In this latest podcast from The New Stack, we interview Manish Devgan, chief product officer for Hazelcast, which offers a real time stream processing engine. This interview was recorded at KubeCon+CloudNativeCon, held last October in Detroit.

 

"'Real time' means different things to different people, but it's really a business term," Devgan explained. In the business world, time is money, and the more quickly you can make a decision, using the right data, the more quickly one can take action.

 

Although we have many "batch-processing" systems, the data itself rarely comes in batches, Devgan said. "A lot of times I hear from customers that are using a batch system, because those are the things which are available at that time.

 

But data is created in real time sensors, your machines, espionage data, or even customer data — right when customers are transacting with you."

 

What is a Real Time Data Processing Engine?

 

A real time data processing engine can analyze data as it is coming in from the source. This is different from traditional approaches that store the data first, then analyze it later. Bank loans may is example of this approach.

 

With a real time data processing engine in place, a bank can offer a loan to a customer using an automated teller machine (ATM) in real time, Devgan suggested.  "As the data comes in, you can actually take action based on context of the data," he argued.

 

Such a loan app may combine real-time data from the customer alongside historical data stored in a traditional database. Hazelcast can combine historical data with real time data to make workloads like this possible.

 

In this interview, we also debated the merits of Kafka, the benefits of using a managed service rather than running an application in house, Hazelcast's users, and features in the latest release of the Hazelcast platform.

 

 

 

 

 

 

 

 

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