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Data Vault Industry Verticals

Last year, I was asked to review a data vault interpretation of an industry data model.

If you’re in the field, you know that there is really no such thing as a data vault industry model. The data vault model itself lends its flexibility to suite any industry or data modelling layer pattern. Upon review, however, I did see a lot of similarities between the industry data model and data vault calling out things like agreements (aka contracts), persons, and accounts. It really resembled a Level 2 model — but there were some oddities. I list them here.

Follows the Standard

The usual hubs, links, satellites, and reference tables are separate and loaded as slowly changing dimension (SCD) Type 2 tables without additional links to those reference tables. Reference codes are treated as dependent-child keys in some of the link tables. Some of the reference tables are in fact role-playing dimensions as its code-value is reused in multiple links.

Tenant id

It is based on a code tied to a department or organization. The same data model structures can be used to host multiple tenants, but the content remains only available to certain tenants. The extension of this leads to views and query assistance table structures that filter by tenant id, and restricting the joins ensures that you only return the records applicable to that tenant.


The source notifies of record deletion. Since the source supplies this, it should remain with the rest of the satellite content. It doesn’t mean we delete the data in the data vault. We simply record that the record was deleted in the source — by virtue of the record being the current active record in data vault, we know it is deleted.

Deviates from the Standard

The data model understood the potential for business object collisions between integrated sources but chose to use the source-system-code as a tie breaker. This was close to being a business key collision code except that the model had it tied to the source that supplied the key by default.

This is a problem and an improper use of the passive integration concept. If source systems do share business keys, then you have illogically separated them in the loaded hub, meaning you need some integration debt (additional logic) or tables to decipher this integration debt your data model introduced!

The data model understood the concept of a dependent-child key but decided to put that dep-key into the hub table itself! Now, this is not a composite key but a sub-category key of the business object itself. It is a type-column, but it means nothing without its parent key. It obscures the meaning of the hub definition and should not be loaded into the hub!

Raw satellite tables weren’t raw. Well, the data model is an industry data model, and it deliberately chooses to mould the various sources into a single satellite table per information type. The danger here, of course, is that should one of the sources experience schema drift, the mappings could need an update, or worse the grain would no longer match the rest of the satellite table source.

And what if both sources provide the same column name to be loaded to the same satellite but, in fact, have different interpretations of what gets populated in there? What if one of the sources does not supply a value for delete_ind? Data vault is sold as not having to refactor modelled data. This pattern inevitably would.

Further Advice

The modeller did seek advice about testing and querying their data vault-styled data model and here is a summary of the advice given:

  • Testing should (like DV) be based on a repeatable template and it should be simple and insert only. To my mind, there are two kinds of automated testing that serve slightly different purposes.

— Pro-active testing based on a warranty — test what you will load won’t break the model, these are:

§ Vertical testing: Does the act of hashing within the staged table create integrity issues between surrogate and business key? Simple test within staging: does the same tenant-id + business key collision code + business key(s) generate more than one surrogate hash key value? And does a single hash key value relate to more than one set of tenant-id + business key collision code + business key(s)? The same check can be applied to the link-hash-key and the columns used to create the surrogate link-hash-key.

§ Horizontal testing: It checks that, for the same tenant-id + business key collision code + business-key(s) combination, it does not generate a surrogate hash key different from those of the target data vault artefact, hub-hash keys, and link-hash keys.

— Reactive testing should run forever and ensure that the trust and integrity of what is in the data model matches up. Against the three data vault artefact types there are three such tests.

§ Have I now got duplicates after the load (needed if your db-platform does not enforce primary keys)?

· Hub duplicates by surrogate hash key.

· Link duplicates by surrogate link-hash key.

· Satellite by parent surrogate hash key and current load date.

§ Does the staged content reconcile with the target data model artefacts?

· Are my keys loaded to the hub(s) one or many?

· Are my keys loaded to the link(s) zero or many?

· Are my attributes loaded to the satellite(s) zero or many?

§ Do my links and satellites reconcile by parent key to their parent entities?

· Hub satellite to hub table.

· Link satellite to link table.

· Link table to one or more hubs.

— Periodic-integrity testing: Can I recreate my source file at any point in time? Don’t run this everyday but do run it on a schedule, say once a week or month. Special consideration is needed for relationships within a link that can revert to a previously captured relationship and no business event date is supplied for that change. If this is true in your data model then the driver-key + effectivity satellite (or record tracking, or status tracking) is applicable. By the way, anyone who claims you can add an effectivity satellite to a hub table does not understand how to build one.

  • Data Quality, there are two such DQ tests for this and they are:

— Technical, schema tests check for domain values and other expected value tests on an individual table, likely satellite attributes.

— Business process tests check that the source applications that map and automate business processes are meeting the required service level agreement (SLA).

Both such tests can and should be persisted to business vault satellites and show as a measure of the source application health!

  • Querying: Data Vault has many tables to join to, the two prominent query assistance (disposable) tables are:

— Point-in-time (PIT) tables: used to simplify and improve query join performance around a hub or link.

— Bridge tables : used to shorten the distance travelled between a hub at one end of the data model and a hub at the other end of the data model.

Both are information mart focused and should be short and thin and filtered to suit the use case.


Aside from the oddities that fail DV standards, could there be a place for industry hub tables? Industry reference models are worked on and managed by business architectures and business architecture professionals as a business recognizes business objects. These business objects are mapped as information concepts that fit and map the organization’s business processes and value streams.

So, come to think of it, it is not the data vault model that could be industry related but the hub table naming standards themselves. After all, the business key is what uniquely identifies a business object.




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Patrick Cuba

Patrick Cuba

A Data Vault 2.0 Expert, Snowflake Solution Architect

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