Architect a event-driven Data Pipeline Engine
Last updated: December 4, 2025
Quick Overview
Design a event-driven data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Brex
December 4, 202531
14
394 solved
Design a event-driven data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Brex asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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 debug a model that works well offline but poorly online?
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
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Requirements
Functional Requirements
- Event Ingestion: The system must be able to ingest millions of events per second from various sources (e.g., user interactions, transactions).
- Data Processing...
Capacity Estimation
Assuming Brex processes around 1 million events per day, with each event generating an average of 10 features:
- Daily Event Count: 1,000,000 events
- Features per Event: 10 features
- **Total...
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