The number of deployments of Apache Kafka at enterprise scale has greatly increased in the years since Kafka’s original development in 2010. Along with this rapid growth has come a wide variety of use cases and deployment strategies that transcend what Kafka’s creators imagined when they originally developed the technology. As the scope and reach of streaming data platforms based on Apache Kafka has grown, the need to understand monitoring and troubleshooting strategies has as well.
Dustin Cote and Ryan Pridgeon share their experience supporting Apache Kafka at enterprise-scale and explore monitoring and troubleshooting techniques to help you avoid pitfalls when scaling large-scale Kafka deployments.
Ryan Pridgeon is a customer operations engineer at Confluent. Ryan has a deep-rooted passion for tinkering that knows no bounds. Be it automotive, software, or carpentry, if it has pieces, Ryan wants to take it apart. He’s still working on putting things back together though.
Dustin Cote is a customer operations engineer at Confluent. Over his career, Dustin has worked in a variety of roles from Java developer to operations engineer. His most recent focus is distributed systems in the big data ecosystem, with Apache Kafka being his software of choice.
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