Complete Guide to Data Vault 2.0: A Revolutionary Approach to Data Architecture
The data landscape has changed significantly in recent years. With the emergence of new technologies and the growing need to deal with massive amounts of information, especially from multiple sources, data architectures have evolved to meet these demands. One of these notable developments is Data Vault 2.0, a robust and flexible approach to data modeling that addresses today’s challenges faced by organizations around the world.
Origin & Description
Data Vault was conceived by Dan Linstedt in the mid-2000s in response to the data management problems facing organizations. Version 1.0 of Data Vault brought innovations in modeling and standardization, but the recently released version 2.0 has improved and refined the original concepts to make them even more suitable for complex and dynamic data environments.
Data Vault is a data modeling methodology that focuses on three key pillars: flexibility, scalability, and resilience. It is designed to handle large volumes of data from a variety of sources while ensuring that the data remains consistent, auditable, and ready for analysis.
Differences with Version 1.0
Although the essence of Data Vault remains the same between versions 1.0 and 2.0, the latest version introduces some significant improvements:
- Simplicity: Data Vault 2.0 simplifies some aspects of modeling compared to the previous version, making the process more agile and accessible.
- Standardization: Version 2.0 defines clearer standards for modeling, loading, and maintaining data, making Data Vault easier to implement and maintain.
- Support for new technologies: Data Vault 2.0 is designed with emerging technologies such as cloud computing and big data in mind, ensuring its relevance in modern data environments.
Modeling
Modeling in Data Vault 2.0 is based on three main types of tables: Hubs, Links and Satellites:
HUB
They store unique business keys and are the basis for relationships between data.
As a good practice, I suggest saving the data source: Rec_SRC.
Links
They manage the relationships between the modeled entities, connecting the hubs.
Satellites
They store contextual and historical attributes about the data, making it possible to track changes over time.
The detail shows that the data on the satellites is only included. Never changed.
This modular and flexible approach makes it easy to adapt the model to changing business requirements and new data sources.
Load Patterns, Staging and Information Delivery
In Data Vault, loading patterns are designed to ensure data integrity and consistency. The loading process generally involves three main stages:
- Staging: The data is extracted from the sources and loaded into a staging area, where it is prepared for transformation and loading into the Data Vault.
- Load: The data is transformed and loaded into the Hubs, Links and Satellites tables according to the integrity and consistency rules defined in the model.
- Information Delivery: The data is made available to end users through presentation layers, such as data marts or data warehouses, where it can be viewed and analyzed to obtain business insights.
Indications for use and problems it solves
Data Vault is especially suited for organizations that deal with large volumes of data from multiple sources, such as social networks, IoT sensors, commercial transactions, among others. It solves a number of common problems faced by these organizations, including:
- Difficulty integrating data from heterogeneous sources.
- The need to track and audit data changes over time.
- Scalability requirements to cope with continuous data growth.
- Flexibility to adapt the data model to new business requirements.
When it should be used and alternatives
The Data Vault is best suited for scenarios where flexibility and scalability are priorities, such as organizations operating in highly dynamic environments or dealing with large volumes of varied data. However, other approaches may be considered, depending on the specific requirements of the project:
- Data Lakehouse: Data Vault can be combined with Data Lakehouse architecture, taking advantage of both, such as the flexibility of Data Vault and the scalability of Data Lake.
- Medallion Architecture: While Data Vault focuses on data modeling, Medallion architecture addresses broader aspects of data governance and integration, and can be used together to create a comprehensive data management solution. Data Vault fits perfectly into the Silver layer of the architecture, but is not limited to it.
In summary, Data Vault is a powerful data modeling methodology that offers flexibility, scalability and resiliency to organizations facing complex data management challenges. When combined with other technologies and approaches, organizations can build robust and adaptable data architectures that drive innovation and business growth.
