Design a Data Pipeline for Lyft
Last updated: December 23, 2025
Quick Overview
Design a distributed data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Lyft
System Design
Software Engineer
Lyft
December 23, 2025Software Engineer
Technical Screen
System Design
Medium
21
11
3,410 solved
Design a distributed data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Lyft goes beyond model selection. This Technical Screen question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
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
A/B testing and experimentation
Online vs offline evaluation
Model serving and latency optimization
Training pipeline and infrastructure
Feedback loops and model retraining
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 the cold start problem?
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
- What would you do if model performance degrades over time?
Practice a Similar Problem on Codemia
Solve a related problem with our interactive workspace, get AI feedback, and view detailed solutions.
Solve on CodemiaSample Answer
Requirements
Functional Requirements
- Data Ingestion: The pipeline should support ingestion of data from various sources such as user interactions, ride requests, and payment transactions in real-time. 2...
Capacity Estimation
Back-of-Envelope Calculations
- Daily Ride Requests: Assume Lyft handles around 1 million rides daily.
- Events per Ride: Each ride generates approximately 20 events (e.g., request, ride s...
Submit Your Answer
Markdown supported