The presentation that goes along with this post is available here In my last post I went over the value cycle of machine generated data. In this post, I want to follow up with a few ideas on how to further amplify value of that data by expanding its context beyond the walls of owning organization, in a construct we came to know as Data Exchange, and list a few innovation opportunities in each one of these areas.
This is one of those posts you write on your phone while getting sprayed for 11+ hours with bathroom chemicals in minimally reclining seat in a second to last row of a transatlantic flight. Still, I’m going to try to be as constructive as my thumbs allow.
Over the holidays, as many of us do, I embarked on a little extra-curriculum development effort I called thingz.io. I was driven by the pattern I’d observed in Data Center (DC) monitoring products; although that pattern also exists in many of today’s Internet of Things (IoT) solutions.
As part of my recent solution review, I wanted to compare a few performance metrics specific to multi-node data service deployment on different clouds. This post is about my experience with Google Compute Engine (GCE) as part of that evaluation.
I am excited to share with you today that starting Monday I will be joining the Big Data team at Intel. Yes, Intel. While not traditionally known for its software offerings, Intel has recently entered the Big Data space by introducing their own, 100% open source Hadoop distribution with unique security and monitoring features.
The “high-priests” of Big Data have spoken. Hadoop Distributed File System (HDFS) is now the de facto standard platform for data storage. You may have heard this “heresy” uttered before. But, for me, it wasn’t until the recent Strata conference that I began to really understand how prevalent this opinion actually is.
We are in a midst of drastic shift in application development landscape. Developers entering the market today use different tools and follow different patterns. One of the core patterns of on-line application development today is cloud scale design. While traditionally architectures would rely on more powerful servers, today, that approach simply does not scale.
Over the last decade, the access to best-of-bread data technologies has become easier. This is due mainly to the increasing popularity of open source software (OSS). While this phenomenon holds true in other areas like operating systems, application servers, development frameworks or even monitoring tools, it is perhaps most prevalent in the area of data.
Over eight months ago, I joined Intel to work on their next-generation data analytics platform. In large, my decision was based on Intel’s desire to address the “voodoo magic” in Big Data: the complexities that require deep technical skills which are preventing domain experts from gaining access to large volumes of data.
Last week I had a chance to attend the 3rd AWS re:invent conference in Vegas. I’m not a big fan of that city myself, but, as in previous years, re:invent has not disappointed. Much coverage has been dedicated to the newly introduced services; I won’t bore you with that.