This is a written follow-up to a talk presented at a recent Strata online event.

A new breed of startup is emerging, built to take advantage of the rising tides of data across a variety of verticals and the maturing ecosystem of tools for its large-scale analysis.

These are data startups, and they are the sumo wrestlers on the startup stage. The weight of data is a source of their competitive advantage. But like their sumo mentors, size alone is not enough. The most successful of data startups must be fast (with data), big (with analytics), and focused (with services).

Setting the stage: The attack of the exponentials

The question of why this style of startup is arising today, versus a decade …

My friends over at IA Ventures are looking both for an Analyst and for an Associate to their team.  If Big Data, New York and start-ups is in your blood then I can’t think of a better VC to be involved in. 

From the IA blog:

"IA Ventures funds early-stage Big Data companies creating competitive advantage through data and we’re looking for two start-up junkies to join our team – one full-time associate / community manager and one full time analyst. Because there are only four of us (we’re a start-up ourselves, in fact), we’ll need you to help us investigate companies, learn about industries, develop investment theses, perform internal operations, organize community events, and work with portfolio companies—basically, you can take on as much …

In life there are really two major types of data analytics.  Firstly, we don’t know what we want to know – so we need analytics to tell us what is interesting.  This is broadly called discovery.  Secondly, we already know what we want to know – we just need analytics to tell us this information, often repeatedly and as quickly as possible.  This is called anything from reporting or dashboarding through more general data transformation and so on.

Typically we are using the same techniques to achieve this.  We shove lots of data into a repository of some from (SQL, MPP SQL, NoSQL, HDFS etc) then run queries/ jobs/ processes across that data to retrieve the information we care about.  

Now this makes sense for data discovery.  If we don’t know what we want to know, having lots of data in a big pile that we can slice and dice in interesting ways is good.   But when we already know what …

It is a constant, yet interesting debate in the world of big data.  What scales best?  OldSQL, NoSQL, NewSQL?

I have a longer post coming on this soon.  But for now, let me make the following comments.  Generally, most data technologies can be made to scale - somehow.  Scaling up tends not to be too much of an issue, scaling out is where the difficulties begin.  Yet, most data technologies can be scaled in one form or another to meet a data challenge even if the result isn’t pretty. 

What is best?  Well that comes down to the resulting complexity, cost, performance and other trade-offs.  Trade-offs are key as there are almost always significant concessions to be made as you scale up.

A recent example of mine, I was looking at scalability aspects of MySQL.  In particular, MySQL Cluster.  It is …

Well as predicted, with Aster Data recently being picked up by Teradata most of the key new generation MPP distributed analytics vendors have been acquired (Aster Data, Vertica, Netezza & Greenplum).  This had to happen and was expected to happen.  The MPP Analytics startup “revolution” is over and these technologies will now be integrated into the mainstream.

So what’s next?  As we now, if you are a massive multi-national software company it is a lot less risky to incrementally innovate and leave the development of “game changing” technologies to startups that can be acquired after they prove both the tech and the market.  So what follows MPP? …

There are so, so many big data platforms in play at the moment it can be confusing for developers to know where to start.  For startups it used to be simple, MySQL, but dust clouds were created when all the NoSQL platforms started to crash the party 18 months or so ago.  But I do see the dust begin to settle and we are starting to see some market “leaders” appear.  A very unscientific approach is to list the technologies I hear about in the “big data startup” world on a daily basis.  These are, in no particular order:

• MySQL - yes it is still very much hanging in there despite the Oracle acquisition.  MySQL has been helped by technologies such as AWS RDS and Xeround making it more digestible for big data startups who want to minimize operational overheads.
• …

“NoSQL”, for lack of better name, is a generic term that describes any data management system that does not use SQL as a query interface.  Generally this means any data management system that is non-relational, but the term also has also been stretched as far to include the boundaries of what constitutes a data management system at all (such as Hadoop).

Early on (a couple of years back in NoSQL time) when the term was coined I think the positioning was much more aggressive, but more recently this has been softened so now NoSQL is commonly quoted as meaning of “Not only SQL” or “next generation databases” (whatever that means).  The common message you get now is something along the lines of NoSQL systems are more “specialized”, each being designed to solve a smaller number of problems than the …

With IBM intending to acquire Netezza the predicted consolidation in the distributed analytics market is well underway.  Recent deals include EMC/Greenplum Teradata/Kickfire and now IBM/Netezza.  A good breakdown of this deal is on Curt’s blog.  There is still more to go of course with one of the crown jewels, Vertica, still ripe for the picking. 

What this indicates is that MPP analytics has moved from the innovative edge into the mainstream market and now the more risk adverse large caps and now willing to invest substantially in growing this market.  Interestingly Microsoft made this move early with the …

I will be at VLDB 2010 next week.  If anyone on this blog is attending and wants to catch up to discuss start ups and innovation in DB, NoSQL, Big Data etc drop me a line and I will try to meet up.

Guest blogger Pamela Samuelson is the Richard M. Sherman Distinguished Professor of Law and Information at the University of California, Berkeley. She teaches courses on intellectual property, cyberlaw, and information privacy, and she has written and spoken extensively about the challenges that new information technologies pose for traditional legal regimes. A version of this material is scheduled to appear in the November 2010 issue of Communications of the ACM.

Two-thirds of the approximately 700 software entrepreneurs who participated in the 2008 Berkeley Patent Survey report that they neither have nor are seeking patents for innovations embodied in their products and services. These entrepreneurs rate patents as the least important mechanism among seven options for attaining competitive advantage in the marketplace. Even software startups that hold patents regard …