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 …

Conor O'Mahony over at IBM wrote a good post on a favorite topic of mine “The Future of the NoSQL, SQL, and RDBMS Markets”.  If this is of interest to you then I suggest you read his original post.  I replied in the comments but thought I would also repost my reply here.

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Hi Connor, I wish it was as simple as SQL & RDBMS is good for this and NoSQL is good for that.  For me at least, the waters are much muddier than that.

The benefit of SQL & RDBMS is that its general purpose nature has meant it can be applied to a lot of problems, and because of its …

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 …

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.
• …

I have been busy travelling recently but thought I would jot down a couple of NoSQL myths that are fresh in my head from my recent discussions.

• Twitter use Cassandra internally but have not migrated their tweet store, despite their earlier plans to.  For now tweets are still stored in MySQL.
• Despite the widely accepted view that the use of Cassandra led to Diggs issues a couple of Digg engineers have apparently discounted this.
• Despite the widely accepted view that NoSQL databases all use eventual consistency this is not so.  HBase, for example, offers full consistency.
• Despite the widely accepted view that NoSQL is only about unlimited distributed scalability this is …

“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 …

Back in 2008 Stonebraker & DeWitt published a paper and associated blog post titled “MapReduce: A major step backwards”.  Their key points being Map Reduce is:

1. A giant step backward in the programming paradigm for large-scale data intensive applications
   
2. A sub-optimal implementation, in that it uses brute force instead of indexing
   
3. Not novel at all — it represents a specific implementation of well known techniques developed nearly 25 years ago
   
4. Missing most of the features that are routinely included in current DBMS
   
5. Incompatible with all of the tools DBMS users have come to depend …