ApacheFlink

Series Posts

Conceptual Every Record Counts: Data Engineering for Backend Engineers

Part 26: "Time Is a Lie" — Streaming Fundamentals

Season 3 opens with a delivery van that warmed up where nobody could hear it. Rohan's consumer groups readings by arrival and misses the excursion. Event time, windows, watermarks, lateness, triggers, state, exactly-once and the Dataflow model, simulated in Python and previewed in Flink SQL.

Project Every Record Counts: Data Engineering for Backend Engineers

Part 28: "Streaming SQL" — Apache Flink I

Rohan writes Flink SQL like a Postgres report: the live sales tile loses 84 late baskets and 2,338 lines carry prices no till charged. Flink's moving parts, dynamic tables and changelogs, windows, temporal joins, dedup, Top-N, and sinks that append or upsert into Kafka and Iceberg, reconciled store by store against the lakehouse.

Project Every Record Counts: Data Engineering for Backend Engineers

Part 29: "Stateful Streams in Java" — Apache Flink II

Rohan writes the stock-out detector like a Spring service, and a restart pages stores twice. Keyed state, event-time timers, side outputs, checkpoints and savepoints, exactly-once sinks, upgrades and backpressure in Flink's Java DataStream API, proven by killing a TaskManager mid-run.