Smart data automation with data vault modeling

An overview of whether data vault modeling is an suitable approach for your analytical data project.

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What is data vault modeling?

More and more organizations consider implementing data vault in their new analytical data solution as a part of the modernization of their BI systems. But does it work for your organization?

Data vault modeling is a database modelling method, especially designed for analytical data solutions with a high number of structural changes. The basic concept of data vault is to split information in a way that allows easy integration and historization of the data. Additionally, the model can be enhanced without migration of the existing tables. With these three types of tables – hubs, links and satellites – comprehensive and extensible data models can be built.

Data vault is typically used for modelling the core layer of analytical data solutions with many different source systems. BI users do not access the data vault tables directly, but run their queries and reports on dimensional data marts, that are loaded from the data vault layer.

Use cases

When do I use data vault?

The data vault approach would be suitable for the following scenarios.

Agile analytical data projects

Agile software projects usually have short development cycles with fast changing requirements and frequent data model extensions.

Multi-source analytical data solution

Reporting based on information from different source systems is only possible if the data has been integrated beforehand.

Large-scale analytical data projects

Data vault is especially suitable for enterprise analytical data solutions with high complexity and data coming from different business departments.


Why should I use data vault?

The data vault approach provides various advantages that might be beneficial to your organization.

Integration of data from various source systems

The source data is integrated using common business keys, stored in hubs. The required business attributes are stored in separate satellites per source system. This makes combining information for further reports easier.

Parallel loading of data from different source systems

With data vault, there is no predefined loading order, this means data sources can be loaded into the data vault independently of each other.

Complete historization of all attributes

The versioning of all attributes in the satellites allows you to trace all changes made in the past, and be able to extract data at any specific point in time.

Easy extensibility of data models

Additional entities or attributes that are used for new requirements are implemented as additional tables in Data Vault. Existing tables are usually not changed. In this way, data migration is avoided.

Simple and uniform ETL patterns

The loading of hubs, links, and satellites happens according to uniform rules, which are always constructed in the same way.


Key benefits of data automation for data vault modeling

Benefit from advanced data automation to mitigate the short-comings of the data vault modeling approach.

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Reduce development efforts

The number of ETL processes increase with with a high number of tables (hubs, links, and satellites) generated from high volume of model extensions and data models. Smart data automation can help speed up this process.

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Consistent usage of conventions

Smart data automation helps you to appropriate the right business keys in data vault, to avoid unsuitable keys complicating the integration of different sources and increase the complexity of loading data marts.

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Dependable design patterns

Smart data automation helps you with consistent design patterns, so your development team does not need to worry about the ETL patterns for different objects.

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Higher efficiency means lower costs

With up to 80% time saved using smart data automation, you and your team can focus your resources on improving on other aspects of your business.

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IT & Project Manager, Tirus International SA

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