Design Data Pipeline Infrastructure for real-time analytics
Last updated: May 29, 2026
Quick Overview
Design a event-driven data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Grafana Labs
May 29, 202628
4
2,711 solved
Design a event-driven data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
System design interviews at Grafana Labs 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
- Clearly define functional and non-functional requirements
- Propose a reasonable high-level architecture with core components
- Choose appropriate data storage solutions with basic justification
- Discuss basic scaling strategies (horizontal scaling, caching)
- Identify potential bottlenecks and suggest simple solutions
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?
- How would you implement rate limiting to protect the system?
- How would you migrate from a monolithic to a microservices architecture?
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Requirements Clarification
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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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