I feel a sense of pride when I think that I was involved in the development and maintenance of what was probably the first piece of software accepted into Debian which then had and still has direct up-stream support from Microsoft. The world is a better place for having Microsoft in it. The first operating system I ever ran on an 08086-based CPU was MS-DOS 2.x. I remember how thrilled I was when we got to see how my friend’s 80286 system ran BBS software that would cause a modem to dial a local system and display the application as if it were running on a local machine. Totally sweet.

When we were living at 6162 NE Middle in the nine-eight 292, we got an 80386 which ran Doom. Yeah, the original one, not the fancy new one with the double barrel shotgun, but it would probably run that one, too. It was also …

The brief outage was due to a scheduled move of the servers to a separate rack and subnet dedicated to our work with the Center for Information Assurance & Cybersecurity (ciac) at the University of Washington Bothell (uwb), and a11y.com

I am currently exercising the new (to us) equipment and hope to winnow the less than awesome equipment over the next quarter. I spent the last six months finding the best in breed of the surplussed DL385 and DL380 chassis we (work) were going to have recycled. The team and I were able to find enough equipment to bring up one of each with eight and six gigs of memory, respectively. These will make excellent hypervisors for provisioning embedded instances of Slackware, Fedora, RHEL, CentOS, Debian, FreeBSD, OpenSolaris, OpenIndiana, FreeDOS, etc.

When I initially configured this xen paravirt environment, I failed to plan for integration with libvirt, so I am now re-jiggering the software bridges so …

When participating recently in a sprint held at Google to document four free software projects, I thought about what might have prompted Google to invest in this effort. Their willingness to provide a hotel, work space, and food for some thirty participants, along with staff support all week long, demonstrates their commitment to nurturing open source.

Google is one of several companies for which I'll coin the term "closed core." The code on which they build their business and make their money is secret. (And given the enormous infrastructure it takes to provide a search service, opening the source code wouldn't do much to stimulate competition, as I point out in a posting on O'Reilly's radar blog). But they depend on a huge range of free software, ranging from Linux …

Often I think about challenges that organizations face with “Big Data”.  While Big Data is a generic and over used term, what I am really referring to is an organizations ability to disseminate, understand and ultimately benefit from increasing volumes of data.  It is almost without question that in the future customers will be won/lost, competitive advantage will be gained/forfeited and businesses will succeed/fail based on their ability to leverage their data assets.

It may be surprising what I think are the near term challenges.  Largely I don’t think these are purely technical.  There are enough wheels in motion now to almost guarantee that data accessibility will continue to improve at pace in-line with the increase in data volume.  Sure, there will continue to be lots of interesting innovation with technology, but when organizations like …

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 …

Friday night, I tweeted a link to a Guardian article stating that app developers were withdrawing apps from Apple's app store and Google's Android market (and presumably also Amazon's app store), because they feared becoming victims of a patent trolling lawsuit. That tweet elicited some interesting responses that I'd like to discuss.

The insurance solution?

One option might be to rely on the insurance industry to solve the problem. "Isn't this what insurance is supposed to be for? Couldn't all these developers set up a fund for their common defense?" wrote @qckbrnfx. An interesting idea, and one I've considered. But that's a cure that seems worse than the disease. First, it's not likely to be a cure. How many insurance companies actually defend their …

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? …