Professor Roberto Zicari of ODBMS.org recently posted his interview with Amazon.com CTO and VP Dr. Werner Vogels about the future of big data, and where Amazon is going with it.

Within the article, Zicari reflects upon Vogels’ mention of a book called The Fourth Paradigm: Data-Intensive Scientific Discovery (you can download a free PDF version of the book here).  The book is a collection of essays that expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.  Also, there’s an interesting article (titled Sailing in an Ocean of 0s and 1s) that talks about The Fourth Paradigm.

You can read the interview with Dr. Vogels here…

On December 7, Strata will be hosting a free online conference about moving to big data.  Here’s a quick synposis of the conference agenda, scheduled to last from 9 AM to 10:30 AM Pacific time:

• Introduction:  Dealing with New Expectations – Alistair  Croll (Bitcurrent)
• Top-Down:  What CEOs Can Do to Accelerate Data Mindsets – Diego Saenz (Data Driven CEO), Jonathan Bruner (Forbes Media)
• Web Analytics: The Enterprise Gateway Drug to Big Data? – Justin Cutroni (WebShare Design LLC)
• Take a Lesson from the Research World – Kaitlin Thaney (Digital Science)
• How Will Deep Q&A Impact the Use of Analytics in Business? – Christer Johnson (IBM)

Strata is sponsored by the technical publisher O’Reilly and has become one of the fastest growing forums for big data and data science applications.  Upcoming events include another Strata Online Conference, scheduled for January 25, 2012, and the Strata Conference, calendared for February 28-March 1, 2012, in Santa Clara, CA.

Wired’s Jon Stokes has a really interesting post on HP’s newly unveiled data server strategy to address two different big data challenges.

As Stokes describes (and which he also mentions really comes from a presentation by Twitter’s Nathan Marz), there are “fast” big data and “slow” big data problems.   For the “fast” problems, you apply a set of pre-developed algorithms and tools to the incoming datastream, looking for events that match certain patterns so that your platform can react in real-time.  However, sometimes you need to ask questions of the data, and then analyze the results, which can’t be done in real-time effectively.  This describes the “slow” problems, or as Stokes puts it, where you gather information and test hypotheses by running queries against a vast backlog of historical data.

It turns out that the natural evolution of analytics is to go from “slow” problems to “fast” problems, turning the inquisitive understanding of the data, requiring analysis, into faster number-crunching analytics.  Knowing the right way to generate these “fast” analytics requires an solid analytics engineering discipline, especially when the problems being answered get harder and harder. 

Big data solutions are currently focused on speed and data management platforms, but there is still a need for understanding the science behind developing the right analytics.

In a post today, Dan Kusnetzky of ZDNet interviews MapR Technologies CEO, John Schroeder, about his views on big data, Hadoop, and what his company is doing in this space.

Here’s a little from Kusnetzky’s post on his thoughts on “big data” (which I think are “spot on”):

What is big data?

A while ago, I posted What is “Big Data?”. Here’s a segment of that post.

“Big Data” is a catch phrase that has been bubbling up from the high performance computing niche of the IT market. Increasingly suppliers of processing virtualization and storage virtualization software have begun to flog “Big Data” in their presentations. What, exactly, does this phrase mean?

If one sits through the presentations from ten suppliers of technology, fifteen or so different definitions are likely to come forward. Each definition, of course, tends to support the need for that supplier’s products and services. Imagine that.

In simplest terms, the phrase refers to the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets and storage facilities. Does this mean terabytes, petabytes or even larger collections of data? The answer offered by these suppliers is “yes.” They would go on to say, “you need our product to manage and make best use of that mass of data.” Just thinking about the problems created by the maintenance of huge, dynamic sets of data gives me a headache.

You can read more of Kusnetzky’s most recent post here…

Next week, Hadoop World will be taking over the Sheraton New York Hotel and Towers in Manhattan.  Sessions will be held on Tuesday and Wednesday, November 8 and 9, with training and certification sessions being held the other days that week.

Companies such as Facebook, bit.ly, Etsy, Netezza, Explorys, and CBS Interactive will be giving presentations on deploying Hadoop across the enterprise.  Also, Cloudera will be holding training and certification sessions focused on Hadoop essentials, administration, development and related technologies, including Apache Hive, Apache Pig and Apache HBase.

Click here for the full agenda, and here for the list of speakers…

The Boston Business Journal is reporting that big data storage firm Basho Technologies raised $5 million in its latest round of financing.  According to the report, this brings Basho’s total to $12.5M raised in 2011, adding to the $7.5M total they raised back in May.

Basho makes highly distributable and scalable non-relational databases, which are needed for handling and managing the incredibly large datasets now available.  These types of technologies are some of the hottest offerings in the market right now, indicated by the ability of these firms to raise significant capital.  Basho, founded in 2008 by a group of software architects, engineers, and executives from Akamai, recently announced the licensing of their NoSQL database technology, Riak, to the National Board of E-Health in Denmark to operate their nationwide medical prescription card program.