Sunday, June 26, 2016

On Kinds of Keys: Natural, Primary and Surrogate Are Sufficient

Note: This is a 12/04/17 re-write of a 2012 post (revised in 2016) to bring it in line with the McGoveran formalization and interpretation [1] of Codd's RDM.

Here is what is wrong with last week's picture, namely:

"Many data and information modelers talk about all kinds of keys (or identifiers. I'll forego the distinction for now). I hear them talk about primary keys, alternate keys, surrogate keys, technical keys, functional keys, intelligent keys, business keys (for a Data Vault), human keys, natural keys, artificial keys, composite keys, warehouse keys or Dimensional Keys (or Data Warehousing) and whatnot. Then a debate rises on the use (and misuse) of all these keys ... The foremost question we should actually ask ourselves: can we formally disambiguate kinds of keys (at all)?" --Martijn Evers, Kinds of Keys: On the Nature of Key Classifications

I, of course, sympathize, as I've deplored the misuse and abuse of terminology in the industry for longer than I care to remember. This is not unique to keys, it's a general absence of foundation knowledge in the industry [2]. The answer is yes, we can disambiguate -- the key (pun intended) to keys is understanding what from the real world they represent and what database function they fulfill. Things are much simpler than the mindless industry jargon.

Sunday, June 19, 2016

This Week

1. What's wrong with this picture?

This week's picture is the one of the state of knowledge about keys that Martijn Evers painted in Kinds of Keys: On the Nature of Key Classifications, that I had already commented on. As a result of discussions I've been having with David McGoveran in the context of our forthcoming books (his LOGIC FOR SERIOUS DATABASE FOLKS, my DBDEBUNK GUIDE TO FUNDAMENTAL DATA MANAGEMENT MISCONCEPTIONS), I've decided to rewrite my
comments On Kinds of Keys on the subject. I refer the reader to Martijn's article for a refresher--my rewrite will be posted next week.

2. Quote of the Week

There are no rules of normalization for non-relational databases. Effectively, you start out by denormalizing everything. Which means you're designing the data organization to serve specific queries. So follow the same principle in NoSQL databases as you would for denormalizing a relational database: design your queries first, then the structure of the database is derived from the queries. --Bill Karwin, What is a good way to design a NoSQL database

Sunday, June 12, 2016

Levels of Representation: Conceptual Modeling, Logical Design and Physical Implementation

From last week:

What's wrong with this picture? (Kinds of Data Models, LinkedIn.com)

David Hay: "Part of the ... confusion as to what exactly was meant by “data modeling”--conceptual, logical or physical--is that most data modeling activities seem to focus on achieving good relational database designs ... my approach is the portrayal of the underlying structure of an enterprise’s data--without regard for any technology that might be used to manage it ... a “conceptual data model” ... that represents the business."

Nothing raises uncertainty whether to laugh or cry better than attempts to dispel confusion which suffer from the very confusion they purport to dispel.

Sunday, June 5, 2016

This Week

1. What's wrong with this picture?
David Hay: Part of the ... confusion as to what exactly was meant by “data modeling”--conceptual, logical or physical--is that most data modeling activities seem to focus on achieving good relational database designs ... my approach is the portrayal of the underlying structure of an enterprise’s data--without regard for any technology that might be used to manage it ... a “conceptual data model” ... that represents the business.

Nigel Higgs: ... many folks do not get the difference between the Barker entity relationship style of modeling and the relational style of modeling ... [because] the modeling conventions are very similar and the former [is always] a precursor to RDBMS design.

Clifford Heath: Any terminology for models must project three aspects of intention: (a) audience, (b) level of detail and (c) purpose. These three variables are sufficient to discriminate all the main kinds of models in use. The traditional terms of "conceptual/logical/physical" are manifestly inadequate.

Remy Fannader: Models are meant to describe sets of instances (objects or behaviors).
--Kinds of Data Models, LinkedIn.com

2. Quote of the Week

The first consideration that needs to be made when selecting a database is the characteristics of the data you are looking to leverage. If the data has a simple tabular structure, like an accounting spreadsheet, then the relational model could be adequate. Data such as geo-spatial, engineering parts, or molecular modeling, on the other hand, tends to be very complex. It may have multiple levels of nesting and the complete data model can be complicated. Such data has, in the past, been modeled into relational tables, but has not fit into that two-dimensional row-column structure naturally. --Jnan Dash, RDBMS vs. NoSQL: How do you pick?