When it comes to Apache Kafka®, there’s no one better to tell the story than Jay Kreps (Co-Founder and CEO, Confluent), one of the original creators of Kafka. In this episode, he talks about the evolution of Kafka from in-house infrastructure to a managed cloud service and discusses what’s next for infrastructure engineers who used to self-manage the workload.
Kafka started out at LinkedIn as a distributed stream processing framework and was core to their central data pipeline. At the time, the challenge was to address scalability for real-time data feeds. The social media platform’s initial data system was built on Apache™Hadoop®, but the team later realized that operationalizing and scaling the system required a considerable amount of work.
When they started re-engineering the infrastructure, Jay observed a big gap in data streaming—on one end, data was being looked at constantly for analytics, while on the other end, data was being looked at once a day—missing real-time data interconnection. This ushered in efforts to build a distributed system that connects applications, data systems, and organizations for real-time data. That goal led to the birth of Kafka and eventually a company around it—Confluent.
Over time, Confluent progressed from focussing solely on Kafka as a software product to a more holistic view—Kafka as a complete central nervous system for data, integrating connectors and stream processing with a fully-managed cloud service.
Now as organizations make a similar shift from in-house infrastructure to fully-managed services, Jay outlines five guiding points to keep in mind:
If there's something you want to know about Apache Kafka, Confluent or event streaming, please send us an email with your question and we'll hope to answer it on the next episode of Ask Confluent.Email Us