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: "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 on 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 I debunked it. Part 4 is my response to a reader's comments on my previous posts in the series.

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 on 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 the 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 is my email exchange with Williams: he provided his "definition" of a data model on which I conditioned any discussion with him and I proved my point by debunking it.

Note: In "Setting Matters Straight" I post on LinkedIn online Q&As that involve fundamentals under the header "What's Right and Wrong with this Database Picture" and then debunk them here. 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.

Q: “I'm not sure what this means: "The order of the rows and columns is immaterial to the DBMS?" -- could anyone explain?”

A: “It means two things:
The engine is under no obligation to insert new rows immediately following the previously inserted row(s)... During processing of selects, the optimizer is free to use any index it finds efficient to use or none at all... For this reason, if the order of returned data is important to your processing, then you must include an ORDER BY clause.”

Q: “How do you reorder fields in the database?”

A: “Depends on how you define "reorder". What view of your data are you trying to set the order. Are you in Table Design view? ... Are you looking at form? The answer is different depending on what you are referring to.”

--Quora.com

• Sub-question 1: the standard to implement

- Do we always have to denormalize a model? For what kind of project must we use denormalization techniques while others may not?
- Since denormalization has its gains and losses, how well should we denormalize a data model? Perhaps, the more complete we denormalize, the more complex, uncertain and poor the situation will be.

• Sub-question 2: the characteristics of normalization

-Does denormalization have several levels/forms the same as that of normalization? For instance: 1DNF, 2DNF...
- Given we can denormalize a data model, it may never be restored to the original one because to do normalization, one can have many ways while to build a data model, you can have multiple choices in determining entities, attributes, etc.””

“Data modelling, star schema, snow flakes, data vault. Implementing virtual data warehouses (many stage to modify relationships). Normalisation (using a lot of surrogate keys) all for the sake of business reporting analytics. Reason a SQL DBMS approach columns rows is mandatory.”

--LinkedIn

“In our industry, there is a strong desire to put names on things. This is natural enough, given the amount of information that we have to classify and deal with in our work. To give something a name is to gain control over it, and this is not necessarily a bad thing. The problem is when the name takes the place of true understanding of the thing named. Discourse tends to be the bantering of names, without true understanding of the concepts involved.”

“... I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules? My concerns were twofold ... I wanted a simple, DIY-style, metadata repository for storing 3-level data models -- what would the meta model of such a thing look like? -- [where] atomicity is of essence ... I took a tour (again) in the Data Modeling zone, trying to deconstruct the absolutely essential metadata, which data modelers cannot do without.”
--Thomas Friesendal, The Atoms and Molecules of Data Models, Dataversity.com

“The term data model is used in two distinct but closely related senses. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain, for example the customers, products, and orders found in a manufacturing organization. At other times it refers to a set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the "data model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses.”

--Data Model, Wikipedia

What a True Data Model Is

“I learned that there is no concept of order in terms of tuples (e.g. rows) in a table, but according to wikipedia "a tuple is an ordered list of elements". Does that mean that attributes do have an order? If yes why would they be treated differently, couldn't one add another column to a table (which is why the tuples don't have order)? [OTOH], "In this notation, attribute–value pairs may appear in any order." Does this mean attributes have no order?”

--Do the “columns” in a table in a RMDB have order?

“Is it possible to reorder rows in SQL database? For example, how can I swap the order of 2nd row and 3rd row's values? The order of the row is important to me since i need to display the value according to the order [and] 'Order by' won't work for me. For example, I put a list of bookmarks in database. I want to display based on the result I get from query. (not in alphabet order). Just when they are inserted. But user may re-arrange the position of the bookmark (in any way he/she wants). So I can't use 'order by'. An example is how the bookmark display in the bookmark in firefox. User can switch position easily. How can I mention that in DB?”

--How can I reorder rows in sql database

"In my experience, using an object model in both the application layer and in the database layer results in an inefficient system. This are my personal design goals:
- Use a relational data model for storage
- Design the database tables using relational rules including 3rd normal form
- Tables should mirror logical objects, but any object may encompass multiple tables- Application objects, whether you are using an OO language or a traditional language using structured programming techniques should parallel application needs which most closely correspond to individual SQL statements than to tables or "objects". --LinkedIn.com

"Business Intelligence specialists are often on the lookout for better way to solve their data modeling issues. This is especially true for Data warehouse initiatives where performance, flexibility and temporalization are primary concerns. They often wonder which approach to use, should it be Anchor Modeling, Data Vault, Dimensional or still Normalized (or NoSQL solutions, which we will not cover here)? These are modeling techniques focus around implementation considerations for Information system development. They are usually packed with an approach to design certain classes of information systems (like Data warehouses) or are being used in very specific OLTP system design. The techniques focus around physical design issues like performance and data model management sometimes together with logical/conceptual design issues like standardization, temporalization and inheritance/subtyping."

"Implementation Data Modeling techniques (also called physical data modeling techniques) come in a variety of forms. Their connection is a desire to pose modeling directives on the implemented data model to overcome several limitations of current SQL DBMSes. While they also might address logical/conceptual considerations, they should not be treated like a conceptual or logical data model. Their concern is implementation. Albeit often abstracted from specific SQL DBMS platforms they nonetheless need to concern themselves with implementation considerations on the main SQL platforms like Oracle and Microsoft SQL Server. These techniques can be thought of as a set of transformations from a more conceptual model (usually envisaged as an ER diagram on a certain 'logical/conceptual' level but see this post for more info on "logical" data models)."