Design a large-scale Data Pipeline Platform
Last updated: October 29, 2025
Quick Overview
Design a scalable data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Booking.com
October 29, 2025108
11
4,044 solved
Design a scalable data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during Onsite at Booking.com. The interviewer expects you to demonstrate your ability to design large-scale distributed systems, make well-reasoned trade-offs, and communicate your thought process clearly. Booking.com values engineers who can think about scalability from day one.
What the Interviewer Expects
- Drive the design discussion proactively with minimal interviewer guidance
- Perform detailed capacity estimation and use it to inform design decisions
- Design for global scale with multi-region deployment and data consistency
- Deep dive into 2-3 critical components with implementation-level detail
- Address complex trade-offs: CAP theorem, eventual consistency, conflict resolution
- Discuss operational excellence: deployment strategy, chaos engineering, SLOs/SLIs
- Propose a phased rollout plan from MVP to full-scale system
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 the deployment pipeline look like for this system?
- How do you ensure data consistency across multiple services?
- What happens if one of your database nodes goes down?
- How would you handle a 10x increase in traffic overnight?
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Requirements
Functional Requirements
- Data Ingestion: The system should support ingestion of event data from various sources like user interactions, bookings, and system logs.
- Real-time Processing...
Capacity Estimation
For a rough capacity estimate:
- Events per Day: Assume 10 million events/day as a baseline.
- Events per Second: This translates to approximately 115.74 events/second (10 million / 86400 seco...
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