This past week, I embarked on a true adventure, and traveled to Lookout Mountain Flight Park in Georgia to learn how to hang glide. What possessed me to take such a bold step, you might ask? I am not a pilot, not an extreme sport enthusiast, and get queasy looking down from high places. But I do love trying new things, facing my fears, and as it so happens, I had acquired a hang gliding boyfriend in the last couple of years. So off I went, excited, nervous, and ready to meet this challenge head on. At the very least, I hoped to have some fun.
I am sure I have your attention by now, but I imagine you are wondering what hang gliding has to do with big data. Did you wander to the wrong blogging site by accident? Did you search on “aeronautics”, rather than “analytics” by mistake? No, you are in the right place. Be patient, and let me tell my story.
First, here is a picture of me standing at the top of the training hill near Lookout Mountain, poised with my glider on my shoulders. My objective is to walk, jog, run, then propel myself into the air for a flight that with luck will keep me in the air for 15 seconds or so, at a maximum height of maybe 20 feet. I am processing data (as well as a lot of emotion) – keep my wings level, maintain the glider’s nose at a particular angle, keep my eye on my target, don’t grip the control bars too tight, don’t look at the ground, make slight adjustments as I fly. It is a lot to deal with, and my mind is racing:
This is a relatively small amount of data that I am processing, and somehow I manage to pull it together and fly through the air:
I spent three days on the training hill, gaining confidence and some skills. I found myself looking at the sky every morning, checking the wind sock in the landing zone, sensing the movement of the air, and observing the clouds. I learned that birds circling overhead were not tracking prey, but lifting and soaring through thermals – the quest of all hang glider pilots.
I realized how much of hang gliding is an understanding of the weather, and how ground topography and temperature affect air currents. A pilot will spend more time assessing the conditions in which he will fly, than flying itself. Hang gliding is as much about observation, judgment, and patience:
as launching, flying, and landing:
The vast collection of data points that characterize atmospheric conditions of concern to pilots constitute “big data”. This data is sensor generated, streaming, and dynamic. The National Oceanic and Atmospheric Administration (NOAA) is one of the most cited examples of an organization that amasses huge amounts of data to aid in climate, ecosystem, and weather research. It uses “Big Data” technologies to load, store and analyze petabytes of data.
On March 23, 2011, while I was musing on big data at Lookout Mountain, Sybase’s CTO, Irfan Khan, was talking big data at the GigaOm Structure Big Data 2011 conference in New York City (http://event.gigaom.com/bigdata/). His presentation was titled “Streamlining the Information Pipeline: How to Drive Towards Zero Latency”. He discussed the increasing importance of real time data and analytics, the race to zero latency and how enterprises can implement and take advantage of next generation information platforms in these times of massive data volumes. Check out this videocast to watch the presentation:
I have just begun my journey as a fledgling pilot, and have much to learn. I look forward to the day when I can not only maneuver my glider with confidence and joy, but interpret the environmental cues that will allow me to assess the conditions for soaring and safe flight. Maybe someday, big data technology will allow me to visualize those thermals in real time from instruments mounted on my glider.