Note: About TYFK posts (Test Your Foundation Knowledge) see the post insert below.
“Traditional databases ... don't have a multi-model capability. Point is that richer data models are underused, relational data models are overused, and graph data models have so many advantages that shouldn't be ignored. Relational models, on the other hand, have wildly complex structures often with hundreds to thousands of tables. Each table then contains tens to hundreds of columns, arbitrarily constructed in each and every relational system. And just in case the situation wasn't complex enough, many of those columns are exist exclusively to manage uniqueness and provide connections to other tables. This Structure-FIrst approach produced the cascade of complexity from which we have struggled to recover ever since.”
First
try to detect the misconceptions, then check against our debunking. If there
isn't a match, you can acquire the necessary foundation knowledge in our POSTS, BOOKS, PAPERS, LINKS or, better,
organize one of our on-site SEMINARS, which can be
customized to specific needs.
Note: About TYFK posts (Test Your Foundation Knowledge) see the post insert below.
“But if you still want to compare NOSQL databases with RDBMS, they primarily vary in
1. "normalization" where RDBMS contains normalized (upto certain degree) data and NOSQL based database contains non-normalized data;
2. RDBMS based databases are (I MUST say, generally and it isn't a criteria) fully ACID compliant while NOSQL databases are partially ACID compliant.
3. RDBMS are much slower and difficult to scale while NOSQL databases are much faster and easily scalable.
4. RDBMS normalization was very useful 50 years ago when cost of disk and memory was high, and computation power was limited. With the revolution in computing power, cheapest disk and memory availability has made RDBMS normalization a matter of joke - many people do not really understand why they need to normalize data in today's time.”
First try to detect the misconceptions, then check against our debunking. If there isn't a match, you can acquire the necessary foundation knowledge in our POSTS, BOOKS, PAPERS, LINKS or, better, organize one of our on-site SEMINARS, which can be customized to specific needs.
Note: In what follows RDBMS refers to a truly relational DBMS (of which currently aren't any), not to be confused with a SQL DBMS.
In an old article I used a Hay-Ross exchange to illustrate how disregard for fundamentals and the associated name proliferation -- which underlies the industry's fad-to-fad tradition -- cause confusion that inhibits understanding of conceptual modeling for database design. A recent LinkedIn exchange -- hardly unique -- showed the article to be as relevant today as it was two decades ago, prompting me to bring it up to
date.
In Part 1
we reiterated pertinent fundamentals. Here is the re-written article
-- try to apply the fundamentals from Part 1 before you proceed with our debunking.
“Data modelling, star schema, snow flakes, data vault. Implementing virtual data warehouses (many stage to modify relationships). Normalisation (using a lot of surrogate keys) all for the sake of business reporting analytics. Reason a SQL DBMS approach columns rows is mandatory.”
--LinkedIn
This recent "comment" reminded me of a decades-old article I published in response to a critique by David Hay of the "fact model" then newly proposed by Ron Ross as an "alternative to the data model". In a Letter to the Editor, Hay correctly observed:
“In our industry, there is a strong desire to put names on things. This is natural enough, given the amount of information that we have to classify and deal with in our work. To give something a name is to gain control over it, and this is not necessarily a bad thing. The problem is when the name takes the place of true understanding of the thing named. Discourse tends to be the bantering of names, without true understanding of the concepts involved.”
of which the above comment is an exquisite example.
Note: About TYFK posts (Test Your Foundation Knowledge) see the post insert below.
“A relational database stores information in a structured format called a schema. This schema is defined according the rules of database normalization. These rules are meant to ensure the integrity of the data. The schema for a database is broken up into the objects such as tables and constraints. Tables hold your data and are broken down into rows. each row represents a single entity such as a person and has columns which define the attributes of the entity such as age. Constraints define limitations around the data. For example a check constraint might limit the range of valid dates in a datetime column. From there queries can be run to extract data from the database. These queries will often join multiple tables to pull data from them.”
First try to detect the misconceptions, then check against our debunking. If there isn't a match, you can acquire the necessary foundation knowledge in our POSTS, BOOKS, PAPERS, LINKS or, better, organize one of our on-site SEMINARS, which can be customized to specific needs.
“RDBMS stands for Relational Database Management System. RDBMS is the basis for SQL, and for all modern database systems like MS SQL Server, IBM DB2, Oracle, MySQL, and Microsoft Access. RDBMS store the data into collection of tables, which might be related by common fields (database table columns). RDBMS also provide relational operators to manipulate the data stored into the database tables. An important feature of RDBMS is that a single database can be spread across several tables. This differs from flat-file databases, in which each database is self-contained in a single table. 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.”
The question got 18 answers online, but none came even close to being correct. This is the only one that merits debunking -- the rest will be posted on LinkedIn as "To laugh or cry?".
Note: While the question is about database, due to routine interchangeable use of database and DBMS, we suspect the intention was DBMS. Our debunking applies to database, and our correct answer makes the proper distinction.
First try to detect the misconceptions, then check against our debunking. If there isn't a match, you can acquire the necessary foundation knowledge in our posts, BOOKS, PAPERS or, better, organize one of our on-site SEMINARS, which can be customized to specific needs.
