R: Unless the need is for ACID compliant transactions, denormalization is generally not considered logically, physically or whatever-ally-–so essentially a thoroughly normalized mode is relevant for a write-infrequently consumption of data and data integrity can be guaranteed by design.
Mathematically, a relation on domains—which are sets of values of a type—is a subset of the Cartesian product of the domains.Note that the whole CP is also a subset, so it is also a relation, which happens to have useful applicability to business modeling and database design. In the database context, it can be pictured as the pool of all possible rows--past, present and future--for a R-tablevar defined by the domains' types. A database R-table is the set of actual rows at any point in time that is consistent with the set of all integrity constraints to which the R-table is subject (see Business Modeling for Database Design).
NoSQL usant correct m'y indeed totof n'y most of the dev ans devops who clearly thing nosql Means they will ne a le to do whatever they wants ans still have answers to their twisted query in a correct time. Those people see nosql as the mean to get ris of DBAs. And il not kiddin since it's happening right now un many companies i know of. --LinkedIn.com
Architecting IMS for Big Data - a symbiotic relationship.
IEEE Computer Issue on CAP TheoremH/t Erwin Smout.
Logical design is where the Architect defines entities (which will become tables in a database), attributes (which will become columns in a database), etc. This is typically the level that SMEs are most comfortable. I think that a Logical design may deal with data types and keys, but it does not cater to any specific platform or engine.2. To Laugh or Cry?
Physical design is where the Architect translates the logical design into tables, columns, datatype specifics like INT versus NUMERIC, indexes, partitions, etc. This is where "the rubber meets the road" and the logical design gets mapped into a form that can exist and be tested on a database server.
While I'm sure that someone will object to this link on religious grounds, the discussion does a pretty good job of making the distinctions that concern me. --LinkedIn.com
MyBatis Schema Migration SystemH/t Ben Samuel, who adds:
"From the department of "we haven't really thought this feature through" comes this gem, one of several schema migration systems that allow "reverse migrations" or "downward migrations". Whereas a forward migration creates tables, columns, etc., a reverse migration drops them. The video proudly shows them "reverse migrating" their database until all tables are dropped. Another vendor patiently explains why they don't offer this feature."
The Death Of Expertise
Codd's relational model is based on set theory, and set theory simply doesn't work for database systems. It can't, for example, model a gum ball machine. Gum balls, you see, have only one attribute, which is color (gum balls don't have names, serial numbers, bar codes, or URLs). If you put 200 gum balls in a gum ball machine, the gum ball machines contains 200 gum balls. If you try to put 200 gum balls in a gum ball relation, you get a relation of 5 gum balls (the number of colors) and 195 duplicate errors. If you then take 5 gum balls out of the gum ball machine, it still contains 195 gum balls. If you take 5 gum balls out of the gum ball relation, it goes empty. --Jim Starkey, LinkedIn.com
How to store and document large data models
Software engineers think they're amazingly great
L: I realize that you have taken much further what Codd wrote on the first page of his 1970 paper but it's still remarkable how many people in the data business are not able to refer to, let alone talk productively, about his "natural structure of data". And many treat RT as a fait accompli when it is still evolving, not to mention those who, as you've pointed out many times, treat SQL gizmos such as outer join as if they come from RT when they don't.
