Streaming Analytics Guide

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Streaming analytics technologies enable action based on an analysis of a series of events that have just happened. Further, modern analytics tools provide support for large data volumes and sophisticated query processing. Instead of thousands or tens of thousands of events per second, a streaming analytics platform

can process millions and even tens of millions of events per second. Because data in a streaming analytics environment is processed before it lands in a database, the technology supports much faster decision making than possible with traditional data analytics technologies. With traditional analytics you gather information, store it and do analytics on it later. This is called “at-rest analytics.” With streaming technologies the analysis is done as the data arrive.

Common use cases for streaming analytics abound. For example, dashboards and visualization software integrated on top of streaming analytics platforms can help enterprises visualize and monitor their business in real-time. Such tools can be used to monitor changing customer attitudes through use of social media sentiment analysis. Similarly, streaming analytics capabilities can be used to enable real-time alerts or leverage new business opportunities—like making promotional offers to customers based
on their geographical location at a specific time. These capabilities are also vital in the security-monitoring context because they give organizations a way to quickly correlate seemingly disparate events to detect threat patterns and evaluate risks. Government agencies have used these capabilities to do security monitoring of both network and physical assets.

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