Handling Late-Arriving Data

Concepts covered: dmLateArriving

When Events Arrive After the Window Closes In the real world, events do not arrive in order. A mobile app queues clicks while offline and sends them hours later. A payment gateway batches settlements daily. A sensor loses connectivity and dumps a backlog. If your pipeline processes events by wall-clock time (when the pipeline sees them), all of these produce wrong results. The mobile clicks land in the wrong hour. The settlements land on the wrong day. The fix: process by event time (when the event happened), not processing time (when the pipeline sees it). But this creates a new problem: how do you know when all events for a given hour have arrived? The answer is: you do not. You use a watermark, a heuristic that says 'I believe all events before time T have arrived.' Events arriving afte

About This Interactive Section

This section is part of the Event Streams 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.