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The mechanics of Data Vault

 

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  • What is Data Vault 2.0?

 

 

  • Developed by Dan Linstedt, Data Vault 2.0 is an evolutionary approach that incorporates ideas from thought leaders in the field, mathematical concepts, usage of hashing to increase performance of historization and rational development processes like Agile.  It aggregates the best practices in methodology, architecture, and modeling from experienced data architects.

At the heart of data vaulting is the data vault model, which offers a solution to the challenges faced by traditional data modeling approaches.

Traditional models, like “entreprise data warehouses” or star schemas, have limitations in terms of adaptability and ease of maintenance.

In contrast, the data vault model organizes data in three types of tables: Hub, Satellite, and Link. The Hub table stores business keys, the Satellite table contains attributes related to the data, and the Link table captures the transactional relationships between data points. This model provides a comprehensive and scalable representation of the business’s data, along with a dimensional layer for easy access by analysts and data consumers.

 

 

Data Vault 2.0 takes the data vault model to the next level. It extends beyond just a data model and incorporates the architecture and methodology described above.

 

 

  • – Data is loaded in parallel, and business rules are tracked over time, allowing for retroactive analysis and version control.
  • – When new data sources emerge, they can be seamlessly integrated into the existing structure without the need for rework or re-engineering.

 

 

Employing Data Vault 2.0 is the least risky way to build a robust and adaptable data platform. It offers a prescriptive methodology for executives, business managers, analysts, and data consumers to deliver business value from the data platform efficiently. For practitioners and managers responsible for data platforms, Data Vault 2.0 provides a comprehensive and resilient approach to building and deploying a functional model and scalable architecture that can withstand the challenges posed by evolving business needs.

 

 

 

 

 

 

Data Vault 2.0 and automation

 

 

  1. Data Vault 2.0 (DV2) can be automated, while Data Vault 1.0 (DV1) had limitations in terms of automation, primarily due to the lack of standardized specifications. Let me explain why.

 

 

  1. Automation in the context of data modeling and implementation refers to the ability to use software tools and processes to streamline and expedite the creation and management of data structures. It allows for consistent and efficient development, reduces manual effort, and minimizes the risk of errors.

 

 

  1. DV2 benefits from increased automation because it provides a more standardized and well-defined methodology compared to DV1. In DV2, the specifications and best practices for modeling and implementing a Data Vault are clearly outlined. The Data Vault 2.0 Standard serves as a comprehensive guide that includes methodology, architecture, and modeling practices.

 

 

  1. The clarity and standardization of DV2 make it easier to automate various aspects of the Data Vault implementation process. With a well-defined set of rules and guidelines, software tools can be developed to generate compliant Data Vault structures automatically. These tools can create Data Vault objects, such as hubs, satellites, and links, following the prescribed patterns and rules.

 

 

  1. In contrast, DV1 did not have as explicit and widely accepted specifications. Different practitioners had their interpretations and approaches to building Data Vault models, which led to variations in compliant Data Vault implementations. The lack of a standardized framework made it challenging to develop automation tools that could cater to the diverse ways of implementing DV1.

 

 

  1. The introduction of DV2 and its standardized approach resolved this issue. It brought together the collective knowledge and experiences of data architects and practitioners who had worked on Data Vault projects over the years. By consolidating best practices and providing clear guidelines, DV2 created a foundation that could be leveraged for automation.

 

 

Data Warehouse and Datalake automation

 

 

Data warehouses and data lakes have the option to embrace Data Vault 2.0 (DV2) as their chosen data modeling and implementation methodology. By adopting DV2, they can ensure the full scalability of their data models for the future and enable their data warehouses or data lakes to become sustainable in the long run. Let’s explore this further.

 

 

Data warehouses and data lakes serve as central repositories for storing and analyzing large volumes of data. They provide a foundation for reporting, analytics, and data-driven decision-making within organizations. The data models used in these systems play a crucial role in organizing and structuring the data for efficient querying and analysis.

 

 

DV2 offers several benefits that make it an attractive choice for data warehouses and data lakes looking for a sustainable and scalable data model.

 

 

  1. Scalability: DV2 is designed to accommodate the ever-changing needs of businesses as they grow and evolve. The flexibility and adaptability of the Data Vault model allow for easy incorporation of new data sources, system changes, and expanding business requirements. This scalability ensures that the data warehouse or data lake can effectively handle increased data volumes and complexity over time.

 

 

  1. Future-proofing: By adhering to the DV2 methodology, data warehouses and data lakes can future-proof their data models. DV2 follows a set of best practices and standardized guidelines that have been refined over years of industry experience. This approach minimizes the risk of creating data models that may become obsolete or difficult to maintain as business requirements evolve.

 

 

  1. Consistency and Reusability: DV2 promotes consistency in data modeling by providing a clear set of rules and patterns. This consistency makes it easier to understand and work with the data model across different teams and projects. Additionally, the standardized nature of DV2 enables the reusability of data vault objects, such as hubs, satellites, and links, which can significantly reduce development effort and enhance data integration and consolidation.

 

 

  1. Reduced Maintenance and Complexity: DV2 simplifies data modeling and management by separating the concerns of the data vault, business vault, and presentation layers. This separation reduces the complexity of the data model and makes it easier to maintain and modify over time. By embracing DV2, data warehouses and data lakes can achieve a more streamlined and sustainable architecture, leading to lower maintenance costs and improved data quality.

 

 

DWH and DL can choose to embrace Data Vault 2.0 to ensure the scalability and sustainability of their data models. DV2’s scalability allows for accommodating future growth and changing business requirements. By following standardized best practices, data warehouses and data lakes can create consistent and reusable data models. Embracing DV2 also reduces maintenance efforts and complexity, leading to more sustainable and efficient data management practices.

Other posts that might interest you

dFakto: Leading the Way with the Data Vault 2.0 Certification from the Data Vault Alliance
Why do data warehouses take forever?
dataFaktory, all benefits in the hands of business users

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