I went straight for a star schema with a fact table for orders and dimensions for products, customers, time, and sellers.
Start by clarifying the business requirements and scope (e.g., which Amazon domain: retail, AWS, etc.) and key use cases (sales analytics, inventory management, etc.). Then propose a dimensional model (star schema) with fact and dimension tables, explaining how it supports those use cases. Finally, discuss trade-offs (e.g., normalization vs. denormalization, batch vs. real-time) and scalability considerations.
Pro tip: Demonstrate awareness of Amazon's scale and data variety by mentioning how you'd handle slowly changing dimensions (SCDs) and partitioning strategies for large fact tables. Also, tie your design to concrete business metrics like customer lifetime value or inventory turnover.
Ask questions to understand the business domain (e.g., retail, AWS), key stakeholders, and analytical needs. Identify the main use cases and metrics (e.g., sales, customer behavior, supply chain).
Decide between dimensional modeling (star schema) and normalized modeling (3NF) based on query patterns and performance needs. Justify your choice with trade-offs.
Identify fact tables (e.g., sales, orders) with measures and foreign keys. Define dimension tables (e.g., customer, product, time) with attributes and hierarchies. Consider grain of fact tables.
Discuss partitioning, indexing, and distribution keys for large datasets. Mention how to handle slowly changing dimensions and late-arriving data.
Compare your design with alternatives (e.g., Data Vault, snowflake schema) and explain why your approach fits Amazon's needs. Highlight trade-offs in storage, query performance, and maintenance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.