Railroads have used track side readers to scan bar codes on the sides of freight cars since the 1970s. Such sensors provided real time tracking of goods as they made their way from the supplier to the delivery point. Retail businesses increased the use of RFID tags in the past 20 years to track goods through the manufacturing process. Since the Indian Ocean tsunami of December 2004 the public has become aware of deep water pressure sensors which sit on the ocean floor to detect tsunamis and are intended to generate warnings about potential disasters.
The cost of sensors has decreased significantly in recent years and as a result inexpensive sensors are present nearly everywhere in businesses. As the price of sensors decreases it becomes economically feasible to deploy thousands and even millions of sensors. Such sensors cumulatively generated huge volumes of data. Imagine placing a sensor capable of measuring temperature, humidity, sun light and air pressure sensor within each square kilometer in the state of Iowa to assist farmers in managing crop production. Now imagine each of those 145,743 sensors generating 100 bytes of data every minute resulting in a data volume of nearly 21GB per day.
There is much buzz about Big Data and the challenges of applying traditional database management tools to extract business value from such data. Fortunately, there is a better way – integrating real time data, as provided by sensors, with stream analytic processing, allows timely enterprise decisions in response to changing conditions.
I urge you to read Damian Black’s recent postings on this blog describing the SQLstream approach to “Big Data”.
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