Giannis Polyzos

Over the last decade, streaming architectures have largely been built around topic-centric primitives—logs, streams, and event pipelines—then stitched together with databases, caches, OLAP engines, and (increasingly) new serving systems.

"For the last decade, Apache Kafka has been at the core of streaming data pipelines. After years in the trenches, we've learned some hard lessons and taken a fresh look at what true streaming analytics really needs.

Autoscaling is the cornerstone of cost optimization, and a prerequisite for improved availability and uptime in distributed systems. In this respect, managing the Apache Flink workload in a cost efficient manner requires algorithms that enable horizontal scaling in the various use cases of an organization.

Introduction Apache Flink, Apache Flink CDC, and Apache Paimon are the three musketeers of the Streaming Lakehouse. In this session, we will see the history of Apache Paimon, why it was created, and what problems it solves.