Late-Arriving Data

Concepts covered: paLateData

What They Want to Hear 'Late data arrives after the window it belongs to has already been processed. A click that happened at 11:58 PM might arrive at 12:03 AM, after the hourly window closed. I handle this with watermarks: a threshold that says how late I am willing to wait. If my watermark is 10 minutes, I keep the window open for 10 extra minutes to accept late events. Events that arrive after the watermark are either dropped or sent to a dead letter queue for reprocessing.' That is the answer. Late data is inevitable. Watermarks define how long you wait. After that, dead letter queue.

About This Interactive Section

This section is part of the Streaming Systems: Beginner 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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DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.