I just recently returned from the O'Reilly Open Source Conference in Portland, where Infobright was exhibiting. For those of you who have not had a chance to visit Portland, it is a great location for this conference. As you walk into the Convention Center, you do not realize how big the property is until you actually find the hall where the conference is. There are these huge sprawling hallways that tend to open up and just when you thought you found the end, there is more. Walking into the hall where the exhibitors were located, there seemed to be a real sense of excitement from everyone involved, not the sense of "this is something we have to do", but more like something everyone was looking forward to. This seemed to be true for the attendees as well.
The exhibitor list ranged from all of the big companies that I expected to see, such as Oracle, Intel, O'Reilly, to quite a few of the up and comers in open source, like Akiban. One of the bigger themes seemed to be the Cloud, and it was clearly attracting a lot of interest. What I found to be most interesting was that the general intent of the attendees seemed to be education. A majority of the attendees seemed to exhibit a real interest in learning about new technologies, which for me meant the need to figure out "How do I earn their attention?". If you are reading this, chances are you already know who Infobright is and what we do, which we excel at. But to continue to grow a strong community, we have to continue to push and expand our base. This was the approach I took and it worked well.
The majority of the attendees I spoke with were unaware of Infobright and how we play a major role in the big data industry. I was able to attract many of them by discussing the core of our technology and how that can be used to solve some of the issues they face within their own businesses.
The conference itself was a hodge-podge of various technologies, which helped to keep attendees on their toes between many of the different speaker sessions that were available. They were eager to learn about new technologies, but it was a constant reminder to me that even though the big data industry is large, the general knowledge and understanding of it is mostly known to those people within the industry. There are numerous engineers and architects who were already familiar with Infobright but the vast majority were not. One of the individuals I spoke with who was already familiar with us said, "Infobright, yeah, tested it, liked it, performed great."
The weekly technical webinars Infobright puts on align with the areas of interest I saw at OSCON– helping educate people on the vast array of emerging technologies for dealing with big data. Education was why so many people came to OSCON. Don't make that a once a year proposition, continue to seek out new sources of learning.
So, for Infobright, the conference was a success and left a good impression on me for the next one. Oh, but you should definitely check out Intel's new Open Source technology Center at 01.org.
I was recently asked this question, "How do you define big data?" My response was "I say big data defines you."
With that in mind, let me expand on my definition. To do this, I would like to digress and say that big data in definition is exactly that; it is a big amount of data. (Current industry discussions add Velocity and Variety, and sometimes Value, meaning how fast it needs to be analyzed to provide value). Data is by definition information in some format or structure, so "big" must be analogous for amount. Yet my definition of big data goes a bit past this. The big data industries that have formed over the last decade focus on many different aspects of it; some deal with the infrastructure to house and manage, some deal with the software, some are in the analytic and business intelligence world, some are the consultants, and numerous companies focus on marketing, education and conferences on the subject. Within each segment of the industry, definitions may vary a little, but there are still some very key attributes that remain the same.
One, there are vast amounts of data which present unique challenges. Two, above and beyond the tasks of storing, managing and analyzing, working with this data is a primary goal (otherwise, why store it.) Three, we look for insights that we can extract from this information to enable us to make decisions that will define the way we act upon the information. For instance, a police chief in Pennsylvania knew that by analyzing criminal behavior and patterns within them, he would be able to reduce crime in a geographic area by implementing a higher police presence at specific times. The data and information that he extracted defined his response. Whether someone is researching a large number of publications and extracting relevant topics on specific articles, or they are gathering sales information for yearly trends during the rise and fall of economic windfalls, the process is still the same: we store, we search, we analyze, we define.
As a programmer myself, I have dealt with the various aspects of the infrastructure surrounding data. It is the service- and data-oriented interfaces that provide the vehicle to deliver information in a meaningful way. I have always focused on ensuring that the end user was able to get to the data that they were looking for, otherwise the data is almost useless. Data's reason for existing is solely the value of that information and what can be extracted from it. One of the most exciting aspects of this industry for me was reading about the different use cases for our analytic database and the very unique approaches that different organizations took to solving complex problems in simple elegant steps. Browse to http://www.infobright.com and read about how the Canadian Space Agency is using Infobright to store and read their machine-generated data, or any one of the JDSU or telecom-related whitepapers.
At the end of the day, we define our actions by what we learn.