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UPDATE 1: I have added Jim Starkey's reply to David's initial response (with my brief comments) and David's reply to it below.

UPDATE 2: I have made a few minor corrections and fixed end-note formatting problems.

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1. I will give the following presentation

Big Data, Analytics and Normalization
"Big Data may offer analytical insights, but with almost certainty will produce really big lies from 100% correct data", particularly when data are from external sources. This presentation will demonstrate

• Why and how
• How to protect yourself

Wednesday, 5/14, 7:00pm
Microsoft San Francisco office
835 Market St., 7th Floor
San Francisco

For more information contact MGinnebaugh@designmind.com.


2. My April column @All Analytics:

Missing Data, Databases & Analytics

3. Quote of the Week

Q: Is it necessary to follow standards during SQL programming? 
 
A: Standards and Best Practices usually come from common sense. I want to point out that it is God given potential which one must realise and be conscious to utilize it for His glory. --LinkedIn.com

4. To Laugh or Cry?

A data model of the SAP Bill of Material Explosion tables

5. Online 

• Will Pattern Discovery Systems Trump Analyst's Rules-Based Models?
• Why don’t RDBMS products support sub-typing?
• Which of these two data model fragments is normalised and to what extent?

6. Interesting elsewhere

Big Data, Little Happiness

(requires free registration)


7. And now for something completely different

The Death Of Expertise

Today being the anniversary of the Holocaust, I decided to add the following:

Berlusconi's holocaust jibe provokes German outrage

The irony of Italians badmouthing the Germans about the extermination of Jews. But this time the former spoke the truth:the latter cannot have it both ways.

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As told by C. J. Date, sometime shortly after the introduction of SQL DBMS's in the industry, when non-relational products e.g. hierarchic and network reigned and the relational idea was a very hard sell, he and Michael Stonebraker (the author of Ingres and at the time a professor of Computer Science at University of California Berkeley) participated in a panel at a technical conference. The following is the (praphrased) exchange between them:

CJD: The reality is that most practitioners are too set in their non-relational ways and we cannot expect them to understand and appreciate the relational model. Rather, we must focus on the young generation of practitioners, who learn the relational model at university.

MS: Chris, you don't understand. I am teaching those youths: they were not around when we struggled with the huge problems of the pre-relational systems and they are reinventing all of them!

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1. Quote of the Week

Now that we have seen a lot of information about NoSQL databases, it is interesting to drop back and look around at how much NoSQL stuff we already have in our organizations. I had never thought of a file system as a database, but it is. The comparison is fascinating. File systems don’t impose any structure on the data that is stored in any given file. There is a key value relationship to each file. There is little control over concurrency beyond file locking. This is very similar to NoSQL, with locking only at the aggregate level. File systems are cheap; everyone has one and they hold huge amounts of data on multiple nodes. --Book review, NoCOUG Journal

2. To Laugh or Cry?

Find GUID in Database

3. Online exchanges

• Consistency...when do you truly need it?
• Is there any difference between Fortress Model and Data Model?
• Which is better practice Composite Key vs Surrogate Key?
• Big data 5nf-fifth normal form
• How to model ternary associative entity with a binary constraint?

4. Interesting elsewhere (corrected first link and added a second):

• CREATE ASSERTION Impossible Dream (by Erwin Smout)
• Why ASSERTIONs Are Important (by Toon Koppelaars)

5. And now for something completely different

Joe Biden wants to nominate Obama for sainthood

The VP of the world's "superpower".

Stanford opening new lab to study bad science

And tomorrow we'll need a lab to study bad science in the study of bad science.

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In What the $&@#^ is Applied Big Data, venture capitalist Greg Sands raises an issue about which I as a scientist (and not just a "data scientist") have often expressed concern. 

"The world is awash in data. Figuring out what to do with it is the problem. The press is littered with reports about Big Data. Many CIOs report that their CEOs have come to them and said, “We need some of that Big Data.” That often means make sure we’re collecting all the available data, often deploying a new Hadoop-based infrastructure to store and analyze it. After this elaborate process and extensive investment, they’ll start mining to figure out if there are critical insights that come out of the data. We see many entrepreneurs that start the same way. Aggregate data and look for a problem."