of Kimball’s method, it is useful to compare it with Inmon’s approach, which offers an alternative approach to data warehouse design. 

So, how do these methods differ? 

Kimball follows a bottom-up approach, where smaller data marts are created first, allowing teams to quickly start using data. This method focuses on user-friendly, easy-to-understand data structures, which makes it possible to access useful data for business decisions faster. In contrast, Inmon takes a top-down approach, starting with a large central data warehouse where all the data is stored first. From there, smaller data marts are created. While this process takes longer to set up, it is designed to create a more integrated and structured data system across the business. In summary, Kimball is quicker to implement and focuses on building data marts for specific business needs, while Inmon aims for a more centralized data system, requiring a longer setup but offering a broader integration of data across the organization. 

In the Kimball method, data is often organized using either a star or snowflake schema. The star schema features a central fact table surrounded by dimension tables, while the snowflake schema builds upon this by normalizing the dimension tables into multiple related tables. 

Advantages 

  • Teams can access insights without technical knowledge 
  • Report development happens quickly 
  • Query performance stays strong at scale 
  • Business rules remain consistent 
  • Integration costs stay lower 
  • Self-service analytics work well 

Limitations 

  • Changes require extensive rework 
  • Historical tracking gets complex 
  • Data cleansing can lose details 
  • Integration patterns become rigid 
  • Real-time processing proves difficult 

Data Vault 2.0 Architecture 

Data Vault 2.0 is an extension of the original Data Vault architecture that introduces several enhancements to improve scalability, flexibility, and usability. It focuses on a more agile approach to handling large amounts of data and increases the ability to process data in real-time. Data Vault 2.0 also places a stronger emphasis on business scalability and incorporates additional methodologies such as data warehousing automation, improved data governance, and advanced security. 

While the original Data Vault was designed to store raw business data in a flexible manner, Data Vault 2.0 provides more robust tools for managing the complexity of growing data environments, enabling better integration of data sources and supporting faster decision-making. 

Advantages 

  • Improved scalability for handling larger datasets and more complex environments. 
  • Enhanced agility, enabling quicker changes and adjustments in response to business needs. 
  • Stronger governance and security controls, ensuring better data integrity. 
  • Advanced automation capabilities to streamline data loading and management. 
  • Better integration with real-time data processing and analytics. 
  • Facilitates easier collaboration between business and IT teams. 

Limitations 

  • More complex setup and design compared to the original Data Vault. 
  • Requires specialized knowledge to implement and maintain effectively. 
  • Higher initial infrastructure costs due to more advanced capabilities. 
  • May involve more time in tuning for optimal performance. 
  • Can be challenging for non-specialists to manage due to its complexity. 

Databricks platform 

Databricks provides modern data processing capabilities. It combines data warehouse and data lake features with machine learning support. 

Advantages 

  • Processing power scales automatically 
  • Analytics capabilities are extensive 
  • Governance comes built-in 
  • Both batch and streaming work well 
  • Machine learning integration is native 
  • Security controls are robust 
  • Development tools are modern 

Limitations 

  • Platform dependency increases 
  • Cloud infrastructure is required 
  • Operational costs are ongoing 
  • Migration complexity is high 
  • Specialized skills are needed 

Making the right choice 

Business leaders should evaluate their situation carefully. Key factors include: 

Current state 

  • Data volumes and growth rates 
  • Source system complexity 
  • Reporting requirements 
  • Team capabilities 
  • Infrastructure investments 
  • Budget constraints 

Future needs 

  • Growth projections 
  • Analytics requirements 
  • Compliance mandates 
  • Integration patterns 
  • Innovation plans 
  • Scalability demands 

Data Modeling – Recommended Approach

  1. Data Investigation 

Begin with careful data investigation to understand the nature, sources, and quality of data. This phase is crucial for identifying where data resides, how it flows across systems, and determining its relevance for business processes. By diving deep into data, companies can lay a solid foundation for informed decision-making and future data modeling efforts. 

  1. Data Vault for Core Data Storage 

Capture raw business data, maintain complete history, enable full auditability, and support future flexibility. This approach ensures that all data, regardless of source, is stored in its original form, providing a comprehensive and reliable data repository. 

  1. Data Modeling with Kimball 

After understanding the data landscape, data modeling becomes the next critical step. Utilize Kimball models to organize data into user-friendly formats, making it accessible for business teams. This phase includes mapping data, defining relationships, and establishing calculations, enabling quick reporting, supporting self-service, and maintaining performance. 

  1. Advanced Data Processing with Databricks 

For advanced analytics, leverage Databricks to scale processing power, enable real-time analytics, support machine learning, and maintain governance. This platform allows for high-level data manipulation and exploration, driving deeper business insights and supporting complex data scenarios. 

  1. Data Governance  

Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. In the context of Databricks, it includes access control, compliance enforcement, data lineage tracking, and auditability, ensuring that organizations maintain control over their data while leveraging powerful analytics capabilities. 

This combination provides immediate benefits while enabling future growth. Companies gain reliable data storage, quick business insights, advanced processing capabilities, and a clear understanding of their data landscape. 

Implementation strategy 

Success requires careful staging of work. Each phase should deliver concrete business value. 

First 90 days: 

  • Implement core data storage 
  • Build critical reports 
  • Train initial team 
  • Document standards 
  • Monitor quality 
  • Establish governance 

Next 90 days: 

  • Add key data sources 
  • Enable self-service analytics 
  • Expand team capabilities 
  • Automate processes 
  • Enhance monitoring 
  • Strengthen controls 

Year one: 

  • Scale infrastructure 
  • Enable advanced analytics 
  • Automate operations 
  • Expand use cases 
  • Measure business impact 
  • Optimize performance 

Keys to success 

Several factors drive successful implementations: 

Leadership focus 

  • Clear data ownership 
  • Adequate resourcing 
  • Change management 
  • Success metrics 
  • Regular reviews 

Technical excellence 

  • Architecture standards 
  • Development practices 
  • Quality controls 
  • Security measures 
  • Performance tuning 

Team enablement 

  • Skills development 
  • Knowledge sharing 
  • Process documentation 
  • Support models 
  • Career paths 

Final Thoughts

Modern businesses need flexible, scalable data architectures. Effective data modeling, combining Data Vault, Kimball, and Databricks approaches, creates a strong foundation. This enables both quick wins and long-term growth. Success requires careful planning, adequate resources, and disciplined execution.

Companies that get this right gain significant advantages. These companies make better decisions faster. They adapt to change more easily and innovate with less friction. Most importantly, they turn their data into a strategic asset that drives business growth.

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