Hybrid Architectures

Concepts covered: paLambdaArch

What They Want to Hear 'Lambda runs batch and streaming in parallel: a batch layer for accurate historical data and a speed layer for fresh approximate data. The serving layer merges both. The problem is maintaining two separate codebases. Kappa eliminates the batch layer entirely by replaying the event log when you need to reprocess. The tradeoff is that Kappa requires a durable event log (Kafka) large enough to hold your full history.'

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.