Roland Bouman's blog: Do we still need to talk about Data Vault 2.0 Hash keys? A few days ago, I ran into the article "Hash Keys In The Data Vault", published recently (2017-04-28) on the the Scalefree Company blog.
Scalefree is a company, founded by Dan Linstedt and Michael Olschminke. Linstedt is the inventor of Data Vault, which is a method to model and implement enterprise data warehouses. The article focuses on the use of hash-functions in Data Vault data warehousing. To be precise, it explains how Data Vault 2.0 differs from Data Vault 1.0 by using hash-functions rather than sequences to generate surrogate key values for business keys. In addition, hash-functions are suggested as a tool to detect change of non-business key attributes to track how their values change over time. Abstract First I will analyze and comment on the Scalefree article and DV 2.0, and explain a number of tenets of DV thinking along the way. A Summary of the Scalefree article I encourage you to first read the original article. Flashback a few years ago Objections Distracting Rhetorics with n m.
Data Vault Modeling Increase Data Warehouse Agility. What is Data Vault?
When talking about data modeling for data warehousing, most organizations implement either a dimensional (Ralph Kimball ) or normalized (Bill Inmon ) modeling techniques. Both approaches have been around for more than 20 years and have proven their practical use over time. In the last several years, however, market forces have made it imperative for business intelligence and analytics processes to become more agile. This trend comes with many challenges. One major issue is that dimensional and normalized data models are not built for rapid change. These types of problems have spurred interest in the Data Vault approach. “… It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema. The underlying idea of Data Vault is to separate the more stable part of entities (business keys and relations) from their descriptive attributes. Figure 1. Hans Hultgrens Präsentationen auf SlideShare.
Lean Data Warehouse via Data Vault. Thoughts on Data Vault vs. Star Schemas. I am back in Belgium to deliver some training and do a bit of consultancy.
Since that leaves me at yet another hotel room, I might as well share some thoughts on something I have noticed over the past year or so and which I also noticed during my last visit here. Thus in continuation of my last post, I will share one more observation from the Data Warehouse automation event in Belgium: Data Vault is a hot topic in the Benelux region and was part of almost every presentation. This is a distinct contrast to what I experience during my travels to the rest of Europe, USA, Canada, South Africa, Iceland and India where it is hard to find a customer, BI consultant or even anyone at a BI conference, that has ever heard of Data Vault.
Data Vault, a secret revolution? I was first introduced to Data Vault a couple of years ago, and have to admit that I did not really see the magic. But before you read on, let me state something that is apparently quite important when it comes to Data Vault. P.S. Data Vault Modeling & Methodology - Data Warehouse Architecture.
DVA - Online Data Vault Training. #NoSQL, #bigdata, #datavault and Transaction Speed. Like many of you, I’ve been working in this industry for years.
Like you, I’ve seen and been a part of massive implementations in the real world where #bigdata has been a big part of that system. Of course, they’ve done this in a traditional RDBMS and not a NoSQL environment. There’s even been a case where Data Vault has been used to process big data with huge success. In this entry I will discuss the problem of big data, and discuss the RDBMS along side the NoSQL approaches. First, a little history please… Why is everyone up in arms about #bigdata? Well, I’m here to tell you that it may not be everything that media hype has cracked it up to be; and if you aren’t careful, you may just lose sight of your massive investments in relational data base systems.
Historically speaking, can Traditional RDBMS do Big Data? What’s one base argument for “switching to NoSQL” for Big Data? Case studies from RDBMS that the media hype wants you to ignore So who’s done what? What about variety? A final point: