Unlocking Data Vault: best practices for success

In our last blog post, we discussed different data modeling concepts. One of them especially has garnered a lot of attention in the past decade. Dan Linstedt developed the Data Vault concept in the 1990s as a response to the limitations of traditional data warehousing techniques. Initially introduced as a way to address challenges in data integration and agility, Data Vault gained recognition for its modular and scalable architecture. The approach has evolved over the years incorporating best practices and lessons learned and is currently applied as Data Vault 2.0. In 2024, this data modeling approach continues to gain tremendous popularity as a way of managing and structuring data warehouses in complex and dynamic environments. But what is Data Vault all about? Let’s dive a bit deeper in this blog post.

Schema structure

Where the traditional modeling ideas of Kimball or Inmon provide easy access to understanding and leveraging the schemas for reporting & analytics, the data vault model itself is not that accessible to the untrained person. On the flipside, Kimball & Inmon make it more difficult to deal with substantial changes. This becomes especially pronounced when you have to deal with larger organizational changes.

Data Vault on the other hand shows its strengths in this type of environment. It does provide is a super robust foundation that is open for changes and the capture of historical data. However, the schema itself is not easily accessible for untrained people. Organizations therefore organize their data vault implementation around the core data vault schema and one or more publish layers on top  . You can view the core schema as an insulation layer for corporate changes. Historical data is protected and you can adapt the changes in the publish layer(s).

Here is a powerful example: a company went through a major business reorg shifting from a traditional profit center view to a matrix organization. The core schema remained stable with few changes. Most adjustments were done at the publish layer. Reporting historical data was still possible.

Building Blocks: Hubs, Satellites, Links

How do you build a data vault schema? The architecture is built upon three types of tables. Each one of these plays an important role:

1. Hubs : Hub tables serve as the central repositories for specific business concepts such as customers, products, orders. These hub tables store business keys and represent the foundational layer for organizing and categorizing data. A typical schema would include hub tables for customers, products, orders, etc ..
2. Links: Links establish relationships between hubs, capturing the connections and interactions between different entities. By defining these relationships, links enable a more comprehensive understanding of the data ecosystem.
3. Satellites: Satellites contain the descriptive attributes associated with hubs and links, providing contextual information and historical data. Satellites play a crucial role in preserving the integrity and lineage of the data.

Once again, the key strength of this approach is its ability to facilitate incremental changes and updates without disrupting the entire system. Organizations find that this can make a significant contribution towardseasier maintenance and evolution of the data warehouse over time. The initial learning curve might be a bit steeper but it’s worth if you operate in a specific environment.

When to use Data Vault

Let’s face it – we all love easy solutions. The reality is, that there are no easy solutions for complex problems. If your organization is stable and few organizational changes are anticipated, Data Vault might be too heavy of an approach for you. The following situations tend to be a good setup for this approach:

1. Complex Data Environments: Look to data vault when data sources are diverse and complex. Whether dealing with multiple systems, varied data formats, or evolving business requirements, Data Vault's flexible architecture can adapt to accommodate diverse data landscapes.
2. Agile Development: Organizations adopting agile methodologies for software development can benefit greatly from Data Vault 2.0. Its modular structure aligns well with iterative development practices, allowing teams to make incremental changes and enhancements without causing extensive rework. You can also break up work packages along specific business concepts.
3. Compliance and Auditing: In industries with stringent regulatory requirements such as finance, healthcare, or government, maintaining data integrity and auditability is paramount. Data Vault's built-in mechanisms for tracking changes and preserving data lineage make it an ideal choice for compliance-driven environments.
4. Scalability: As data volumes continue to grow exponentially, scalability becomes a crucial consideration for data warehousing solutions. Data Vault's ability to scale horizontally by adding additional hubs, links, and satellites ensures that it can accommodate increasing data loads without sacrificing performance.
5. Data Quality and Consistency: By separating business keys from descriptive attributes, Data Vault promotes data consistency and quality. This separation reduces the risk of data anomalies and ensures that the integrity of the data is maintained throughout its lifecycle .

Is Data Vault a silver bullet?

While data vault is a great idea in many environments, it is not a silver bullet for every organization. There is certainly a learning curve that needs to be factored in your project plan. Also, in some cases data vault might be too complex of a solution. A straight up Kimball model might be a better solution for stable and smaller environments. Without any specialized data warehouse automation tools, Data Vault can be notoriously difficult to implement and operate. Agile Data Engine, for example, provides rich functionality to make Data Vault work seamlessly in your environment.

How to get started

The experienced team at Agile Data Engine has paired up with Inform DataLab to develop a unique hands-on workshop to get you started with Data Vault. Through the course of a day, you can learn the base concepts and their application in real life. Where the workshop differs from others is the hands-on format. All participants will get to practice the newly acquired knowledge and build an actual data model in various exercises. At the end of the workshop, you will leave with a small sample model.

If you want to read this blog post in German, schau Dir diesen und andere Blog Posts auf der Webseite unseres Implementierungspartners Inform Datalab an.