Streaming Audio: A Confluent Podcast about Apache Kafka

How much can Apache Kafka scale horizontally, and how can you automatically balance, or rebalance data to ensure optimal performance? You may require the flexibility to scale or shrink your Kafka clusters based on demand. With experience engineering cluster elasticity and capacity management features for cloud-native Kafka, Ajit Yagaty (Confluent Cloud Control Plane Engineering) and Aashish Kohli (Confluent Cloud Product Management) join Kris Jenkins in this episode to explain how the architecture of Confluent Cloud supports elasticity.

What are useful practices for migrating a system to Apache Kafka and Confluent Cloud? Picnic, an online-only, European grocery store that processes around 45 million customer events and five million internal events daily. An underlying goal at Picnic is to try and make decisions as data-driven as possible. In this episode, Dima Kalashnikov (Technical Lead, Picnic Technologies) shares several reasons for their recent migration to Confluent Cloud for better data analytics.

Coding is inherently enjoyable and experimental. With the goal of bringing fun into programming, Kris Jenkins (Senior Developer Advocate, Confluent) hosts a new series of hands-on workshops—Coding in Motion, to teach you how to use Apache Kafka and data streaming technologies for real-life use cases.

You already know Apache Kafka is a distributed event streaming system for setting your data in motion, but how does its internal architecture work? No one can explain the internal architecture better than Jun Rao, one of the original Apache Kafka Creators and Co-Founder of Confluent. Jun has an in-depth understanding of Kafka that few others can claim—and he shares that with us in this episode, and in his new Kafka Internals course on Confluent Developer.

In this episode, Bobby Calderwood, founder of Evident Systems and creator of oNote explains event modeling—a converse approach to the reductive data model system. Event model system is enabled by tools like Apache Kafka, which effectively saves every bit of activity generated by the data system.