Distributed In Memory Processing And Streaming With Hazelcast Data Engineering Podcast

Summary
In memory computing provides significant performance benefits, but brings along challenges for managing failures and scaling up. Hazelcast is a platform for managing stateful in-memory storage and computation across a distributed cluster of commodity hardware. On top of this foundation, the Hazelcast team has also built a streaming platform for reliable high throughput data transmission. In this episode Dale Kim shares how Hazelcast is implemented, the use cases that it enables, and how it complements on-disk data management systems.

Announcements

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Your host is Tobias Macey and today I’m interviewing Dale Kim about Hazelcast, a distributed in-memory computing platform for data intensive applications

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing what Hazelcast is and its origins?
What are the benefits and tradeoffs of in-memory computation for data-intensive workloads?
What are some of the common use cases for the Hazelcast in memory grid?
How is Hazelcast implemented?

How has the architecture evolved since it was first created?


How is the Jet streaming framework architected?

What was the motivation for building it?
How do the capabilities of Jet compare to systems such as Flink or Spark Streaming?


How has the introduc