Although 37 % of organisations use centralised data warehouses, there is a significant gap between implementation and effective management of the growing volumes of data. Traditional data modelling techniques often struggle with changing business requirements and data integration. But what if there was a way to design a data warehouse that was flexible, scalable and future-proof? 

Data Vault is an innovative approach to data modelling that is becoming increasingly popular as it can handle complex data environments. Once you know the benefits of Data Vault, you will understand how it can be the perfect solution for your organisation.

What is a data vault? 

Data Vault is a Method for data modellingwhich was developed by Dan Linstedt in the 1990s. It was developed for the construction of flexible Data warehouses developed, the heterogeneous Data from different sources while maintaining data integrity. It is ideal for the long-term storage of historical data and can be easily adapted to new data sources and business requirements.

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3 types of Data Vault modelling units

The main strength of a Data Vault model lies in its three basic entity types: Hubs, Links and Satellites. Each of these types plays a specific role in the storage and organisation of data. Let's discuss them in detail:

1. hubs (nodes)

Hubs are the central pillar of your data vault, capturing the unique business keys (e.g. customer ID, order ID) and the associated metadata. They are the Central reference points for linking other tables (e.g. satellites and links) to ensure the consistency and integrity of the entire data warehouse.

Hubs usually contain slowly changing dimensions (SCDs). This means that their core attributes, such as customer ID or product code, remain relatively stable over time. However, the model allows new descriptive attributes to be added to a hub as business requirements evolve.

2. links (shortcuts)

Links serve as bridges between the hubs in your Data Vault. They establish relationships between different entities and enable you to understand their interaction and the data flow in your system.  

For example, a link table could connect the customer hub with the product hub and show which products each customer has purchased. The links contain foreign keys that refer to the primary keys of the connected hubs.

3. satellites

Save satellites Descriptive attributes and context for hubs and links. Unlike hubs, satellites are very volatile and can change frequently when new data arrives. They contain the basic details about your business processes, such as transaction data, order quantities or sensor readings. 

Satellites usually contain foreign keys that refer to the corresponding hub or link, as well as descriptive attributes for the contained data.

Differences between Data Vault 1.0 and Data Vault 2.0

The original Data Vault methodology, often referred to as Data Vault 1.0, provided a solid foundation for building flexible and scalable data warehouses. However, as data ecosystems became more complex and data volumes exploded, an improved version was developed: Data Vault 2.0. Although both versions have the same basic principles, Data Vault 2.0 offers Data Vault 2.0 important improvements for managing modern data requirements.

Here you will find a detailed table comparing the similarities and differences between the two versions:

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Differences between Data Vault and Data Mesh

Data Vault and Data Mesh are gaining ground in the field of data management, but they deal with different aspects of data architecture. Below is a breakdown of the key differences and how they can complement each other.

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Advantages of a data vault

As data volumes grow, a data warehouse needs to be more than just a static storage location. Data Vault offers a compelling approach that emphasises flexibility, scalability and the ability to handle change. Below are some of the key benefits that a Data Vault model offers for your data warehouse:

Agility and adaptability

The biggest advantage of a data vault is its adaptability to changing data sources and business requirements. Unlike traditional data models, which can become rigid and require significant rework when new data is introduced, the Data Vault's non-volatile design allows for the smooth integration of new data sources without changing the existing structure. This makes it ideal for organisations with evolving data ecosystems or those anticipating future growth.

Simplified data integration

Integrating data from different sources can be a complex challenge. The Data Vault focuses on the retention of historical data and ensures that all incoming data is captured exactly as it was received. This eliminates the need for complex data transformation upfront, simplifying the integration process and reducing the risk of errors.

Improved data linking and verifiability

With a data vault, the origin of all data can be clearly traced. You can easily trace their origin and all the transformations they have undergone. This is crucial for complying with legal regulations and ensuring the security of your data. Data quality. In addition, the history of the data vault enables a look back at past data points, which can be very useful for trend analyses and forensic investigations.

Scalability and performance

A data vault is designed to process large and growing amounts of data. The use of hash keys in Data Vault 2.0 improves query performance and simplifies parallel processing, making it efficient for managing large amounts of data. In addition, the modular design allows for easy expansion as data storage needs increase.

Reduced development time and costs

The Data Vault's standardised approach and focus on simplicity can lead to faster development times for your data warehouse. The modular structure enables the parallel development of different data domains, which further speeds up the process. In addition, data vaults can help reduce the overall cost of data management by simplifying data integration and reducing the need for complex transformations.

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Challenges and considerations

Data Vaults offer various benefits for organisations, but there are also some challenges and considerations associated with them:

• Initial investmentImplementing a Data Vault model may require an initial investment in training and possibly new data management tools. This can be challenging for organisations with limited budgets or resources due to the upfront costs and time required to train staff.
• Complexity of the designWhile the core concepts of data vaults are relatively easy to understand, developing a complex data vault model requires expertise in data modelling best practices. A lack of internal expertise can lead to inefficiencies or a sub-optimal data vault implementation.
• Ensuring data qualityThe Data Vault is great for capturing all incoming data, but it does not cleanse or transform it. The implementation of data quality checks and processes is still crucial.

Why should companies use Data Vault?

Despite these considerations, Data Vault offers remarkable advantages to companies that want to build a future-proof data warehouse. Due to its flexibility, the focus on Data governance and the efficient handling of large amounts of data it is suitable for companies in various industries. 

A data vault is a compelling approach to consider if your organisation:  

• has problems with the integration of data from different sources
• anticipates an evolving need for data
• requires a scalable and verifiable data basis

Data Vault provides a powerful and adaptable approach to data warehousing for your organisation. With its core principles of historical data retention, non-volatile design and focus on integration, Data Vault is ideal for organisations faced with evolving data sources, complex data ecosystems and the need for scalability. By taking advantage of Data Vault, you can build a data warehouse that is flexible, auditable and enables data-driven decisions across your organisation.