Build a real-time Data Pipeline Pipeline
Last updated: February 27, 2026
Quick Overview
Design a real-time data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
MongoDB
February 27, 2026269
13
1,913 solved
Design a real-time 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 Technical Screen at MongoDB. 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. MongoDB values engineers who can think about scalability from day one.
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
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 optimize costs as the system scales?
- How would you implement rate limiting to protect the system?
- What would the deployment pipeline look like for this system?
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Requirements
Functional Requirements
- Real-time Data Ingestion: The system must support ingestion of data from multiple sources in real-time (e.g., web applications, IoT devices).
- Data Processing:...
Capacity Estimation
To estimate capacity, we need to consider the expected traffic and data volume. Assuming:
- Data Sources: 10,000 sources generating 100 events per second each.
- Total QPS: 10,000 sources * 10...
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