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Combine the Simplicity of Kimball Modeling with the Versatility of Data Vault 2.0 

Get the Best of Both Worlds.

AnalyticsCreator's mixed modeling approach combines the simplicity and transparency of Kimball modeling with the versatility and adaptability of Data Vault 2.0 architecture. This gives you the best of both worlds, allowing you to create a data warehouse that is both easy to use and scalable.

Benefits of the mixed modeling approach:

  • Simplicity and transparency: The mixed modeling approach is easy to understand and use, even for those who are not familiar with Data Vault 2.0. This makes it a good choice for businesses of all sizes.
  • Versatility and adaptability: The mixed modeling approach is very versatile and can be used to model a wide variety of data sources. It is also adaptable to changing business needs.
  • Performance and scalability: The mixed modeling approach can improve the performance and scalability of your data warehouse by reducing the need to create and maintain multiple models.
  • Accuracy and reduced risk: The mixed modeling approach can help to improve the accuracy of your data warehouse and reduce the risk of errors.
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How it works

The mixed modeling approach works by adding hash keys and hash key relations to a traditional Kimball model. This allows you to take advantage of the benefits of Data Vault 2.0, such as its flexibility and scalability, without sacrificing the simplicity and transparency of Kimball modeling.

Get more information on Mixed Modeling by AnalyticsCreator

Business

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  • Agility
  • Automate Tasks
  • Prevent Fraud
  • Employee Efficiency

Information

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Technical

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  • Simplify ETL
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  • Data Modeling

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Improved decision-making

Make better decisions by providing business with access to accurate and reliable data.

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Reduced costs

Reduce costs by improving the efficiency of the data warehouse and reducing the risk of errors.

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Increased agility

Become more agile in your decision-making by making it easier to add new data sources or change the way that data is analyzed.