Timezone-Correct Cohorting at Scale

Concepts covered: sqlExtract

Date-aware incremental pipelines need a watermark: the boundary that separates 'data we have already processed' from 'data still to process.' The watermark is conceptually simple but operationally hard because the choice between event time and processing time watermarks determines how the platform handles late-arriving data. The platform decision is which time axis the watermark advances on, and how late the platform tolerates data being before it is dropped. Event-time vs processing-time watermarks A processing-time watermark advances with wall-clock time. At hour T, all events processed before hour T are in; all events processed at or after hour T are not yet. This watermark is monotonic and simple; late-arriving data is included in the next hour's batch regardless of its event time. The

About This Interactive Section

This section is part of the Date Arithmetic: 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 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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