David Rubinstein: Data Streaming Crosses the Chasm
December 15, 2007 —
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This is the time most publications wax nostalgic over the year that was, recounting the highlights and lowlights, and trying to put it all in perspective. Count us among them; SD Times will publish its traditional "Year in Review" issue Jan. 1.
So, what to write about in this, the final issue of 2007? Why, what we can expect in 2008, of course. You've got to see where you're going to know where you've been…or something like that!
Anyway, with the help of Daniel Chait, founder of software consulting company Lab49, here are some recent trends that are poised to cross the chasm, pass the tipping point and take off into the everyday world of software development.
The first area that will become much more commonplace in 2008 is data streaming, otherwise known as complex event processing. CEP, which had been relegated to highly specialized applications such as financial trading, will make its way into retail and other markets that have a need to perform calculations on data and see patterns in near real time.
The financial services industry has driven CEP, because institutional, algorithm-driven trading has led to massive increases in trading volume. "The old method of store and process for data is falling by the wayside," Chait said. "Writing processes in batch jobs at the end of the day is increasingly inadequate." Financial services companies need information on activity, trading and market information throughout the day to remain competitive.
Complex event processing came out of the world of academia, Chait noted, where the work was focused on processing data from sensors. In the area of weather prediction, sensors that can collect data on temperature, humidity and wind speed need to be read in real time to be useful.
For retail, Chait said companies can analyze traffic and consumer patterns. "Using CEP engines, they can do clicksteam analysis, to see what's going into a shopping cart, what add-ons are being chosen, when dropouts are occurring," he said. A company might use that data analysis to decide to mark down a certain item on its Web site for a certain time period of the day to drive sluggish sales, for example.
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