Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

Then ...

“I find [your article in DM Review] to contradict your stated devotion to scientific methods and the value of theory. You present a single example of denormalization, then proceed to draw a conclusion about denormalization in general. In addition, the example chosen is not typical of real world denormalizations.” In order to be half-way consistent with your own ideals, you would need to present at a minimum an exhaustive list of the types of denormalizations used in practice, along with an objective list of the pros and cons of each.  I would expect that if this were undertaken, you would end up with a more balanced view, and some exceptions to your black-and-white conclusions. Of course, to prove your point scientifically would require far more effort than this, if indeed it were at all possible to prove or disprove your statements. This brings me to my key point: if your contention is not falsifiable, it does not belong in the realm of true science at all, instead it belongs in the domain of mere opinion and belief. Please tell us how you have proved your propositions, or else refrain from claiming that you are working from a sound scientific foundation and everyone else is somehow misguided. Relational algebra has nothing to say about real-world performance.”

Note: In "Setting Matters Straight" posts I debunk online pronouncements that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

“A conceptual data model usually just includes the main concepts (entities) required to store information and the relationships that exist between these entities. We don’t usually include any details about each piece of information. We can consider the conceptual stage as an initial model, without all the details required to create a database.

A logical data model is probably the most-used data model. It goes beyond the conceptual model; it includes entities, relationships, details on entities’ different attributes, and unique ways to identify entities (primary keys) and establish the relationships between them (foreign keys).

A physical data model is usually derived from a logical data model for a particular relational database management system (RDBMS), thus taking into account all technology-specific details. One big difference between logical and physical data models is that we now need to use table and column names rather than specifying entity and attribute names. This allows us to adapt to the limits and conventions of the desired database engine. We also provide the actual data types and constraints that allows us to store the desired information.”

--Vertabelo.com

Note: In "Setting Matters Straight" posts I debunk online pronouncements that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

What's right/wrong about this database picture?

“Other than constraints on cardinality, business rules are not generally represented on data models of either kind. Even in the case of business data models, the models are supposed to represent fundamental structures, while business rules represent variable constraints.”

                                                                    --TDan.com

Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2002 posts on Simon Wlliams' Lazy Software so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

• Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that triggered the series.
• Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.
• Part 3 was my email exchange with Williams where he provided his definition of a data model on which I conditioned any discussion with him and I debunked it.
• Part 4 is my response to a reader's comments on my previous posts in the series.
• Part 5.1 provided the background for my critique of Edward Hurley's report on Simon Williams's Lazy Software and his so-called "Associative Model of Data" (AMD).

Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This is a re-published series of several DBDebunk 2001 exchanges about Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series.
Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.
Part 3 was my email exchange with Williams, where he provided his definition of a data model on which I conditioned any discussion with him and where I debunked it.
Part 4 is my response to a reader's comments on Parts 1-3.

Note: In "Setting Matters Straight" posts I debunk online pronouncements that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

In Part 3 we set the matter straight about normalization to 1NF. In this part we do it wit respect to further normalization to 5NF. Non-1NF relations (i.e., with relation-valued attributes) are no longer part of industry practice, so we focus on 2NF-5NF violations. The term further normalization originates with Codd, who initially thought 1NF was sufficient and 2NF-5NF were discovered later (hence, further = beyond 1NF). The industry lumps both under normalization, but the two are distinct (e.g., only further normalization involves redundancy).

What's right/wrong with the following?

“So, what is this theory of normal forms? It deals with the mathematical construct of relations (which are a little bit different from relational database tables). First, second, and third normal forms are the basic normal forms in database normalization. Normalization in relational databases is a design process that minimizes data redundancy and avoids update anomalies. Basically, you want each piece of information to be stored exactly once; if the information changes, you only have to update it in one place. The normalization process consists of modifying the design through different stages, going from an unnormalized set of relations (tables), to the first normal form, then to the second normal form, and then to the third normal form.”

--Vertabelo.com