Spark Streaming vs Flink

Concepts covered: paBatchVsStreaming

What They Want to Hear 'Spark Structured Streaming is micro-batch: it collects events for a trigger interval (e.g., 10 seconds), processes them as a batch, then starts the next interval. Flink processes each event as it arrives with continuous operator pipelines. The practical differences: Spark has a 100ms latency floor and simpler state management. Flink has sub-10ms latency, more powerful windowing (session windows, event-time processing), and built-in savepoints for state migration. I choose Spark when the team already runs Spark for batch and latency requirements are relaxed. I choose Flink when latency is critical or the streaming logic requires complex stateful processing.' This is the answer that shows you can make a technology choice with concrete tradeoff reasoning.

About This Interactive Section

This section is part of the Streaming Systems: 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.