Design a low-latency Data Pipeline System
Last updated: December 3, 2025
Quick Overview
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Jump Trading
System Design
Machine Learning Engineer
Jump Trading
December 3, 2025Machine Learning Engineer
System Design Round
System Design
Medium
21
6
2,653 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 Jump Trading 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
Consistency models and replication
Database selection and data modeling
Caching strategies (local, distributed, CDN)
High-level architecture and component design
Security and authentication
Message queues and async processing
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 optimize costs as the system scales?
- How would you migrate from a monolithic to a microservices architecture?
- How do you ensure data consistency across multiple services?
- How would you handle schema migrations with zero downtime?
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Requirements
Functional Requirements
- Real-time Data Ingestion: The system must handle millions of incoming data requests per second from various sources (financial market data, trades, etc.).
- **Data ...
Capacity Estimation
Back-of-Envelope Calculations
- Requests Per Second (QPS): Assume an average of 2 bytes per request. With a target of 10 million requests per second, we expect:
- Data Ingestion: 10M r...
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