Design a large-scale Data Pipeline Platform
Last updated: July 12, 2025
Quick Overview
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Datadog
July 12, 20255
9
939 solved
Design a low-latency data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Datadog asks this during the Onsite to assess your architectural thinking. They want to see how you decompose a complex problem, choose appropriate technologies, and reason about failure modes. Strong candidates proactively discuss monitoring, alerting, and operational concerns.
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
- How would you handle a 10x increase in traffic overnight?
- What would the deployment pipeline look like for this system?
- What happens if one of your database nodes goes down?
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Requirements
Functional Requirements
- Data Ingestion: The system should accept millions of events per second from various sources (e.g., servers, applications).
- Real-time Processing: The pipeline ...
Capacity Estimation
Assuming Datadog processes logs and metrics from approximately 500,000 hosts, with each host generating around 10 metrics per second, we can calculate:
- Total Ingest Rate: 500,000 hosts * 10 metr...
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