The Evolution of Apache Kafka: From In-House Infrastructure to Managed Cloud Service ft. Jay Kreps
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: Cloud-native systems abstract away operational efforts for you without infrastructure concernsIt’s important to have a complete ecosystem for Kafka, including connectors, a SQL layer, and data governanceA distributed system should allow data to be accessible everywhere and across organizationsIdentifying a reliable storage infrastructure layer that is dependable, such as Amazon S3 is criticalCost-effective models mean sustainability and systems that are easy to build aroundEPISODE LINKSBuilding Real-Time Data Systems the Hard WayKris Jenkins TwitterThe Hitchhiker’s Guide to the GalaxyHedonic treadmillWatch the video version of this podcastJoin the Confluent CommunityLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperLive demo: Intro to Event-Driven Microservices with ConfluentUse PODCAST100 to get an additional $100 of free Confluent Cloud usage (details)