Jay Kreps

Jay Kreps, Co-founder and CEO of Confluent and a key creator of Apache Kafka, discusses the pivotal role of real-time data in modern business. He shares insights on how companies like Expedia and eBay leverage this technology as their 'central nervous system.' Kreps also addresses the reality behind the myth of AI-driven efficiency, highlighting the long journey to building transformative businesses. The conversation includes personal anecdotes about navigating the challenges of entrepreneurship, balancing work and life, and the evolving dynamics of tech leadership.

Jay Kreps, CEO of Confluent, discusses his journey as a writer turned CEO, the challenges of building a management team, dropping out of high school, and the decision to pursue computer science. He reflects on the birth and success of Project Kafka, the power of product marketing, and the transition from individual contributor to CEO. Other topics include navigating the public markets, the future of artificial intelligence, and the evolution of open source.

Jay Kreps, co-founder and CEO of Confluent who built Apache Kafka at LinkedIn, reflects on moving from engineer to CEO and the new skillset that requires. He recounts Kafka’s rocky open-source launch and the long blog post that sparked adoption. He also explains why Confluent doubled down on a cloud-first path and the strategic choices that made that survival bet.

Jay Kreps, Co-founder of Confluent and creator of Apache Kafka, discusses the significance of real-time data, the challenges businesses face in accessing it, and the transition from batch processing to real-time continuous processing. He shares his unique childhood and unconventional career path, as well as the struggles of transitioning from a tech leader to a CEO of a publicly traded company. The chapter also covers their IPO journey, interest in sustainable energy, and a recommendation to check out Confluent.io.

Jay Kreps, CEO and Co-founder of Confluent discusses ksqlDB, a database for stream processing. Topics include querying data with ksqlDB, its architecture and scalability, handling changes and schema, query planning and optimization, stream processing anti-patterns, the future of ksqlDB, and the importance of event streaming.