Sunday, September 27, 2015

Weekly Update

UPDATE: I have posted, via David McGoveran, an update to last week's post on Codd's 12 rules.

Reactions to my presentation "The Real Science: Tables- So What?" to the Silicon Valley SQL Server User Group. 

With regards to Language Redundancy and DBMS Performance: A SQL Story:

1. Quote of the Week

... the challenges inherent in the SQL RDBMS [sic] approach ... the constrained schema (or schema-first) approach of SQL RDBMS engines imposes semantic infidelity rather than fidelity on all applications and services that depend on this RDBMS type, solely ... SQL RDBMS engines (as per what I've outlined above) do impose a "one size fits all" constraint on DBMS driven apps and services that manifests as the "data variety issue" outlined by the "Big Data" meme.

Tuesday, September 22, 2015

Database Fundamentals for Analysts

My September post @AllAnalytics:
The Real Data Science: Tables -- So What?

Sunday, September 20, 2015

Interpreting Codd: 2. The 12 Rules (UPDATED)

I have recently come across an "explanation" of Codd's 12 RDBMS rules in a book appendix posted on line that is a set of mostly rule regurgitations. While they are no longer used to assess the relational fidelity of DBMS's, inquiries about them persist, yet they are still misunderstood.

In the current context of proliferation of non-relational products e.g. NoSQL, there is value in understanding the rules' origins and they can still help expose persistent flaws of SQL implementations and the superiority of RDBMS's over non-relational products. So here are the book "explanations", followed by mine.

Sunday, September 13, 2015

Weekly Update

The Real Data Science: Tables--So What?

My Presentation to Silicon Valley SQL Server User Group

6:30 PM, Tuesday, September 15, 2015

1065 La Avenida, Building 1
Mountain View, CA

Free and open to the public (+ pizza)
For details and RSVP see Meetup

1. Quote of the Week
You see, in Cassandra 1.x, the data model is centered around what Cassandra calls “column families”. A column family contains rows, which are identified by a row key. The row key is what you need to fetch the data from the row. The row can then have one or more columns, each of which has a name, value, and timestamp. (A value is also called a “cell”). Cassandra’s data model flexibility comes from the following facts:
* column names are defined per-row
* rows can be “wide” — that is, have hundreds, thousands, or even millions of columns
* columns can be sorted, and ranges of ordered columns can be selected efficiently using “slices”.
Compare this to the RDM.

2. To Laugh or Cry?

3. Online Debunkings

4. Elsewhere

5. And now for something completely different