Friday, August 28, 2020

(TYFK) Denormalization Fundamentals

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).
 
  ““Main Question: How do we trade-off while doing denormalization? 
  • 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.””

In Part 1 we discuss the relevant fundamentals in which we will ground the debunking in Part 2.

Thursday, August 20, 2020

(TYFK) Relations, Tables, Domains and Normalization

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 domain.”

Friday, August 7, 2020

(OBG) Data Models and Physical Independence


Note: To appreciate the stability of a sound foundation vs the industry's fad-driven cookbook practice, I am re-publishing some of the articles and reader exchanges from the old DBDebunk.com (2000-06), giving you the opportunity to judge for yourself how well my claims/arguments hold up and whether the industry has progressed at all. I am adding comments on re-publication where necessary. Long pieces are broken into smaller parts for fast reading.

From "Little Relationship to Relational" originally posted on March 29, 2001.

 
“E.F. ("Ted") Codd conceived of his relational model for databases while working at IBM in 1969. Codd's approach took a clue from first-order predicate logic, the basis of a large number of other mathematical systems and presented itself [sic] in terms of set theory, leaving the physical definition of the data undefined and implementation dependent. In June of 1970, Codd laid down much of his extensive groundwork for the model in his article, "A Relational Model of Data for Large Shared Data Banks" published in the Communications of the ACM, a highly regarded professional journal published by the Association for Computing Machinery. Buoyed by an intense reaction against the ad hoc data models offered by the physically oriented mainframe databases, Codd's rigid separation of the logical model, with its rigorous mathematical underpinnings, from the less elegant realities of hardware engineering was revolutionary in its day. Codd and his relational ideas blazed across the academic computing landscape over the next few years.”

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