Processing data in real time is a process, as some might say. Angela Chu (Solution Architect, Databricks) and Caio Moreno (Senior Cloud Solution Architect, Microsoft) explain how to integrate Azure, Databricks, and Confluent to build real-time data pipelines that enable you to ingest data, perform analytics, and extract insights from data at hand.

Availability in Kafka Streams is hard, especially in the face of any changes. Apache Kafka Committer and Kafka Streams developer Sophie Blee-Goldman shares about how to solve the stop-the-world rebalance and scaling out problem in Kafka Streams using probing rebalances.

Event-driven architecture has taken on numerous meanings over the years—from event notification to event-carried state transfer, to event sourcing, and CQRS. Why has event-driven programming become so popular, and why is it such a topic of interest? Simon Aubury (Principal Data Engineer, ThoughtWorks) is here to tell all, including his own experiences adopting event-driven technologies and common blunders when working in this area.

Should enterprises be scared of Kafka? Why can it be hard to adopt Kafka? How do you drive Kafka adoption internally? All good questions, which Sam Benz (CTO) and Patrick Bönzli (Product Owner) address. They also explain how their company, SPOUD, has fully embraced Kafka for data delivery, which has proven to be successful for SPOUD since 2016 across various industries and use cases.

Synthesis Software Technologies, a Confluent partner, is migrating an existing behavioral IoT framework into Kafka to streamline and normalize vendor information. Nick Walker (Principle of Streaming) and Yoni Lew (DevOps Developer) of Synthesis discuss how they use Apache Kafka with the existing behavioral data that they collect.