Architect a scalable Analytics Engine
Last updated: September 26, 2025
Quick Overview
Design a scalable analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
HRT
September 26, 202593
7
3,632 solved
Design a scalable analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at HRT goes beyond model selection. This System Design Round question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
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
- How would you handle the cold start problem?
- How would you run A/B tests on different model versions?
- How would you debug a model that works well offline but poorly online?
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Requirements
- Functional Requirements:
- Collect real-time event data (e.g., user interactions, transaction details) from various sources.
- Process and analyze data to generate analytics reports and insi...
Capacity Estimation
Assuming HRT expects to handle 10 million user interactions per day:
-
Requests per Second (RPS):
- 10 million requests/day / 86400 seconds/day ≈ 115.7 RPS.
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Data Volume:
- If each inter...
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