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March blog post @All Analytics:

They should be represented in the database by integrity constraints -- not perfect substitutes, because they are very loose approximations to the rules -- but if they are enforced in the database by the DBMS they are usually recorded either in the definition statements that created the tables and constraints, or the database catalog.

Read it all

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See the series starting at

The First Normal Form in Theory and Practice Part I

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March 28, 11:15,  Mountain View

MEANINGLESS, BUT CONSISTENT: DATABASE TRUTH VS. CORRECTNESS

You're a SQL Server ace: your ability to squeeze everything from SQL and your performance tuning skills are unparalleled, but do you know what your tables really mean and, therefore, what queries make sense and whether results are correct and their interpretations sensible? This is a critical part of data fundamentals, the grasp of which is poor. It is a subject usually neither much covered in education, nor part of job requirements and industry dialogue, yet can defeat the entire purpose of your DBMS expertise. This presentation covers

• Meaning, business rules and table interpretations;
• Types of business rule; 
• Meaning and database truth; 
• Business rules, integrity constraints and database consistency; 
• DBMS and user reponsibilities.

Session Level: Intermediate

Event full details

Contact: Mark Ginnebaugh  mark@designmind.com

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1. Quote of the Week

what is an index in database? how can it make the search faster? please help me understanding this. Project Manager Technical
--LinkedIn.com

Note the job title.


2. To Laugh or Cry?

Tableau Data Modeling Resolving Many to Many Relationship

3. Online debunkings

Comments on Codd's Marks are not SQL's NULLs

4. Interesting elsewhere

• Lies, Damn Lies And The Myth Of Following The Data
• Surviving Data Science at the Speed of Hype

5. And now for something completely different 

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My February post @All Analytics.

To apply manipulation that makes sense to data originating in database tables and interpret results correctly, the analyst must know the meaning of the table(s) -- the underlying business rules. For tables designed to represent facts about a single class of entities each, the analyst should expect two categories of rules: property rules (discussed last month) and class rules, of which there are several types.

Read it all. (Please comment there, not here)



 

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1. Quote of the Week

The most visible limitation of the relational model has been its inability to handle multimedia files, but the importance of this has been overstated. In fact, the relational model has some far more significant limitations that have not yet been challenged:

Every new relational application needs a new set of programs developed from scratch, which is labour-intensive, expensive and wasteful.

Relational applications cannot be readily tailored to the needs of large numbers of individual users, which is an issue for ASPs.

Relational applications cannot record a piece of information about an individual thing that is not relevant to every other thing of the same type. This limits our ability to continually improve customer service levels.

Information about identical things in the real world is structured differently in every relational database, so it is difficult and expensive to amalgamate two databases."
--Simon Williams, The Associative Data Model

2. To Laugh or Cry?

• Rise of the Multi-Model Database
• 42% of Database Pros Struggle to Manage NoSQL Solutions

3. Online debunkings

• Alternate Keys, Normal Form question
• With large hacks, how should we change database design

4. Interesting elsewhere

Rare Alan Turing journal shows his genius at work"

It's clear that fundamental logic is at the heart of computer science and everything we do--and in that sense it's clear the whole field owes Turing so very much" ... But in a sense, it also shows how far we've come."

Including away from logic.

5. And now for something completely different

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GE: The future in data modeling is Object Role Modeling (ORM). It is a far superior way to approach data modeling (compared to any record-based methods such as relational) that avoids all the pitfalls of "Table Think" and the necessity of normalization.

Big data or any other kind of data--you still need to know your data and what it represents. That is the myth in big data--that you don't need a schema, i.e., knowledge of what the data means. True you may not need a SQL schema in Oracle, but you do need to know your data. You need to have names for things (that is the vocabulary) and their relationships.