Summary
Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble.
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Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant
Interview
Introduction
How did you get involved in the area of data management?
Can you describe your experiences with Kafka?
What are the operational challenges that you have had to overcome while working with Kafka?
What motivated to write a book about how to manage Kafka in production?
There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice?
In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster?
When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing?
What are the axes along which size/scale need to be determined?
The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss?
Under what circumstances can data be lost?