Design a Data Pipeline Service

Last updated: May 13, 2026

Quick Overview

Design a multi-tenant data pipeline system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Databricks
System Design
Machine Learning Engineer
Databricks
May 13, 2026
Machine Learning Engineer
Technical Screen
System Design
Medium

438

3

3,747 solved


Design a multi-tenant 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 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
Requirements gathering and capacity estimation
Caching strategies (local, distributed, CDN)
Security and authentication
Database selection and data modeling
How to Approach This
  1. Start by clarifying functional and non-functional requirements with the interviewer.
  2. Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
  3. Draw a high-level architecture first, then deep dive into 1-2 critical components.
  4. Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
  5. 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 optimize costs as the system scales?
  • How would you handle a region-wide outage?
  • How would you handle schema migrations with zero downtime?
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Sample Answer
Requirements Clarification

Before diving into the architecture, clarify the scope with the interviewer. For Data Pipeline Service, key functional requirements include: what are ...

Capacity Estimation

Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...


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