Sunday, January 25, 2015

Weekly Update

1. Quote of the Week
I like a GUID as a primary key on every table so that I can uniquely identify that row. For consistency I'll call it "UID" and defined as a NewSequentialID. I'm aware of the various discussions that have been had regarding Guids vs sequences vs COMB etc., etc., but for me any performance issues have occurred in the size of databases I've worked with. The ability to create a new UID as part of an insert is of huge benefit to reduce round trips if you're handling that kind of thing from within a business layer outside of the database server.

2. To Laugh or Cry?
Data Principles

3. Online debunkings
Comments on Relational Fidelity & Analytics Integrity
Comments on Data Model: Neither Conceptual, Nor Logical, Nor Physical Model

4. Elsewhere
Thou shalt not commit logical fallacies

5. And now for something completely different

Sunday, January 11, 2015

Weekly Update

1. Quote of the Week
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.
2. To Laugh or Cry?
Types of database management system and their evolution
3. Online debunkings
4. Elsewhere
Big Data is Dead!
5. And now for something completely different

Monday, January 5, 2015

Silicon Valley SQL Server UG Presentation

"To Laugh or Cry?" Test Your Foundation Knowledge

Tuesday, January 20, 2015 6:30 PM

1065 La Avenida
Building 1
Mountain View, CA  (map)

RSVP here

You are a DBMS ace, able to squeeze every ounce of performance out of it. But how about your foundation knowledge, how good a grasp of data fundamentals do you possess? Are you a data management ace too?

The two are distinct and while the former is necessary for a career, it is insufficient for an informed, intelligent, reliable and productive data management practice. The industry is full of myths, misconceptions and traps and without foundation knowledge you are unable to see through them.

This is your opportunity to test yourself. If your instinct is neither to laugh, nor to cry at the contents of this presentation, education may be in order.

• How misconceptions that you are unaware of, lead you astray;
• Practical implications thereof;
• How foundation knowledge, scarce in the industry, is the only way to see through them.

Property Rules & Domains

Data Fundamentals for Analysts Blog @All Analytics: January post.

Thursday, January 1, 2015

New Version and Revision of Papers

V.1 of paper #6, Domains: The Database Glue, was a significant revision of a chapter in my 2003 book. I have just completed V.2, which is a brand new version, a complete overhaul.

I strongly recommend those who purchased the paper to email me for a free copy.

Table of Contents


1. Properties, Domains and Types
1.1. Data Types
1.2. Domains and System-defined Types
1.3. Domains vs. Types
1.4. Attributes, Columns and Meaningful Comparability
1.5. Value Atomicity

2. Domain Types
2.1. User-defined Domains
2.2. Simple and Complex Domains

3. Domains and SQL

4. Some Practical Implications
4.1. "Universal" DBMS
4.2. Entities or Properties?
4.3. R-tables or Objects?
4.4. Tackling Complexity



I have also revised paper #1, Business Modeling for Database Design, from V.3 to V.3.1, with respect to domains and data types, for consistency with the new version of paper #6.

Those who purchased the paper in 2014 should email me for a free copy; 2013 buyers are eligible for a 50% discount.

Table of Contents


1. Business Modeling
1.1. Basic Modeling Concepts
1.2. Business Rules
1.2.1. Property Rules
1.2.2. Class Rules
1.2.3. Associative Entities
1.3. Business Models

2. Database Design
2.1. Formalizing the Informal
2.2. Predicates and Propositions
2.3. The Relational Data Model
2.3.1. Relational Structure
2.3.2. Relational Manipulation
2.3.3. Relational Integrity External Predicates Internal Predicates
2.4. Logical Models

3. Understanding Database Management
3.1. Note on missing values
3.2. A Foundation Framework

Appendix A: Constraint Formulation and Verification

Appendix B: Integrity Constraints in Dataphor’s D4

Appendix C: Applying the Framework