Data Vault

The efficient modelling method for creating agile data warehouse projects.

What is it about?

Due to the ever-growing amount of data, it is becoming increasingly important to establish efficient methods for collecting and evaluating company data. Thanks to the collection of this data, strategic and operative business decisions can be made faster.

In this context, Business Intelligence (BI) and Data Warehouse Projects (DWH) are established methods and processes that enable companies to systematically collect and evaluate data in electronic form. Due to the ever shorter update intervals and the increasing amount of data and complexity in BI projects, the demands on the data warehouse system are also growing. Today more than ever, they must be able to react flexibly and agilely to these requirements.

While traditional modelling methods such as the 3NF and the star schema often reach their limits when creating agile DWH projects, Data Vault offers numerous advantages and easily breaks these limits.

As a core element of a data warehouse architecture, Data Vault integrates business objects and links them. When modelling with Data Vault, all information about a business object is divided into only a few units (entities) that are strictly separated from each other:

Entity 1: Hubs

Hubs contain key information that uniquely identifies an object.

(Identity: e.g. customer number, item number)

Entity 2: Satelliten

Satellites contain attributes that fundamentally describe an object.(Attributes: e.g. customer name, item description).

(Attributes: e.g. customer name, item description)

Entity 3: Links

Links contain information describing relationships between two or more entities.

(Relationships: e.g. assignment of a customer to a customer type).

Data Vault makes data warehouses future-proof

With traditional modelling methods, data is processed right at the beginning to make it comparable. With Data Vault, on the other hand, data is first stored raw and only transformed at the end of the development chain.

Data Valut Architektur

On the one hand, this offers the advantage that only a small part of the project has to be rebuilt if the company changes its strategy, without having to redesign the entire project, and on the other hand, it makes the data warehouse system clearer and guarantees easy maintenance of the system.


These are the advantages of switching to Data Vault:

  • Standardisation of the strategy for loading processes and selections

  • Standardisation and automation of data warehouse processes

  • Company-wide increase in efficiency as well as reduction of IT costs: A data warehouse modelled with Data Vault is extremely scalable and has a flexible architecture. This combination allows companies to grow and change without being confronted with long development cycles, enormous costs and negative effects on the existing data warehouse. Data warehouses thus gain in efficiency, stability and verifiability.

  • Maximum transparency and traceability: All data remains traceable and verifiable at all times through comprehensive historisation and holistic integration in hubs.

  • Acceleration of data integration processes: Division according to the units hubs, links and satellites. This allows the system to be populated independently for each of these units. This accelerates data integration processes and shortens loading times, making the system suitable for big data and almost real-time capable.

2BIT Team


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