I’ve worked with every release of Microsoft Excel, and I know it takes effort to keep up to date with certain releases. Clearly, the Data Analysis eXpression (DAX) Language introduced in Excel 2010 went unnoticed by many, which was sad. DAX is truly a powerful extension to the analytical and modeling approaches in Microsoft Excel.

I’d like to recommend Microsoft Excel 2013 Building Data Models with PowerPivot to those who haven’t learned how to use DAX in Excel 2010, 2011, or 2013. DAX works with tables but if you don’t use tables, I guess you can skip DAX …

3Ci processes over a billion transactions a month. More than 100 million unique U.S. consumers have engaged with a business through our platform. All that activity creates massive amounts of data. The Data Team at 3Ci is responsible for keeping our offerings running at optimal performance and for making sense of our data. They manage MySQL [...] …

Join us next Friday in Santa Clara for a free day of learning and fun from the SkySQL & MariaDB gang & their partners

We’re proud to announce that Jim Tommaney, CTO of Calpont, has just signed on to speak at the MySQL & Cloud Database Solutions Day, hosted by SkySQL and MariaDB - taking place next Friday, April 26, directly after Percona Live: MySQL Conference & Expo.

read more

Join us next Friday in Santa Clara for a free day of learning and fun from the SkySQL & MariaDB gang & their partners

We’re proud to announce that Jim Tommaney, CTO of Calpont, has just signed on to speak at the MySQL & Cloud Database Solutions Day, hosted by SkySQL and MariaDB - taking place next Friday, April 26, directly after Percona Live: MySQL Conference & Expo.

read more

Join me for a webinar where I discuss how the recent changes and trends in big data management effect the enterprise.  This event is sponsored by Red Rock and RockSolid.

Overview:

It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward. 

These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to …

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 …

Reading yesterday that the NSA has submitted a proposal to Apache to incubate their Accumulo platform.  This, according to the description, is a key/value store built over Hadoop which appears to provide similar function to HBase except it provides “cell level access labels” to allow fine grained access control.  This is something you would expect as a requirement for many applications built at government agencies like the NSA.  But this also is very important for organizations in health care and law enforcement etc where strict control is required to large volumes of privacy sensitive data.

An interesting part of this is how it highlights the acceptance of Hadoop.  Hadoop is no longer just a new technology scratching at the …

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.

-----------------------------------------------------------------------------------------------

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 …

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 …