System Design for Data Engineers

Published 2026-02-07 in Data Engineering

System Design for Data Engineers — Core Concepts You Can’t Ignore Data Engineering isn’t just about writing SQL or Spark jobs. If you want to crack interviews and build production-ready systems, you must understand System Design fundamentals. Here’s what I revised today 👇 🧱 1. Scaling (Vertical vs Horizontal) 🔹 Vertical → Add more CPU/RAM 🔹 Horizontal → Add more machines 👉 Real-world systems scale horizontally ⚖️ 2. Load Balancers User Requests → Load Balancer → Multiple Servers ✔ Distributes traffic ✔ Improves availability ✔ Prevents server overload Essential for APIs, microservices & streaming systems. ⚡ 3. Caching Strategies (Redis / Memcached) Store frequently accessed data closer to users. ✔ Faster reads ✔ Reduced DB load ⚠ Cache invalidation is tricky. 🗄️ 4. Database Replication & Sharding 🔹 Replication → High availability 🔹 Sharding → Massive scalability Used in global-scale applications & modern data lakes. 🔄 5. Batch vs Stream Processing 📦 Batch → Hadoop / Spark (historical data) ⚡ Stream → Kafka / Flink (real-time data) Used in fraud detection, IoT analytics & log pipelines. 🏗️ 6. Lambda vs Kappa Architecture 🔹 Lambda → Batch + Speed + Serving layers 🔹 Kappa → Everything…

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