Data availability, usability, integrity, and security are words that we sometimes hear a lot. But what do they actually look like when put into practice? That’s where data governance comes in. This becomes especially tricky when working with real-time data architectures.

In this episode, you’ll get a taste of how Apache Kafka is used at Honeycomb! Liz Fong-Jones (Principal Developer Advocate, Honeycomb) explains how Honeycomb manages Kafka-based telemetry ingestion pipelines and scales Kafka clusters. Honeycomb is an observability platform that helps you visualize, analyze, and improve cloud application quality and performance. Their data volume has grown by a factor of 10 throughout the pandemic, while the total cost of ownership has only gone up by 20%.

With experience in data infrastructure and distributed data technologies, author of the book “Designing Event-Driven Systems” Ben Stopford (Lead Technologist, Office of the CTO, Confluent) explains the data mesh paradigm, differences between traditional data warehouses and microservices, as well as how you can get started with data mesh.

What is serverless? Having worked as a software engineer for over 15 years and as a regular contributor to Kafka Streams, Bill Bejeck (Integration Architect, Confluent) is an Apache Kafka committer and author of “Kafka Streams in Action.” In today’s episode, he explains what serverless and the architectural concepts behind it are.

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.