Data Vault Modeling Approach

Benefit from Data Vault as a modeling technique for Data Warehouses that provides fast data understanding, more flexibility, historization and parallel data loading processes.

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Challenges of classic Data Warehouses

In the Data Warehouse environment, there are two well-known modeling approaches according to Kimball and Inmon that have been used for countless years when it comes to storing data. However, these have to face more and more growing challenges:

New requirements

Requirements for technologies, concepts and best practices in the work environment have constantly evolved.

Larger amounts of data

Often, larger data volumes and the flexibility required of today's systems pose major problems for these approaches.

Growing IT costs

One of the main advantages of this modeling approach is its flexibility to changes, which of course also has an impact on costs.

It is therefore questionable whether these approaches are still appropriate for all the modern issues and requirements of today. This consideration gave rise to the Data Vault modeling approach.

What is Data Vault?

Data Vault is a modeling technique that is particularly suitable for agile Data Warehouses. It offers a high flexibility for extensions, a complete historization of the data and allows a parallelization of the data loading processes.

This hybrid approach combines all the advantages of the third normal form with the star schema. Especially in today's world, companies need to transform their businesses in ever shorter cycles and map these transformations in the Data Warehouse. Data Vault supports exactly these requirements without significantly increasing the complexity of the Data Warehouse over time. Unlike Kimball and Inmon, this eliminates the ever-increasing IT costs associated with extensive implementation and testing cycles and a long list of potential dependencies.

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Procedure for Data Vault

The Data Integration Architecture of the Data Vault approach has robust standards and definition methods that bring information together to use them in a way that makes sense. The model consists of three basic table types:

Schaubild zur Veranschaulichung der Datenintegrationsarchitektur des Data Vault Ansatzes

Advantages of Data Vault

Due to the structure and the defined standards, there are many advantages for the Data Vault approach:

Massive reduction in development time when implementing business requirements
Earlier return on investment (ROI)
Scalable Data Warehouse
Traceability of all data back to the source system
Near-real-time loading (in addition to classic batch run)
Big Data Processing (>Terabytes)
Iterative, agile development cycles with incremental expansion of the DWH
Few, automatable ETL patterns

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Marc Bastien

Software Architect TIMETOACT Software & Consulting GmbH

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