System Design for Data Engineer & Data Professionals

数据工程师系统设计面试指南

专为数据工程师与专业人士打造，掌握FAANG级系统设计。课程涵盖实时数据管道、Azure、Spark与Delta Lake，助你轻松应对高级面试与高可扩展架构设计。

Published 1/2026
Created by Certify Pro
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 16 Lectures ( 59m ) | Size: 888 MB

Ace FAANG System Design Interviews | Real-Time Data Pipelines with Azure, Spark & Delta Lake.

What you’ll learn
Understand key concepts and principles of system design tailored for data engineering.
Design scalable and efficient data pipelines and architectures.
Evaluate and choose appropriate storage and processing technologies for different data scenarios
Apply best practices for reliability, fault tolerance, and data quality in system design.

Requirements
No programmimg exp required

Description
Master the Blueprint of High-Scale Data SystemsAre you a Data Engineer who can write complex SQL and Spark jobs, but feels paralyzed when asked to design a system from scratch? In senior-level (L5+) interviews at FAANG and top-tier tech firms, the “System Design” round is where most candidates fail. They can explain a join, but they can’t architect a $150K/month production-grade pipeline that handles 1 Billion events per day.This course is specifically designed to bridge that gap. We don’t just talk about theoretical components; we build a Clickstream Analytics Mastery project that mirrors the exact challenges faced by engineers at companies like Netflix, Uber, and Meta.Why This Course is DifferentWe use a battle-tested 5-Step Design Framework that allows you to break down any complex system design prompt into an 8-minute, offer-winning presentation:1. High-Throughput Architecture: Master the flow from Event Hubs (57.8K QPS) through Spark Structured Streaming to a Delta Lake gold layer.2. Uncompromising Reliability: Learn to design for 99.99% uptime with 20s failover and 4s recovery protocols.3. Elastic Scaling: Understand the economics of scale—moving from 10 to 100 partitions and managing budgets from $4.4K to $150K/month.4. Production Observability: Implement 20+ critical production metrics using Grafana and Databricks.5. Senior-Level Trade-offs: Develop the architectural maturity to choose between Event Hubs cost-efficiency and Kafka operational overhead.What You Will BuildThroughout this case study, you will implement a Clickstream Analytics Engine capable of 12-minute End-to-End latency. You will master transaction ID deduplication in Spark, time-travel recovery in Delta Lake, and high-concurrency serving in Power BI.Target Audience• Data Engineers (2+ years) looking to break into L5/L6 Senior roles.• Architects who need to design cost-effective, massive-scale Azure/Databricks ecosystems.Stop being a “task-taker” and start being a “system-maker.” Join us and master the architecture that powers the modern web.