Event stream processing: Think "rapid"


What sends a data management product to the top of the “hot” list? In a word – speed. Especially when that speed can gracefully accommodate the huge world of streaming data from the Internet of Things.

One of SAS’ hottest (and recently enhanced) products, SAS Event Stream Processing is an in-memory technology designed for speed. Its combination of high throughput (millions of events per second) and low latency beats out every other SAS product you can name. What’s more, this version of the software adds SAS Event Stream Processing Studio and Streamviewer , making it easier than ever to design, test and improve your projects.

Let’s use the word R-A-P-I-D to spell out how SAS Event Stream Processing delivers faster-than-ever insights to what’s happening right now.


Streaming data often has a half-life beyond which it is of little use. Acknowledging that reality, SAS Event Stream Processing helps businesses react to their customers’ behavior in real time. Consider: Telco companies that make personalized offers to extend a data plan before the user runs out of data. Retailers that use iBeacons in combination with historical shopper data to create a timely, personalized shopping experience. And banks that react to fraudulent transactions before the money leaves.


Models in SAS Event Stream Processing are known as continuous queries. These are essentially algorithms designed by a user to process streaming data in real time. As data is ingested, it makes its way through a series of windows that are customized to implement the desired logic. Within a continuous query, inside a procedural window, complex algorithms derived from analytical models can be used to score events in real time. This real-time scoring for a breadth of advanced analytical models is what enables SAS Event Stream Processing to process streaming data intelligently, using algorithms – a process often called streaming analytics.


One of the hallmark features of SAS Event Stream Processing is its ability to do complex event processing, or CEP, which entails detecting complex event patterns in real time. Whether you’re designing simple or complex patterns of events, this technology’s robust framework will help you identify and react to patterns quickly. It includes prebuilt routines for data normalization, data cleansing and streaming text data analytics.


Many products don’t achieve their full potential if they’re only used standalone. To extend the value of SAS Event Stream Processing, you can easily integrate it with other technologies – like SAS Asset Performance Analytics and SAS Real-Time Decision Manager. Because it prepares and cleanses data in real time, SAS Event Stream Processing allows everything downstream of it to use resources efficiently and operate more effectively. And, by integrating with these technologies, analysts can identify patterns of interest, then embed them into event stream continuous queries.

White paper cover for Understanding Data Streamsin IoT
Learn how event stream processing helps you make sense of the Internet of Things.


Not all bits of data are equal. So somewhere, decisions have to be made about what’s most relevant. When it processes streaming data – before ever landing it to disk – SAS Event Stream Processing distinguishes between data that’s critical for immediate action, data that should be stored and analyzed later, and data that is of little to no value. It’s a great way to boost the value of big data technologies. Because we all know data lakes will eventually overflow. That makes it increasingly important to know what to store in the first place.

Streaming data analytics for the IoT requires a major shift from traditional batch processing. That’s why it’s more important than ever to think RAPID, and harness the power of incremental results in real time. And that’s precisely what SAS Event Stream Processing helps you do.


About Author

Evan Guarnaccia

Solutions Architect

Evan Guarnaccia is a Solutions Architect for Americas Technology Practice focusing on SAS Real Time Solutions. He helps customers realize their business goals and derive value from Analytics in Real Time. Evan holds a PhD in High Energy Experimental Particle Physics from Virginia Tech.

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