Design TikTok Data Engineering Systems
Company: Apple
Role: Data Engineer
Category: System Design
Difficulty: medium
Interview Round: Technical Screen
You are interviewing for a data engineering role at a large short-video platform. Design and discuss the following systems:
1. **Massive video upload processing pipeline**: Design a scalable pipeline that ingests a very large volume of video upload events, processes videos asynchronously, and supports downstream analytics. Address streaming ingestion, processing orchestration, data partitioning, failure handling, and scalability. You may use technologies such as Kafka and Flink, but explain why they fit.
2. **Analytics data warehouse**: Design a warehouse schema for product analytics on videos, users, uploads, views, likes, comments, shares, and creator performance. Discuss star schema design, fact tables, dimension tables, partitioning strategy, columnar storage, and query optimization.
3. **Real-time live-stream analytics**: Design a real-time analytics system for live video streams that tracks metrics such as current viewer count, chat message volume, engagement events, and stream health. The system should support ultra-low-latency dashboards and scalable aggregation.
Quick Answer: This question evaluates data engineering competencies including scalable streaming ingestion, processing orchestration, data partitioning and failure handling, analytics warehouse modeling (star schema, fact and dimension tables, partitioning and columnar storage), and real-time aggregation for ultra-low-latency dashboards.