Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to our debunking thereof. If there isn't a match, you can review references -- which reflect the current understanding of the RDM, distinct from whatever has passed for it in the industry to date -- which explain and correct the misconceptions. You can acquire further knowledge by checking out our POSTS, BOOKS, PAPERS and LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).

“[As a] set [a database relation] is a collection of similar or related things.”

--ArtfulSoftware.com

Can you tell what's wrong with this statement (hint: one word is wrong)? If not, it is because it is impossible without the old industry interpretation of the RDM.

Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to read our debunking, which is based on the current understanding of the RDM, distinct from whatever has passed for it in the industry to date. If there isn't a match, you can acquire the knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).

“The most popular data model in DBMS is the Relational Model. It is more scientific a model than others. This model is based on first-order predicate logic and defines a table as an n-ary relation. The main highlights of this model are:

• Data is stored in tables called relations.
• Relations can be normalized.
• In normalized relations, values saved are atomic values.
• Each row in a relation contains a unique value.
• Each column in a relation contains values from a same [sic] domain.”

--What is a relational database and why is it important, Quora.com

Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references.

Skyscrapers with Shack Foundations

(originally posted 06/04/2000)

 “Well, it's really a judgment call and I think a lot of experience comes into it. It's a little bit like building a shack. Say you want to build a skyscraper, and you started out building a shack and you just keep trying to add onto it. After a while you have this severe structural problem ... So there is a fallacy to the build-upon-a-simple structure approach. Sometimes you get up to three stories and you have to do some major structural changes, and I just accept that.”
--Wayne Ratliffe, developer of dBase

“Client Servers were a tremendous mistake. And we are sorry that we sold it to you. Instead of applications running on the desktop and data sitting on the server, everything will be Internet based. The only things running on the desktop will be a browser and a word processor. What people want is simple, inexpensive hardware that functions as a window on to the Net. The PC was ludicrously complex with stacks of manuals, helplines and IT support needed to make it function. Client server was supposed to alleviate this problem, but it was a step in the wrong direction. We are paying through the nose to be ignorant.”
--Larry Ellison, CEO, Oracle Corp.

“To Normalize or not to Normalize? that really isn't a question. few things to consider:
Normalization is supposed to protect from data anomalies, but not prevent us from using data encapsulation is the magic trick that allows you to do what you want without breaking rules.what are your experiences with normalization?”

This is a question that at this time need -- and should -- not be asked anymore, and the fact that it still is is one confirmation -- among many -- that there is no progress in data management. According to the current understanding of the RDM:

• Database relations are both normalized (in 1NF) and fully normalized (in 5NF) by definition, otherwise they are not relations and the relational algebra (RA) does not work;
• Adherence to three database design principles produces 1NF and 5NF relational databases;
• Consequently, there should not be such a thing as "doing" normalization (to 1NF) and further normalization (to 5NF) except to repair databases that are non-relational due to failure to adhere to the principles.

Note: The three design principles are fundamental to SST/FOPL foundation of the RDM, but were never understood even by relational proponents. I do not know what encapsulation has to do with this.

Note: I have documented and debunked these misconceptions so many times that I will no longer reference them -- the reader motivated to gain genuine understanding should use the (1) blog labels (2) Blogger search (3) POSTS page to locate the relevant posts.

I have long claimed that a core problem in the industry is the vast majority of practitioners who use relational terminology, do not know/understand what it means, yet are convinced they do -- the less the understanding, the greater the convinction. A recent LinkedIn exchange provided -- as if it were needed -- yet another example. It was triggered by my comment:

“How many know today that a relation is by definition in 5NF, otherwise it's not a relation, the relational algebra has "anomalies" and all bets are off? IMO, none! If you need to "do" normalization, you did not design correctly, which means you don't understand the RDM.”

“Is that really true? You construct a table and fill it full of garbage. It may not even be in 1NF, but is it not still a "relation" of columns, even if it's not a relation of rows or attributes? Codd had no real conception of syntax as separate from semantics, I don't think relational theory has a clear position on this. This is where Kimball and dimensional systems differ from Codd's relational, it made some effort (not a lot) to distinguish syntactic and semantic elements.”

--Joshua Stern

Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references.

In response to a LinkedIn thread where interest was expressed in a "NULL research" project, we re-publish as "Oldies But Goodies" a series of old exchanges with readers reacting to our The Final NULL in the Coffin: A Relational Solution to Missing Data: in Parts 1, 2, 3  we re-published a past exchange between myself and Hugh Darwen with a reader on the pros and cons of our solution vs. Hugh Darwen's "horizontal decomposition" NULLless solution, and in Part 4 my debunking of reactions to an article of mine on the subject which exhibit the confusion inevitably evoked by NULL.

We complete this series with an exchange with a reader who claimed that our criticism applies to SQL's specific implementations of NULL, not to the NULL concept itself. I and David McGoveran explained that the implementation flaws in SQL derive directly from the deeper problems endemic to many-valued logics. 

Note: To demonstrate the correctness and stability of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may break long pieces into multiple posts, revise, and/or add comments and references.

In response to a LinkedIn exchange we continue the series about missing data, NULL and the RDM. In Parts 1,2 and 3 we re-published a past exchange between myself and Hugh Darwen on the pros and cons of our relational solution to missing data vs. Hugh's "horizontal decomposition".

Here we re-publish my debunking of reactions to an article of mine exhibiting the common confusions evoked by NULL.