The business value of data decreases rapidly after it is created, particularly in use cases such as fraud detection, cybersecurity, and real-time system monitoring. The high-volume, high-velocity datasets used to feed these use cases often contain valuable, but perishable, insights that must be acted upon immediately.

In order to maximize the value of their data and reduce their decision latency, enterprises must fundamentally change their approach to processing real-time data by focusing on the perishability of the insights that they are deriving from their real-time data streams.

Generating timely insights in a high-volume, high-velocity data environment is challenging for a multitude of reasons. As the volume of data increases, so does the amount of time required to transmit it back to the datacenter and process it. Secondly, as the velocity of the data increases, the faster the data and the insights derived from it diminish in value.

In this talk, we will present a solution based on Apache Pulsar Functions that significantly reduces decision latency by using probabilistic algorithms to perform analytic calculations on the edge.