Design a large-scale Data Pipeline Platform
Last updated: March 1, 2026
Quick Overview
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Scale AI
March 1, 202637
3
752 solved
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Scale AI 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
- Systematically gather requirements and estimate capacity (QPS, storage, bandwidth)
- Design a scalable architecture with clear component responsibilities
- Make well-reasoned database and caching decisions with trade-off analysis
- Address consistency vs availability trade-offs specific to the use case
- Discuss partitioning strategy, replication, and data modeling
- Cover failure handling, monitoring, and alerting strategies
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 handle a 10x increase in traffic overnight?
- What monitoring and alerting would you set up on day one?
- How do you ensure data consistency across multiple services?
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Requirements
Functional Requirements
- Low-latency Data Ingestion: The system must ingest millions of data points per second from various sources, including APIs, databases, and streaming platforms.
- **...
Capacity Estimation
Back-of-Envelope Calculations
Assuming we need to handle 1 million requests per second (QPS) with an average payload size of 1KB:
- Total bandwidth: 1 million requests/sec * 1 KB/request = 1 T...
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