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

The Real Data Science: Tables -- So What?

My September post @AllAnalytics. 

We have seen that if database tables are designed to represent a set of (facts about) a single class of attribute-sharing entities each and to preserve the mathematical properties of relations, databases are easier to understand, and query results are guaranteed to be provably correct and easier to interpret. Let's see how and why with the help of an example.

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


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

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