← Databricks Interview Insights
Wasn't sure if they wanted a system design answer or a product walkthrough.
Start by clarifying the scope and requirements of the bookstore system, focusing on core functionalities like inventory management, search, and order processing. Then, design a scalable architecture that leverages Databricks' strengths in data processing and analytics, and finally dive into data modeling and key components.
Pro tip: Emphasize how you would use Databricks' Lakehouse architecture to unify transactional and analytical workloads, enabling real-time inventory updates and personalized recommendations. This shows you understand the company's core product and can apply it to a practical problem.
Ask questions to understand the scope: Is this an online bookstore? What are the key features (search, recommendations, inventory, orders)? What scale (users, books, transactions)? What are the non-functional requirements (latency, availability)?
Outline the main components: frontend, backend services (catalog, inventory, order, user), databases, and analytics. Sketch a simple architecture diagram and explain data flow.
Design the core data entities (Book, User, Order, Inventory) and their relationships. Discuss schema design, normalization vs. denormalization, and how to store data for both transactional and analytical needs.
Explain how to scale each component: sharding, caching, read replicas, and using Databricks for batch/stream processing. Address how to handle high traffic and large data volumes.
Describe how Databricks can be integrated: using Delta Lake for reliable data storage, Spark for ETL and analytics, ML for recommendations, and Unity Catalog for governance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.