Distributed In Memory Processing And Streaming With Hazelcast - Episode 150
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.
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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
- 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 introduction of hardware capabilities such as NVMe drives influenced the market for in-memory systems?
- How is the governance of the open source grid and Jet projects handled?
- What is the guiding heuristic for which capabilities or features to include in the open source projects vs. the commercial offerings?
- What is involved in building an application or workflow on top of Hazelcast?
- What are the common patterns for engineers who are building on top of Hazelcast?
- What is involved in deploying and maintaining an installation of the Hazelcast grid or Jet streaming?
- What are the scaling factors for Hazelcast?
- What are the edge cases that users should be aware of?
- What are some of the most interesting, innovative, or unexpected ways that you have seen Hazelcast used?
- When is Hazelcast Grid or Jet the wrong choice?
- What is in store for the future of Hazelcast?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Apache Spark
- CAP Theorem
- Intel Optane Persistent Memory
- Hazelcast Jet
- Kappa Architecture
- IBM Cloud Paks
- Digital Integration Hub (Gartner)