Merge Strategies

Concepts covered: paFullVsIncremental

What They Want to Hear 'I pick the merge strategy based on table size and access pattern. For tables under 100M rows, MERGE/UPSERT on the primary key is straightforward and correct. For larger tables, I use partition REPLACE: delete the entire partition for the date range, then insert fresh data. This avoids the row-level matching that makes MERGE slow at scale.' This is the answer that shows you have hit the performance wall and solved it.

About This Interactive Section

This section is part of the Keeping Data Fresh: 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.