Non-Historized Links in Data Vault 2.0

Non-historized links in Data Vault 2.0 simplify data modeling by focusing on current relationships instead of historical changes. This improves efficiency, reduces complexity, and enhances query performance for more agile analytics.

Introduction to Non-Historized Links

Non-historized links, also known as Transaction Links, are one of the nuts and bolts of the Data Vault 2.0 framework, but how do they work within a model? When Dan Linstedt, co-founder of Scalefree, invented the Data Vault, he had several goals in mind. One of the goals was to load data as fast as possible from the source into a data warehouse model, process it into information, and present it to the business analyst in any desired target structure.

For Data Warehouse automation and simplicity, the Data Vault 2.0 model exists only of three basic entity types:

1. Hubs: a distinct list of business keys
2. Links: a distinct list of relationships between business keys
3. Satellites: descriptive data, that describe the parent (business key or relationship) from a specific context, versioned over time.

In this example, Sales can be modeled with non-historized links that capture sales transactions of a customer, related to a store. The goal of the non-historized link is to ensure high performance on the way into the data warehouse and on the way out. Don’t forget, that the ultimate goal of data warehousing is to build a data warehouse not just model it. And building a data warehouse involves much more than just the model: it requires people, processes, and technology.

Key Characteristics of Non-Historized Links

So, how do non-historized links meet these goals? Think about your business analysts. What are their goals? In the end, to be honest, they don’t care about a Data Vault model. Instead, they would like to see dimensional models, such as star schema’s and snowflake models or flat-and-wide models for data mining. Or, once in a while, they want to see the ugly-looking tables from the mainframe, sometimes linked, sometimes not, and not a lot of people fully understand the relationships anymore…but for backward compatibility, that’s just great.

Having defined the target, the next question comes into mind: what is the target granularity? For example, in a dimensional model, the target granularity of fact tables often reflects the transactions to be analyzed (think about call records in the telecommunications industry or banking transactions).

Interestingly, this desired target granularity can often be found directly in the source systems. Because a telecommunications provider has an operational system in place that records each phone call. Or a banking application that records every account transaction. These records are typically loaded to the data warehouse without aggregation (at least in Data Vault where we are interested in the finest granularity for auditing and delivery purposes).

And here comes the problem with the standard Data Vault entity types. While they are very simple and patternized, they have one problem. It’s the fact that the standard link “stores a distinct list of relationships”, as stated above. That means the link is only interested in relationships from the source that are unknown to the target link. If a customer walks into a store multiple times and purchases the same product, the relationship between the customer (number), store (number), and product (number) is already known and no additional link entry is added.

As a result, the granularity of the incoming data is changed when loading the target link. If the transaction’s underlying relationship is already known, the transaction would be omitted (and instead captured by a satellite).

The next problem is that the link granularity now differs from the target granularity because the business analyst wanted one record per transaction and not per distinct business key relationship. Another grain shift is required that typically involves joining the satellite of the link to the link itself to recover the original grain.

As we explained in our book “Building a Scalable Data Warehouse with Data Vault 2.0” a grain shift is relatively costly in terms of performance. This is because the operation requires costly GROUP BY statements or LEFT and RIGHT JOINS.

And for what? The end result of both operations often results in the original granularity from the source system. Two expensive grain shifts for nothing sounds like a bad deal.

And it is.

That is where non-historized links come into play: the link is a simple variation of the standard link with the goal of capturing the source transactions and events at the original granularity.

Figure: Non-Historized Link with Sales ID as an additional key

One deeper scenario would be, if the same product (in that case the highest granularity) emerges twice in a sale, because of different discounts for example. In that case, the line item number would be an additional key (Dependent Child Key) to make every data set unique.

For performance reasons, avoid grain shifts on the way out. If a different target granularity is required, and it is not possible to load this granularity from the source system or from the Data Vault model, consider a bridge table. The purpose of this entity type is to materialize the grain shift while keeping the advantage of customizing the target according to the individual requirements set for the dimensional target.

Figure: Virtual Bridge Table as Fact Table with GROUP BY Store Hash Key

Conclusion

Non-historized links offer a streamlined approach to Data Vault modeling, optimizing performance and simplifying data structures. By focusing on current relationships rather than historical changes, they enhance efficiency, reduce storage needs, and improve query speed. Incorporating them into your data architecture can lead to a more agile and scalable analytics environment.