Design a large-scale Data Pipeline Platform
Last updated: April 25, 2026
Quick Overview
Design a fault-tolerant data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Cruise
April 25, 20268
13
2,705 solved
Design a fault-tolerant data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Cruise typically last 45-60 minutes. You are expected to drive the conversation, starting from requirements gathering through to a detailed architecture. The interviewer will evaluate your ability to handle ambiguity and make practical engineering decisions.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you implement rate limiting to protect the system?
- How would you optimize costs as the system scales?
- What happens if one of your database nodes goes down?
- How would you handle schema migrations with zero downtime?
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Requirements
Functional Requirements:
- Data Ingestion: Ability to ingest data from various sources (sensors, logs, etc.) in real-time.
- Data Processing: Support for ETL (Extract, Transform, Load) p...
Capacity Estimation
To estimate capacity, let’s assume:
- Cruise processes data from 100,000 vehicles, each generating 1 MB of data per minute.
- This results in a total of 100,000 MB/min or approximately 1.66 GB/s.
- Fo...
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