Watermarks for Irregular Streams

Concepts covered: paWatermarkStrategies

The intermediate tier introduced bounded out-of-orderness as the default watermark strategy. In production, very few real streams behave the way that strategy assumes. Streams from mobile devices have multi-modal lateness distributions. Streams from IoT sensors have idle periods longer than any reasonable timeout. Streams from financial systems carry markers that explicitly declare segment boundaries. Picking a watermark strategy is a per-source design decision, not a default. The Strategy Catalog Each strategy makes a different bet about what the stream looks like. Picking the wrong strategy means either dropping legitimate events or stalling the pipeline. The choice should follow from a measured profile of the source: lateness percentiles, idle-period distribution, presence or absence of

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This section is part of the Schema Evolution and Late Data: Advanced lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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