“3rd normal form data models in data warehousing efforts struggle when changes impact parent child relationships. These impacts cause cascading changes to the data model, the queries, and the loading processes. [For example:]

• There are bank accounts
• Each account belongs to exactly one customer
• A customer can have more than one account

The bank introduces a new product: joint accounts, which means an account can now have more than one owner. It is clear that the 3NF model has to be extended in order to keep this new information; the data vault models seems to be able to fulfill the new requirement.

Some banks propose joint accounts, some don’t, therefore some use M:N relation between client and accounts and others 1:N. A model which is good for any possible case is actually awful model because it describes nothing: by looking at this model you can’t say if joint accounts exist among bank's products.”

--Data Vault and Model (in)Stability

• Normal forms do not pertain to the data model itself -- the RDM -- but to relations in logical models created using strictly the RDM[2].
• 3NF is insufficient -- relations are in 5NF by definition, otherwise correctness is not guaranteed[3].
• The RDM was introduced as a database representation superior to old directed graph -- hierarchic and network (CODASYL) -- systems for conceptual models focused on relationships among entity groups, rather than among individual entities[4]. Graph database representation (nodes and edges) corresponds to a worldview at the conceptual level of parents-children (network) relationships, of which parent-children (hierarchy) is a special case. The relational representation (relations) corresponds to M:N relationships among entity groups, of which M:1 is a special case[5].

“Over the past decade, there have been a number of DBMSs introduced (typically labeled as NoSQL) which utilize a network or hierarchical data model. MongoDB and Cassandra come immediately to mind as examples. Some such systems support networks through the concepts of "links" and some support hierarchical data using a nested data model often utilizing JSON. In my opinion, these systems have not internalized lessons from history.

“At the SIGFIDET (now SIGMOD) annual conference in 1974, there was a "Great Debate" over the merits of the relational model versus the network and hierarchical models ... Basically, the argument was about which model [relational or network] was a better fit for structured data (as opposed to documents, e-mails, etc.) and boiled down to two questions:

Question 1: Are high-level data sublanguages a good idea?
Question 2: Are tables the best data structure or should one use a network or hierarchy?”

“The last 45 years have definitely affirmed Codd’s position on both issues ... The conclusion from the 1970s was that the relational model provides superior data independence, compared to the network and hierarchical [graph] models. Forty-five years later, this conclusion is still true. If you want to insulate yourself from the changes that business conditions dictate, use a relational DBMS. If you want the successor to the successor to your job to thank you for your wise decision, use a relational model.”

"I have a database for a school ... [with] numerous tables obviously, but consider these:

CONTACT - all contacts (students, faculty): has fields such as LAST, FIRST, ADDR, CITY, STATE, ZIP, EMAIL;
FACULTY - hire info, login/password, foreign key to CONTACT;
STUDENT - medical comments, current grade, foreign key to CONTACT."

"Do you think it is a good idea to have a single table hold such info? Or, would you have had the tables FACULTY and STUDENT store LAST, FIRST, ADDR and other fields? At what point do you denormalize for the sake of being more practical? What would you do when you want to close out one year and start a new year? If you had stand-alone student and faculty tables then you could archive them easily, have a school semester and year attached to them. However, as you go from one year to the next information about a student or faculty may change. Like their address and phone for example. The database model now is not very good because it doesn’t maintain a history. If Student A was in school last year as well but lived somewhere else would you have 2 contact rows? 2 student rows?  Or do you have just one of each and have a change log. Which is best?"

“Scientific research experiments that "require assignment of data to tables, which is difficult when the scientists do not know ahead of time what analysis to run on the data, a lack of knowledge that severely limits the usefulness of relational [read: SQL] databases.”

Data, Information, and Knowledge

“1. Data: Categorized sequences of values representing some properties of interest, but if and how they are related is unknown (e.g., research variables in scientific experiments);
2. Information: Properties further organized in named combinations -- "objects", but how they are related is unknown (e.g., "runs", or "cases" in scientific experiments);
3. Knowledge: Relationships among properties and among objects of different types are known.”

--David McGoveran

“A particular bug-bear and a mistake that +90% of "data modelers" make, is analyzing "point in time" views of the business data and "normalizing" those values hence failing to consider change over time and the need to reproduce historic viewpoints. Let’s say we start with this list of data-items for a Sales-Invoice (completely omitting details of what’s been sold):

SALES-INVOICE
 {Invoice-Date,
  Customer-Account-ID,
  Customer Name,
  Invoice-Address-Line-1,
  Invoice-Address-Line-2,
  Invoice-Address-Line-3,
  Invoice-Address-Line-4,
  Invoice-Address-Postcode,
  Net-Amount,
  VAT,
  Total-Amount
 };

Nearly every time, through the blind application of normalization we get this ... there’s even a term for it -- it’s called "over-normalization":

SALES-INVOICE
 {Invoice-Date,
  Customer-Account-Id
   REFERENCES Customer-Account,
  Net-Amount,
  VAT,
  Total-Amount
 };

CUSTOMER-ACCOUNT
 {Customer-Account-Id,
  Customer-Name,
  Invoice-Address
   REFERENCES Address
 };

ADDRESS
 {Address-Line-1,
  Address-Line-2,
  Address-Line-3,
  Address-Line-4,
  Postcode
 };”

Fact Modeling

“... another school of modelers working with "fact modeling". Their approach is not new. It goes back to the 70's, where Eckhard Falckenberg and Sjir Nijssen started working on the approach (in parallel). Fact Modeling was known for many years as Object-Role-Modeling (ORM), and it was supported by the popular Visio diagramming tool at the time that Microsoft bought the company behind Visio. I like Nijssens name “Binary Relationship Modeling” a lot and it has been in the back of my head since the early 80's. Fact Modeling is definitely at the right level (concepts and their relationships), but it also contains all of the logic details required for formal, precise specifications. The visual syntax goes back to: Nijssen, G.M. and T.A. Halpin, Conceptual Schema and Relational Database Design — A fact oriented approach, Prentice Hall 1989.”

Object Orientation and Unified Modeling Language

“A "counter revolution" against the relational movement was attempted in the 90’s. Graphical user interfaces came to dominate and they required advanced programming environments. Functionality like inheritance, sub-typing and instantiation helped programmers combat the complexities of highly interactive user dialogs. The corresponding Data Modeling tool is the Unified Modeling Language ...”

"Entity-Relationship Model"

“One of the first formal attempts at a framework for Data Modeling was the Entity-Relationship data model paradigm proposed [in 1976] by Peter Chen. Notice that in the original Chen-style, the attributes are somewhat independent and the relationships between entities are named and carry cardinalities ("how many" participants in each end of the relationship) ... Attributes are related to their "owner" entity" in what other people called "functional dependencies".”

“... 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

“We have Building, Room, and Bed entities. Logically, if this is in the scope of some hypothetical hotel, then each one of those entities is dependent on their parent to exist ... you cannot have a bed without a room. Also, that room wouldn't exist without its parent, Building. So, why have I rarely seen this identifying relationship introduced? When I was learning databases, everything was apparently "non-identifying". When is this type of relationship necessary, if at all? I see the issue arises when that BED can exist without a BUILDING. If you were to INSERT into the BED table, you are constraint [sic] to provide a building_id, as the building_id is part of that BED's primary key. Couldn't you avoid an identifying relationship by giving each table its own surrogate primary key? Is this the correct representation  of an identifying relationship? I could avoid that by just giving each table its own ID. At the end of the day, this is about IDENTIFYING relationships, not their existence, which is how I've been logically determining if something is an "identifying relationship" If that were the case, then any 1:N relationship could be "identifying" but that's not how you define identifying or non-identifying.”

“Interesting -- I’d never heard this term before. I’ve heard it referred to as a cached ID though, as that 2nd ID isn’t required, but may be beneficial for performance purposes. For this example with 3 levels it’s not a huge joint statement, but for some systems with 12 tables the joins get unpleasant. I’ve never started a system with this additional id, but I have added one later on once the need was there and the profiling led to this being the best solution for our specific situation. Usually though, just creating a view that does the joins for me has been easier. I’ll be curious what has led others to use this approach.”

It's not really introduced because it's way more towards academic than functional.”

--Reddit.com

 “We have Building, Room, and Bed entities. Logically, if this is in the scope of some hypothetical hotel, then each one of those entities is dependent on their parent to exist ... you cannot have a bed without a room. Also, that room wouldn't exist without its parent, Building. So, why have I rarely seen this identifying relationship introduced? When I was learning databases, everything was apparently "non-identifying". When is this type of relationship necessary, if at all? I see the issue arises when that BED can exist without a BUILDING. If you were to INSERT into the BED table, you are constraint [sic] to provide a building_id, as the building_id is part of that BED's primary key. Couldn't you avoid an identifying relationship by giving each table its own surrogate primary key? Is this the correct representation  of an identifying relationship? I could avoid that by just giving each table its own ID. At the end of the day, this is about IDENTIFYING relationships, not their existence, which is how I've been logically determining if something is an "identifying relationship" If that were the case, then any 1:N relationship could be "identifying" but that's not how you define identifying or non-identifying.”

“Interesting -- I’d never heard this term before. I’ve hears it referred to as a cached ID though, as that 2nd ID isn’t required, but may be beneficial for performance purposes. For this example with 3 levels it’s not a huge joint statement, but for some systems with 12 tables the joins get unpleasant. I’ve never started a system with this additional id, but I have added one later on once the need was there and the profiling led to this being the best solution for our specific situation. Usually though, just creating a view that does the joins for me has been easier. I’ll be curious what has led others to use this approach.”

“It's not really introduced because it's way more towards academic than functional.”

--Reddit.com

“[According to Date] relvar ≠ class. [But i]n simple terms, class applies to a collection of values allowed by a predicate, regardless of whether such a collection could actually exist. Every set has a corresponding class, although a class may have no corresponding set ... in mathematical logic, a relation is a class (and trivially also a set), which contributes to confusion.”

“In modern programming parlance, class is generally distinguished from type only in that the latter refers to primitive (system-defined) data definitions, while class refers to higher-level (user-defined) data definitions. This distinction is almost arbitrary, and in some contexts, type and class are actually synonymous.”

“I don't understand the concepts of domain and attribute in relational database modeling. Can someone give me an effective example?”

“Domain is an overloaded word in the DB lexicon. It probably should also be avoided. When one refers to an attribute domain in practice it is only referring to columns that have a check constraint on them that limit the values. Reference tables with foreign key constraints in general also fulfill the spirit of what domain attributes do outside of an RDBMS.”

“A domain in most SQL usage is essentially an alias name for an existing type + restrictions on an existing type that can be used in a column. As for an attribute, it's essentially a COLUMN in SQL, a field in other types of databases, etc.”

“When we create specific domains, relations, and attributes we are constraining (restricting) an abstract logical system to a specific interpretation (meaning). Seen the other way around, an interpretation of the logical system is a representation of a specific segment of the world, and that is exactly the purpose of database design. For example, an attribute name created by the designer is assigned meaning intended by the modeler as representing an entity property, which is the very meaning of semantics. That is why full normalization cannot be achieved or assessed without reference to some conceptual model -- what attribute names mean, and how they are related to each other (i.e., their dependencies), and so on.” --David McGoveran

• Properties in context (PiC) shared by entities of each type;
• Collective group properties (i.e., relationships among entity group members);
• Multigroup properties (i.e., inter-group relationships).

• Objects -- entities, entity groups, and multigroups -- formalize as tuples, relations, and databases, respectively;
• Properties formalize as domains, and when associated with entities of specific types, as attributes;
• Group and multigroup properties -- relationships among entities, and among groups[3] -- formalize as constraints on and among relations enforceable by the DBMS.

“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