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

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."

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

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?

"Data is stored in two-dimensional tables consisting of columns (fields) and rows (records). Multi-dimensional data is represented by a system of relationships among two-dimensional tables. This usually leads to data storage becoming redundant, and also difficult to maintain on account of addition and deletion anomalies. This is only the case if we do not normalize the data. Keys are fields or combinations of fields used to identify records." --Nigel Peck, Denormalization Summary

"Peter Chen (E/R Modeling, 1976) used the term "weak" entity to describe one which could not meaningfully have an independent existence. An example might be an Order which requires a customer (and a Product or set of products). A weak entity need not be a composite, as in your OrderItem example. The central issue here is dependency of one entity [of one] type on another [of another type]. Furthermore, there could be more that one such dependency. In your example, the OrderItem would be dependent on BOTH Order and Item.

It is important to note that this dependency will NOT be enforced by a simple RDBMS unless you also define referential integrity on both parts of the composite key, notwithstanding that some RDBMS's (e.g., SQL Server) couple the definition of the relationship with a referential integrity constraint. In other words, you cannot have a defined relationship without enforcing referential integrity.

This is not always desirable. Consider the task of bulk loading some records which have a relationship with some other entity type(s). The only practical way to do this is to first turn off referential integrity enforcement which means deleting the definition of the relationship entirely. After completing the loading of data, if you reinstate the defined relationship, will the system automatically ensure that referential integrity is not violated? Good question." --Gordon Everest

"In Oracle you can instruct the DBMS to log violating rows into an 'exceptions' table while enabling a constraint (be it, a primary key, unique key, foreign key, or plain check constraint). This fits the bill better, particularly if it's possible to INSERT...SELECT them after corrections." --Toon Koppelaars

"SQL Server will check on re-enabling the constraint that it is not violated by the new state of the database. If it is, it only reports that an error occurred. It has a real time violation catalog to easily map a constraint name to a specific violation and transaction id. In other words, doing bulk batch constraint validation is part of the product." --William Sisson and Racim Boudjakdji

"How to implement“existence dependence” ... between two entities ...is a design decision and is dependent upon the architecture of the implementation technology. [Chen's E/R models] ... were completely technology agnostic and had no “relational” flavor at all. Why? With the rapid emergence and popularity of the NoSQL, NewSQL, Graph Databases, etc. no longer can relational implementation and FK be assumed and we again need to separate the “conceptual data model” from the “logical data model”. Whether this is evolution of de-evolution is a whole other subject (for those of us that remember WHY relational technology emerged in the 1980s--and as a warning to those who would forget their history--IDMS, IMS, etc.)"

The relational model has been so dominant for so long, we have forgotten our roots and the reason that there were originally three layers of data model, not just two. The rise of the post-relational technologies has again required their creation and reminds us of their utility. --David Tryon

• A logical model is implemented in specific hardware and DBMS software, a conceptual model is formalized as a logical model by means of a data model (e.g., the RDM). Using 'implementation' for both induces and reinforces the confusion.
• Emergence, or popularity are not grounds for technology adoption: a superior generality-to-simplicity ratio (G2SR), soundness and flexibility are.