Architect a low-latency Data Pipeline Engine
Last updated: January 6, 2026
Quick Overview
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Discord
January 6, 2026286
6
1,009 solved
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Discord's System Design Round tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
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
- What would you do if model performance degrades over time?
- How would you debug a model that works well offline but poorly online?
- How would you handle the cold start problem?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For low-latency Data Pipeline Engine, key functional requirements include...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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