Architect a fault-tolerant Analytics Engine
Last updated: October 26, 2025
Quick Overview
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Databricks
October 26, 202585
6
4,525 solved
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This is a common system design question asked during System Design Round at Databricks. 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. Databricks 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
- What happens if one of your database nodes goes down?
- How would you handle schema migrations with zero downtime?
- How would you optimize costs as the system scales?
- How do you ensure data consistency across multiple services?
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Requirements
Functional Requirements:
- High Throughput: The analytics engine must handle millions of requests per second (QPS) for real-time data analysis.
- Fault Tolerance: The system should remai...
Capacity Estimation
To estimate capacity, we need to consider peak load scenarios:
- Users: Assume 10,000 concurrent users.
- Requests per User: Each user makes an average of 10 requests per second.
- **Total ...
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