Data Vault 2.0 mixed approach

Data Vault 2.0 mixed approach
Peter Smoly Sep 30, 2022

In this post, we will introduce an upgraded modelling technique that AnalyticsCreator customers can easily use. Many of our customers are already using this approach. There are two options you can use:

  1. Data Vault 2.0 as a base layer with the mixed approach on top, or

  2. Only the mixed approach without Data Vault 2.0 in the base layers.

We believe that AnalyticsCreator's Data Vault 2.0 mixed approach is an important feature for all developers.

Mixed modelling and hashing your data are two powerful techniques that can help you improve your data modeling and analytics capabilities.

Mixed modelling is an approach that combines the strengths of two popular data modeling methodologies - Data Vault 2.0 and Kimball dimensional modelling. With mixed modelling, you can enjoy the flexibility and agility of Kimball's approach, while also leveraging the robustness and scalability of Data Vault 2.0.

One of the key advantages of mixed modelling is that it allows you to create a comprehensive data model that captures both the transactional and analytical aspects of your data. By combining the two methodologies, you can create a more complete picture of your business operations, which in turn can lead to better insights and decision-making.

Hashing your data is another technique that can help you improve your data modeling and analytics. Hashing involves converting data of any length or size into a fixed-length string or hash value. By doing so, you can create a unique identifier for each piece of data, which can help you quickly and efficiently locate and retrieve specific data points.

When you combine mixed modelling with hashing your data, you can create a powerful data analytics engine that can help you extract insights from even the most complex and large datasets. With AnalyticsCreator, you can easily implement mixed modelling and hashing techniques, giving you the ability to quickly and easily create and manage your data models.

By "hashing" the data model, you can enjoy the key benefits of Data Vault 2.0 modelling without making the data model more complex. The user always has the option to choose between subject PK and relationships and hash key PK and relationships because both exist in the data model and can be used interchangeably.

Analytics Creator Data Vault 2.0 mixed approach Analytics Creator Data Vault 2.0 mixed approach

Mixed Model Approach

With AnalyticsCreator, you can use many useful modelling techniques from Data Vault 2.0 in the traditional dimensional modelling (Kimball) approach. Additionally, we offer the option to automatically "hash" your Kimball model and implement one of the most important features of Data Vault 2.0. This means that hash keys and hash key relationships can also be created in addition to primary keys and subject table relationships.

Each table is assigned a PK (primary key) hash key field, and if a table is referenced with another table, reference FK (foreign key) hash key fields are also added. References are then created between the PK hash key fields and FK hash key fields, which can be used instead of subject references. The PK and FK hash key fields are created as persisted calculated columns to speed up access to the hash keys.


So if you're looking to take your data modeling and analytics capabilities to the next level, be sure to explore mixed modelling and hashing your data. With these powerful techniques at your disposal, you can unlock new insights and make better decisions based on the data that matters most to your business.




Meet the team:

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Mr. Peter Smoly CEO

Peter Smoly is a serial entrepreneur in the Data Warehouse and Business Analytics as well software development. All together more than 25 years’ experience as a founder, CEO, project manager and consultant.


Mr. Dimitri Sorokin CTO

Dimitri Sorokin, PhD electrical engineering,  has more than 30 year IT experience. As a CIO in global operating companies, Head of development, BI and data warehouse consulting and Analytics tool development.