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).
SQL Joins and Set Operations
Combining data across multiple tables using inner, outer, cross, and self joins, plus set operations (UNION, INTERSECT, EXCEPT). Covers join-key selection, fan-out and row-multiplication pitfalls, merge strategies, and integrating data from disparate sources. A high-frequency interview surface for anyone who queries relational data.
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.
Consistency Models and Distributed Databases
Data correctness across distributed systems: strong versus eventual consistency, the CAP and PACELC trade-offs, consensus and quorum reads/writes, and consistency-versus-availability decisions. Covers how distributed databases reconcile replicas and what guarantees applications can rely on. A staple of distributed-systems and architecture interviews.
Query Optimization and Execution Plans
Making queries fast: reading and interpreting execution/explain plans, identifying full scans, spotting SQL anti-patterns, and rewriting queries for better performance. Covers how the planner chooses join order and access methods, and how statistics drive those choices. A core skill for anyone responsible for query performance in production.
Database Selection and Trade-offs
Choosing the right database and data platform for a workload: relational versus NoSQL versus specialized stores, managed versus self-hosted, and matching technology to consistency, scale, cost, and query or access-pattern needs. Covers OLTP versus OLAP and transactional-versus-analytical workload splits, polyglot persistence across multiple data stores, structuring an ambiguous selection prompt, naming trade-offs, and defending a recommendation to stakeholders.
NoSQL and Polyglot Persistence
Non-relational data stores and mixing them: key-value, document, wide-column, and graph databases, their data models and access patterns, and polyglot persistence where different stores serve different needs. Covers when NoSQL beats relational and how to integrate multiple stores coherently. Increasingly common in system-design and data-platform interviews.