Batch Mechanics

Concepts covered: paFullVsIncremental

What They Want to Hear 'I use a high-water mark pattern. The pipeline records the maximum timestamp from the last successful run. On the next run, it only reads rows with a timestamp after that mark. This means we process 50,000 changed rows instead of re-reading 500 million.' That is the core answer. Then add depth: 'I run incremental daily and a full reload weekly as a safety net to catch anything the incremental logic missed.'

About This Interactive Section

This section is part of the How Data Moves: Intermediate 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.

How DataDriven Lessons Work

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.