Wednesday, January 30, 2013

OBIEE Bundle Patch 11.1.1.5.5 Available

Bundle Patch 11.1.1.5.5 for Oracle Business Intelligence Enterprise Edition, also known as 11.1.1.5.0 BP5, is now available for download from Oracle Support.

The easiest way to locate this bundle patch on Oracle Support is to search under the "Patches & Updates" tab with the id 15887317 - because that is the id of this patch.
21 bug fixes have been delivered in this bundle patch. Since bundle patches are cumulative, it also contains bug fixes from the earlier updates, including 11.1.1.5.0PS111.1.1.5.0BP2, 11.1.1.5.0BP3, and 11.1.1.5.0BP4

Furthermore, this bundle patch is available for the following eight platforms:
  • Microsoft Windows (32-bit) 
  • Microsoft Windows x64 (64-bit) 
  • Linux x86 (32-bit) 
  • Linux x86-64 (64-bit) 
  • Oracle Solaris on SPARC (64-bit) 
  • Oracle Solaris on x86-64 (64-bit) 
  • IBM AIX PPC (64-bit) 
  • HPUX- IA (64-bit) 


Why are bundle patches recommended? They are recommended because these are a collection of critical bug fixes for a product or specific products. These patches can sometimes also contain minor enhancements as well as security updates. These patches undergo a rigorous set of tests before they are released for consumption by customers. Furthermore, these bundle patches are cumulative in nature, which means a bundle patch contains the contents of previous bundle patches that have been released. Therefore, to take a simple example, if you are on version 11.1.1.5.0, you do not have to install any of the bundle patches that have released since, and can apply bundle patch 11.1.1.5.5, secure in the knowledge that this bundle patch contains all the updates that went into bundle patch 11.1.1.5.4, which in turn contained all the fixes that went into bundle patch 11.1.1.5.3, and so on and so forth.

As always, you should carefully go over the documentation accompanying the bundle patch and come to a considered decision on applying the patch.

Thank you.
Abhinav,
30 Jan, 2013
Bangalore

Free Big Data Workshop

It's a one-day workshop. It's on Big Data. It's free. What more could you want?
Ah yes, some details. You will find them on Keith Laker's blog post, Data Warehousing and Big Data from an Oracle Perspective: OTN Developer Day - Free Oracle Big Data Workshop

The bare bones about the Big Data workshop are:
When: Wednesday, February 20, 2013
Where:  Oracle's HQ in Redwood Shores, California
How Much: Free.
Ok, seriously, how much? It's Free
What will you learn: As much as you want to.
What will be taught:

  • Write MapReduce on Oracle’s Big Data Platform
  • Manage a Big Data environment
  • Access Oracle NoSQL Database
  • Manage Oracle NoSQL DB Cluster
  • Use data from a Hadoop Cluster with Oracle
  • Develop analytics on big data
Anything more? Yes, check Keith Laker's blog post for more.

Happy second last day of Jan, 2013
Abhinav,
Banglaore

Tuesday, January 15, 2013

Enterprise Analytics Book - Review


I was somewhat excited and was looking forward to reading this book, given that it firstly is on my area of work at Oracle - enterprise analytics, secondly because it covers an important, growing, and still nebulous area of both the technology and business of Big Data, and thirdly, because the author is a very well-respected authority on the field and who shot to wider fame with his now famous 2007 article in the Harvard Business Review, "Competing on Analytics". With this preamble, I submit to you, my review of the book, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data (FT Press Operations Management)", Thomas H. Davenport, et al.

"Lectures, Meanders, Pontificates, But Does Not Educate"
(AmazonMy Amazon review, Kindle US, Amazon UK, Kindle UK, Flipkart)
Or, how a book on Big Data, Enterprise Analytics, and technology can neatly skirt any meaningful discussion of Big Data, Enterprise Analytics, and technology.

While a few chapters stand out for their reasoning and clarity, what is jarringly absent from this book is any meaningful, technical discussion about Big Data itself. Without such a discussion, most of the book's content can be recycled with minimum effort ten years from now and applied to the next big thing in technology. Even assuming that this book is targeted at decision makers and so-called C-level executives, an absence of the nuances and complexities of Big Data mean that executives will be as clueless on that dimension of Big Data knowledge after reading the book as before. If you are responsible for selling sausages, you had jolly well get a look at the sausage factory, if not work there a day.

Big Data, Unstructured Data, the Cloud - if these three buzzwords were not enough, you can add the salsa-ish phrase SoLoMo - i.e. Social, Local, and Mobile, to the mix. Businesses, consultants, enterprises, anyone who is anything in technology wants to know more about what this buzzword alphabet soup is, and how to make sense of it before their competitor does, or worse - a disruptor.

The hope is that if the decision makers, the corner-room occupiers can understand this, they will be better able to drive a coherent process and structure within their organizations to take advantage and benefit. Hence this book.

The book is a collection of eighteen chapters, divided into five parts - the first is an "Overview", "Application", "Technologies", "Human Side", and "Case Studies" of Analytics. Each chapter is written by a different author, with a total of fourteen authors in the fray. A few chapters have been written by the editor himself, Thomas Davenport, and these are among the standout chapters - for their clarity and organization.

Since I work in the technology of analytics, I should be excused for either taking too technical a view of things, or for being too harsh in my criticisms. Having said that, there are at least quibbles that in my opinion leave this book only a middling, mediocre effort, and not one that will be remembered or consulted, much, if at all.

- The close and financial collaboration of at least a few technology vendors with the International Institute of Analytics means that most of the specific examples cited in this book are where the technology vendor's solution was used. Fair enough, but it leaves an aftertaste of an advertorial in the reader's mind.

- When discussing web analytics, there is a lot of ink devoted to the topic of "page views". Page Views are still relevant, but they are becoming increasingly obsolete in the world of AJAX - where parts of a page and its contents can be updated without having to reload the entire page. Web analytics metrics operate at the least granular level of pages, and hence cannot capture a significant chunk of user interactions and engagement that occur on pages and sites that make heavy use of such asynchronous page content refreshes (AJAX does stand for "Asynchronous JavaScript and XML", and no - it does not contain the buzzword "Agile"). More sophisticated measures of user engagement are being built that track more than simple page views. When the chapter's author fixates on page-views without once mentioning the inaccuracies of measurement that AJAX can inject, the credibility of the chapter suffers.

- The chapter on "NBOs", i.e. "Next Best Offers" takes several cheap shots at Amazon (see page 90), which left me wondering whether Amazon had not turned down six-figure consulting offers from either of the authors to warrant this broadside.

- There is a laboured chapter on "engagement" - an attempt to define a compound measure based on essentially a summation of basically arbitrary weighted base measures. For instance, a measure of online engagement is a summation of eight different indices. Putting a "sigma" symbol in the equation makes it look impressive, but in the end it is more arbitrary than methodical. Because decision makers need to have information supplied to them in a simple manner, it is often supplied to them in a simplistic manner, cloaked in technical-sounding phrases.

- Privacy is becoming an increasingly sensitive and relevant issue as more data is collected from customers and users, often without their knowledge, and sometimes without their consent, almost always without providing users a clear picture of what is done with that user-data so collected. Privacy is an important topic in this discussion on enterprise analytics. And it is given short shrift in the book. After a cursory nod, almost as an afterthought, to privacy concerns, there are examples cited that are almost creepy in the extent they suggest the invasion of a user's privacy. Sample these: "The next generation of video game offers could have pictures of your friends or your tastes and interests built right in." Or "A company called Sense Networks has developed an application to help infer a person's lifestyle based on his or her location history." Harvesting a user's location and web-click information should require explicit opt-in - it is basic respect for human decency. Take the section where the authors talk about collecting data by anonymizing data. It has been proven that even after anonymizing data, it is possible to individually identify users with a very high degree of accuracy based on only a few attributes. There is no such thing as truly anonymous user-tracking on the web. Only if you use "unsophisticated marketing techniques" do you risk customer ire over harvesting their "spending habits", which they consider "inviolate". Get it? Be sophisticated, and you can get away with pillaging your customers' privacy. Is this really the new normal - customer privacy shmrivacy?

- Then there is this most curious statement that states - "Despite all the hype around the unstructured data component of "big data", it seems that structured data still rules the in predictive analytics." Well, yes! Unstructured data is fairly recent, especially when compared with structured data, that has been around for literally decades. It is but natural that the use of unstructured data in predictive analytics will take time to gain traction, especially as the technology and means of blending structured and unstructured data evolve.

- Even the chapter, "Predictive Analytics in the Cloud" contains phrases that make absolutely no sense, other than to bump up the chapter's jargon-index. Sample this: "These cloud-based solutions inject predictive analytics into other software that is cloud-based or delivered as SaaS." Is a specific example too much to ask? "inject" is an impressive-sounding word, especially if you have heard of the phrase "sql-injection" in the context of hacking attacks, but just what does the word "inject" mean in the context of predictive analytics and the cloud? And why does this injection require the cloud? Can it not be done with more traditional, hosted solutions? And what exactly are "cloud-based dashboards"??? Is any dashboard served via a browser "cloud-based"?

I could go on an on, but a short summary of the book would be this: each chapter suggests and promises value, but falls short.

In a good mood, as always.
Yours, Abhinav
Jan 15, 2013
Bangalore, India

[Originally posted on my personal blog, at blog.abhinavagarwal.net]

Kindle Excerpt:

 

Friday, January 04, 2013

BIWA Summit 2013

The Oracle Business Intelligence Warehousing and Analytics Summit (aka the BIWA Summit) is taking place at the Sofitel Hotel in Redwood City in California next week, from the 8th - 10th Jan, 2013. You can register here.

This promises to be a fruitful and informative conference, and not only do you get six tracks to choose from, but also an impressive array of speakers, as well as a combination of presentations and hands-on labs.

For instance, the keynote speakers are Vaishnavi Sashikanth, Vice President of Development for Advanced Analytics at Oracle (speaking on "Making Big Data Analytics Accessible"), Tom Kyte, Senior Technical Architect at Oracle (speaking on "What's new from Oracle in BI and Data Warehousing"), Balaji Yelamanchili, Senior Vice President for Business Analytics and Web Center at Oracle (speaking on "Fast and Furious: A Sneak Peak into the Future of Oracle BI"), Mark Rittman, ACE Director, Author, Technical Director Rittman Mead, and Ari David Kaplan, leading authority in sports analytics (speaking on "Sports Analytics in Action"). Use the Keynotes link to view more information about these keynotes and to also add these to your calendar.

The focus areas are a mix of the established, like "Business Intelligence and Visualization", "Data Warehousing and Data Integration", "Spatial Technologies", and also new and upcoming areas like "BigData", and "Advanced Analytics". Add to this Hands-on Labs where you can get to try out well-designed exercises to guide you, step-by-step, through the product and technology, and it should be a must-attend for people interested in the intersection of existing analytics and upcoming technologies. And you will get to meet and talk with the product experts from Oracle, including technologists, leaders, developers, and product managers, who will be as excited to meet with you.

Learn. Enjoy.
Abhinav,
Jan 04, 2013
Bangalore.

Thursday, January 03, 2013

TimesTen and Exalytics - Additional Features

Did you know that Oracle TimesTen In-Memory Database is available in four different licensing options, of which one is "Oracle TimesTen In-Memory Database for Exalytics"? Well, I think you knew that it is available with Oracle Exalytics, but were not sure that it is available in three other licensing options. In any case, what is interesting here is that the TimesTen In-Memory Database for Exalytics "license includes all features available under the Oracle TimesTen In-Memory Database license", but also has two additional features:
  • In-Memory Columnar Compression
  • OLAP Grouping Operators: Cube, Grouping Set, Rollup
From the short but useful paper, "Oracle TimesTen In-Memory Database Licensing Information", published December, 2012.

Read more about Oracle Exalytics In-Memory Machine, Oracle TimesTen In-Memory Database,

And, best wishes to all for a happy 2013.

Take care.
Abhinav
Jan 03, 2012
Bangalore