Stream processing has idled on the backwaters of the analytic market for years. But with the advent of Hadoop and new open source streaming tools, such as Storm, Spark, and Kafka, many companies are taking a closer look. And many stream processing tools are finally finding a home with the Internet of Things, in which consumer and commercial devices--from smartphones and household appliances to automobiles, utility meters, and medical equipment--emit millions of events per second and require specialized analytical systems to process them in real time.
Stream processing platforms, like SQLstream, provide both the horsepower and smarts to filter, aggregate, group, compare, and analyze large volumes of data in flight as well as visualize the results in real time. Telecommunications companies use SQLstream to monitor network performance, track service usage, and detect fraud in real time; oil and gas producers use it to monitor operations of drilling rigs, digital wells, and intelligent oil fields; and transportation companies use it to monitor traffic congestion, among many other things.
Compared to Storm and Spark, SQLstream is a complete enterprise platform for streaming analytics that can be deployed quickly without a large development effort. Whereas the open source projects are free to download, they require a lot of development talent and time to make work, especially in high-volume environments. Moreover, SQLstream, which gets its name because it uses continuous SQL to generate analytics, runs more efficiently, requiring many fewer servers and less overall expenditures.
As devices become more intelligent with the addition of sensors, product companies will need to invest in stream processing systems to make sense of the deluge of data. SQLstreams is well positioned to capitalize on the emerging Internet of Things.
For more information, see www.sqlstream.com
Posted September 16, 2014 11:22 AM
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