Database Engineering & Data Systems Topics
Database design patterns, optimization, scaling strategies, storage technologies, data warehousing, and operational database management. Covers database selection criteria, query optimization, replication strategies, distributed databases, backup and recovery, and performance tuning at database layer. Distinct from Systems Architecture (which addresses service-level distribution) and Data Science (which addresses analytical approaches).
Data Modeling and Schema Design
Designing relational schemas end to end: entity-relationship modeling, normal forms and deliberate denormalization, primary/foreign keys, data types, and integrity constraints, together with applied schema design driven by real business requirements and query access patterns. Covers modeling a domain from ambiguous requirements, choosing structures that serve the queries a system must run, trading normalization for correctness against denormalization for read performance, and evolving schemas as needs change. Foundational data-modeling judgment for building and reviewing databases, tested through open-ended domain-modeling prompts.
Data Warehousing and Dimensional Modeling
Analytical data platforms: star and snowflake schemas, fact and dimension tables, slowly changing dimensions, and cloud data warehouse design and optimization (Redshift, BigQuery, Snowflake-style systems). Covers modeling for analytics versus transactions and tuning warehouse query performance. Central to data-engineering and BI interviews.