Tagged: Data Vault 2.0

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Loading too fast for unique date/time stamps – what to do?

Let’s start by clarifying that this concerns the RDBMS world, not the Hadoop world 😉 It’s a good problem to have – loading data too quickly. So quickly that, even at high precision, multiple changes for the same key end up being inserted with the same Load Date/Time Stamp (LDTS). What happens here? A quick recap: in Data Vault the Load Date/Time Stamp (LDTS, LOAD_DTS, or INSERT_DATETIME) is defined as the moment data is recorded...

 
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NoETL – Data Vault Link Satellite tables (part 2)

This is the second part of the Link Satellite virtualisation overview (the first post on this topic is here), and it dives deeper into the logic behind Driving Key based Link Satellites. Driving Key implementation is arguably one of the more complex things to implement in Data Vault – and you (still) need to ensure you can cover reloads (deterministic outputs!), zero records / time variance and things such as re-opening closed relationships. In the example...

 
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Virtualising your Data Vault – regular and driving key Link Satellites

Virtualising the EDW core integration layer by applying Data Vault concepts turned out to be a very useful and achievable exercise. So achievable even, that it only requires three posts to present an idea on how this all works. The Hubs and Links are already covered in the first post, and the Satellites in the second. It’s now time for the remaining primary entities: the Link Satellites. What’s the driving key? As explained in this post...

 
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Data Vault 2.0 – how to handle Referential Integrity?

I was working on adding some of the automation code to support Data Vault 2.0 and this got me thinking about Referential Integrity (RI)  related to the modifications that Data Vault 2.0 requires. With Data Vault ‘1.0’ Referential Integrity is always enabled (except for very big systems – let’s leave that one out of the scope for now – see this older post) and in Data Vault 2.0 this hasn’t changed according to the specifications. For Data...