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Why Hubs in Data Vault are Essential
Data Vault modeling is a powerful methodology for building robust and scalable data warehouses. One of its core components, the Hub, often raises questions among practitioners and stakeholders. Why do we need hubs? Can’t we just simplify the model by putting business keys directly into satellites? In this article, we delve into the reasons behind the existence of hubs and explore scenarios where deviating from the standard practice might be acceptable.
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The Role of Hubs in Data Vault
Hubs play a pivotal role in Data Vault by storing a distinct list of business keys. These keys serve as unique identifiers for real-world entities, such as customers, products, or employees. Hubs provide several critical benefits:
- Data Integration: Hubs act as anchors for integrating data from disparate source systems. By consolidating different representations of the same entity into a single hub, you ensure consistency and accuracy across your data warehouse.
- Scalability: Hubs facilitate seamless scalability. When new data sources are introduced, you can simply add the business keys to the existing hub without the need for major model refactoring. This simplifies the onboarding of new data and reduces the risk of introducing inconsistencies.
- Auditability: Hubs maintain a clear lineage and audit trail for your data. The load timestamp in a hub functions as a “first seen” date, making it easy to track the evolution of your data over time.
- Granularity: Perhaps most importantly, hubs define the granularity of multiple downstream objects, including information marts and dimensions. This granularity is crucial for accurate reporting and analysis, making hubs indispensable for many use cases.
Why Not Put Business Keys in Satellites?
While hubs are generally considered best practice, there are rare instances where storing business keys in satellites might be justifiable. One such scenario is when a business key represents an entity that currently lacks descriptive data and is not actively queried.
For example, consider an employee dataset that includes the vehicle identification number (VIN) of the employee’s company car. If there’s no additional information about the car and no immediate need to query it, treating the VIN as a descriptive attribute within the employee satellite might be acceptable.
However, if the need to query or analyze data related to company cars arises in the future, a refactoring strategy called “Hub It Out” can be employed. This involves extracting distinct VIN numbers from the employee satellite into a new hub, creating links between the employee and car hubs, and potentially adding satellites with descriptive data about the cars.
Important Considerations
While the above scenario demonstrates a valid exception, it’s crucial to remember that storing business keys in satellites should be the exception, not the rule. Hubs offer numerous benefits in terms of data integration, scalability, auditability, and granularity, making them essential for most Data Vault implementations.
Before deviating from the standard practice, carefully assess whether the potential benefits of storing business keys in satellites outweigh the potential drawbacks, such as increased storage costs, redundancy, and a less elegant data model.
Schlussfolgerung
In conclusion, hubs are fundamental building blocks in Data Vault modeling, providing a range of benefits that contribute to the overall integrity, scalability, and usability of your data warehouse. While there are rare cases where storing business keys in satellites might be justifiable, it’s crucial to carefully weigh the pros and cons before adopting this approach. By adhering to Data Vault best practices and understanding the specific requirements of your use case, you can ensure that your data warehouse is optimized for performance, maintainability, and long-term success.
Treffen mit dem Sprecher
Julian Brunner
Julian Brunner arbeitet als Senior Consultant bei Scalefree und hat Wirtschaftsinformatik und Betriebswirtschaft studiert. Seine Schwerpunkte liegen in den Bereichen Business Intelligence, Data Warehousing und Data Vault 2.0. Als zertifizierter Data Vault 2.0 Practitioner hat er 5 Jahre Erfahrung mit Business Intelligence Lösungen und Data Warehouse Entwicklung mit den Data Vault 2.0 Standards. Er hat erfolgreich Kunden aus dem Banken- und Beratungssektor beraten.
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