“Relational databases like MySQL, PostgreSQL and SQLite3 represent and store data in tables and rows. They're based on a branch of algebraic set theory known as relational algebra. Meanwhile, non-relational databases like MongoDB represent data in collections of JSON documents. The Mongo import utility can import JSON, CSV and TSV file formats. Mongo query targets of data are technically represented as BSON (binary JASON).

“Relational databases use Structured Querying Language (SQL), making them a good choice for applications that involve the management of several transactions. The structure of a relational database allows you to link information from different tables through the use of foreign keys (or indexes), which are used to uniquely identify any atomic piece of data within that table. Other tables may refer to that foreign key, so as to create a link between their data pieces and the piece pointed to by the foreign key. This comes in handy for applications that are heavy into data analysis.

“If you want your application to handle a lot of complicated querying, database transactions and routine analysis of data, you’ll probably want to stick with a relational database. And if your application is going to focus on doing many database transactions, it’s important that those transactions are processed reliably. This is where ACID (the set of properties that guarantee database transactions are processed reliably) really matters, and where referential integrity comes into play.”

“The foundation of modern database technology is without question the relational model; it is the foundation that makes the field a science.”
--C. J. Date, AN INTRODUCTION TO DATABASE SYSTEMS

“Over the past decades mainstream economics in universities has become increasingly mathematical, focusing on complex statistical analyses and modeling to the detriment of the observation of reality.”
--J. Luyendijk, Don’t let the Nobel Prize fool you, economics is not a science

“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].

CUSTOMERS
===============================================
| CID | NAME     | AGE | ADDRESS   | SALARY   |
-=====-----------------------------------------
|   1 | Ramesh   |  32 | Ahmedabad |  2000.00 |
|   2 | Khilan   |  25 | Delhi     |  1500.00 |
|   3 | Kaushik  |  23 | Kota      |  2000.00 |
|   4 | Chaitali |  25 | Mumbai    |  6500.00 |
|   5 | Hardik   |  27 | Bhopal    |  8500.00 |
|   6 | Komal    |  22 | MP        |  4500.00 |
|   7 | Muffy    |  24 | Indore    | 10000.00 |
-----------------------------------------------

ORDERS
===================================
| OID | DATE       | CID | AMOUNT |
-=====-----------------------------
| 102 | 2009-10-08 |   3 |   3000 |
| 100 | 2009-10-08 |   3 |   1500 |
| 101 | 2009-11-20 |   2 |   1560 |
| 103 | 2008-05-20 |   4 |   2060 |
-----------------------------------

SELECT c.cid,c.name,o.oid,o.amount,o.date
FROM customers c
INNER JOIN orders o
ON c.cid = o.cid;

====================================================
| C.CID | C.NAME   | O.OID | O.AMOUNT | O.DATE     |
-=======------------=======-------------------------
|     2 | Khilan   |   101 |     1560 | 2009-11-20 |
|     3 | Kaushik  |   102 |     3000 | 2009-10-08 |
|     3 | Kaushik  |   100 |     1500 | 2009-10-08 |
|     4 | Chaitali |   103 |     2060 | 2008-05-20 |
----------------------------------------------------

by David McGoveran

“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.”

• A data sublanguage is short for data manipulation language (DML) that combines (1) a relationally complete retrieval component (i.e., that expresses the RA) with (2) a component that expresses updates as relation transformations;
• A DBMS language is a careful combination, for practical purposes, of the data sublanguage with several sublanguages, each of which expresses a data management function (e.g., data definition, transactions, concurrency, authorizations) -- that are not relational, but are consistent with the RDM, and must not include syntactic elements that are at odds with, or subvert those of the DML.

“Consider dimensional design and Big Data as two additional paradigms ... Big Data paradigms like Hadoop and NoSQL will alleviate the temptation people have to try to use the relational database in unnatural ways.”

• Data sublanguage is short for a relationally complete data manipulation sublanguage (DML) that expresses  retrievals and updates, the latter correctly understood as set-theoretic relation transformations.
• A data definition sublanguage (DDL) is a metalanguage for DML that is outside the theory but consistent with the RDM and at least as powerful expressively as the DML (e.g., a very carefully restricted SOL to avoid self-referencing).
• The DML and the DDL can, for practical purposes, be carefully unified into what Codd called a "comprehensive data sublanguage", but we prefer DBMS language to avoid confusion.

“With a relational catalog, definition can be performed via the RA, which requires physical implementation to be determined exclusively by the catalog (behind the scenes as it were) -- a kind of skeletal, primitive, or rudimentary DBMS. This is why Codd created a relational catalog that contains a description of the database and could be managed using RA-based DML. It works well unless one is allowed to mix DDL (metalanguage) with DML (language) in the same expression. Otherwise put, the database can be read to modify the catalog, but not vice-versa (as far as the DML is concerned, the catalog that describes the database does not exist).

But with a data model that, unlike the RDM, does not define a catalog such that the same language can be used for both database and it, a rudimentary DBMS must provide a workaround, and if the model is computationally complete (like CODASYL was), there must limits on how "active" the catalog is to prevent users from writing self-referencing expressions that cannot be automatically implemented because they may corrupt the database (same as would mixing data sublanguage and host language). This is one reason some of the pre-RDM directed graph DBMSs had limited notions of catalog that often required completely separate facilities to maintain.”

“Recently I have read that SQL is actually a data sublanguage and not a programming language like C++ or Java or C# ... The answers ... have the pattern of "No, it is not. Because it's not Turing complete.", etc, etc. ... I am a bit confused, because since you can develop things through SQL, I thought it is similar to other programming languages ... I am curious about knowing why exactly is SQL not a programming language? Which features does it lack? (I know it can't do loops, but what else more?)”

--StackOverflow.com

“The SQL operators were meant to implement the relational algebra as proposed by Dr. Ted Codd. Unfortunately Dr. Codd based some of his ideas on a "extended set theory", which was an idea formulated and described in a 1977 paper by D. L. Childs ... But Childs’ extensions were not ideally suited, which is explained in quite some detail in [a] book ... by Professor Gary Sherman & Robin Bloor [who] argue that mainstream Zermelo-Fraenkel set theory (Cantor), would have been a better starting point. One key issue is that sets should be able to be sets of sets.”

--Dataversity.net

"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?"

• Understanding Conceptual vs. Data Modeling Part 4: Properties-object Modeling

“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
 };”

“I just wanted to drop a note of thanks for the website, especially the latest articles on understanding data modeling, which among other things, explains very nicely the difference between the application of set theory and graph theory. It parallels in the real world with the community (set of data elements) and the individual (node in a network) and how it is easier to connect communities (RDM), but how much more complex it would be to connect individuals directly (GDM) without going through such a community connection arrangement (e.g. e-mail, postal system).”

“I'm currently working out the concept of what I call CMCs or contextual metadata connectors. I'm sure such entities will be heavily dependent upon the usage of RDM to do their job. In the project, I would like to use both approaches (RDM, GDM) due to the power of set theory and graph theory, but exactly where one should do so is so critical.”

“It's exciting to think of the endless potential for AI-based automation when one correctly leverages the underlying principles of data relationships. Since my discovery in 2004 about a much better way to approach test automation which I called data-centric (vs. the code-centric industry standard), I have found that it applies anywhere there is data, as long as one holds to a proper understanding of data and how to view it relationally.”

“What I find very surprising though is how rare it is to find in the I.T. industry a proper understanding of data, especially when viewing it relationally. It is indeed one of the most massively misunderstood aspects of the I.T. industry to this day, as your website alludes to. Rather than running away from it, RDM should be the very first course taught in any program involved in either computer science or information science. Maybe then I wouldn't always be losing people in technical conversations whenever I start talking about it. I see a diamond and they just see carbon.”

“Up to 2018, DBDebunk was maintained and kept free with the proceeds from my @AllAnalitics column. In 2018 that website was discontinued. You will not find its content anywhere else, so if you deem it useful, particularly if you are a regular reader, please back up your appreciation with concrete support -- purchase publications, or regular donations. Thank you.”

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

“The relational databases that emerged in the ’80s are efficient at storing and analyzing tabular data but their underlying data model makes it difficult to connect data scattered across multiple tables. The graph databases we’ve seen emerge in the recent years are designed for this purpose. Their data model is particularly well-suited to store and to organize data where connections are as important as individual data points. Connections are stored and indexed as first-class citizens, making it an interesting model for investigations in which you need to connect the dots. In this post, we review three common fraud schemes and see how a graph approach can help investigators defeat them.”

--AnalyticBridge.DataScienceCentral.com

• There is no "tabular data" (the relational data structure is the relation, which can be visualized as a table on a physical medium[2], and SQL tables are not relations);

• Analysis is not a DBMS, but an application function (while database queries, as deductions, are an important aspect of analysis, and computational functions can be added to the data sublanguage (as in SQL), the primary function of a DBMS is data management)[3];

• A data model has nothing to do with storage (storage and access methods are part of physical implementation, which determines efficiency/performance[4]).

“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

“Fabian - With respect, maybe it's time to' shake the formal foundations' of data management, especially given the rising costs and increasing segregation of silos.”

“John, if I were to say what I really think, I would be accused of insulting, so I won't. You don't need to respect me, but you better respect formal foundations. Since they are what gives SOUNDNESS to data management practice, what you are really saying is that you don't care about soundness -- do you really intend to take this position? I would not be surprised, because the industry has long "shook" the formal foundations and lack of soundness is precisely what characterizes it. But because there is no longer proper education, practitioners are totally unaware of the relationship between formal foundations and soundness, everything is ad-hoc and arbitrary, yet they fail to recognize the consequences.”[1]

--LinkedIn.com

“Ok, now that we have those two (Parts 3 and 4 of your series) 'on the table' so to speak, perhaps you would address these questions...

1. Would it be safe to say that facts expressed in a Conceptual model should be verifiable in reality?

2. Are the following facts logically equivalent or are they different:

a) The car with license number 62-JZK-6 has the color aquamarine blue
b) De auto met kenteken 62-JZK-6 heeft de kleur aquamarijnblauw

3. If a previously true fact is found in reality to be verifiably false, would that mean the Conceptual model is wrong or the Logical model, or reality?”

“I'm going to add another:

4. How does RDM handle temporal changes to the 'truth' of statement 2a) when:

a) The owner of the car paints it black.
b) The owner of the license plate legally transfers it to a truck.
c) The owner of the car replaces every single part except the chassis.”