Micro-Batch vs True Streaming

Concepts covered: paMicroBatchVsTrue

What They Want to Hear 'Micro-batch processes events in small time windows, typically every few seconds. Spark Structured Streaming uses this model. True streaming processes each event as it arrives with no batching delay. Flink uses this model. The practical difference is latency: micro-batch has a floor around 100 milliseconds. True streaming can process in single-digit milliseconds. For most use cases, micro-batch is good enough and simpler to operate.' That is the answer. Micro-batch = small windows, 100ms floor, simpler. True streaming = per-event, sub-10ms, more complex.

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.

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.