Design a large-scale Analytics Platform
Last updated: May 29, 2026
Quick Overview
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Datadog
System Design
Software Engineer
Datadog
May 29, 2026Software Engineer
System Design Round
System Design
Medium
558
6
266 solved
Design a fault-tolerant analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Datadog 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
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
Key Topics to Cover
Model selection and architecture
ML objective formulation and metric selection
Online vs offline evaluation
Training pipeline and infrastructure
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 prediction requests?
- How would you debug a model that works well offline but poorly online?
- How would you handle the cold start problem?
- How would you run A/B tests on different model versions?
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Requirements
Functional Requirements
- Data Ingestion: The system should accept and process streaming and batch data from various sources, including logs, metrics, and events.
- Analytics Dashboard: ...
Capacity Estimation
To estimate the capacity needed:
- User Base: Assume Datadog has 10,000 active users. Each user generates an average of 100 metrics per minute.
- Request Rate: This results in 1,000,000 metr...
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