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The Data Vault Guru: A Pragmatic Guide on Building a Data Vault

Why Write a Book?

What Is in it for the Reader?

Who would benefit?

Where can you find the book?

Where to get more Data Vault?

  • Data Vault 2.0 on Snowflake…To hash or not to hash… that is the question — an idea on how to deploy data vault in Snowflake given that Snowflake does not have indexes,
  • Data Vault Mysteries… Effectivity Satellite and Driver Key — the two concepts are intertwined, the article explores where they fit, the book contains the SQL on how to build it, and even query it,
  • Data Vault Elevator Pitch,
  • Data Vault Mysteries… Zero Keys & Ghost Records — a Data Vault 1.0 practitioner are not familiar with this concept or the difference between the two, the article dives into their place and the book provides SQL on how to implement them,
  • Seven Deadly Sins of Fake Vault — an article designed to stir controversy between DV1.0 and DV2.0 practitioners. On the one hand you have modelling designed by a community not cognizant with the original intentional of the methodology, on the other you have a methodology designed to scale, the book is based on a methodology that scales
  • Data Vault Mysteries… Business Vault, a common question when delivering a data vault is where the BV fits in, how to build it. Yes it doesn’t just exist, it must be designed and built, just like the source applications that populate raw vault. The book goes deeper into actual implementations of a BV,
  • A Rose by any other name… Wait.. is it still the same Rose? The importance of passive integration is highlighted here, and how to apply it is described in the book
  • Apache Spark GraphX and the Seven Bridges of Königsberg, the right tool for the right job is highlighted in this post. The article dives into how a business requirement for recursion that scales was delivered on Big Data, to be honest it would have been much simpler on a platform that provided the functionality we needed! Here we explore this journey and how the outcome was delivered in Business Vault,
  • Data Vault has a new Hero, this is the pattern getting a lot of attention in the data vault community now, XTS. It is (at the time of writing) something being considered in dbtvault, a data driven approach to timeline-correction! It is not without guardrails / caveats! The article demos the use-case, the book dives into a lot more with advanced scenarios and the SQL to build your own XTS,
  • Bring out your Dead… Data,
  • What does dbt give you?,
  • Passive integration explained…,
  • How I can get away without paying the Pied Piper… in Data Vault 2.0,
  • Advantage Data Vault 2.0, a straight up comparison to DV1.0 and why it would not scale,
  • Building Data Vault modelling capability through the Mob, this is a quick and effective methodology to deploying a Data Vault 2.0 at scale, the book dives into more and more tools at your disposal to define a governance council that focuses on rapid deployment of Data Vault 2.0 models,
  • Learning Data Vault is Like Learning How to Make Beer! Why not? DV2.0 is not that scary, the book compares learning data vault 2.0 to photography instead! Like beer, photography only has three key elements to learn that drives everything you need to know about photography. Master those and every variation of photography is based on those three key elements!
  • Data Vault or: how I learnt to stop worrying and love Data Governance, a short take on the DataOps movement and how Data Vault 2.0 was already geared to deliver this,
  • Business Key Treatments, marry this content to the idea behind passive integration and you can see why this approach is invaluable. Row-level business key treatment assignment is possible in a data-driven approach that ensures the raw vault continues to integrate data across source systems,



Snowflake articles from engineers using Snowflake to power their data.

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