Many architectural diagrams show a clean pipeline: Kafka into Flink, Flink into Iceberg, Iceberg governed by Unity Catalog or queried from Snowflake. In practice this stack has real friction. Flink is not a neutral glue layer. It is a JVM-centric stream processor with non-trivial operational cost. Iceberg is not a storage engine but a table format that imposes structure. Unity Catalog and Snowflake add their own expectations around governance and schema. This article examines what actually happens when you try to connect these pieces, and where the real work sits for loosely structured data. Apache Flink: Strengths, Limits, and Operational Friction Flink is attractive because it offers low latency, stateful stream processing, event time semantics, and a mature SQL layer for streaming analytics. The problems start at the platform and DevOps level. JVM and language dependence Flink is fundamentally a Java and Scala framework. Tooling, compilation and libraries assume the JVM, Maven and ...
Hey, I'm Alex. I build backend, data and distributed systems. I founded X-Warp, Infinimesh, Infinite Devices and Scalytics, and worked with Cloudera, E.On, Google and Evariant on large-scale data, IoT and AI systems.