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 Dialects and Built-in Functions
Portability and idiom differences across SQL engines (PostgreSQL, Redshift, MySQL, SQL Server, and others), plus fluent use of built-in string, date/time, and type-conversion functions. Covers dialect-specific syntax, function availability, and writing queries that behave predictably across platforms. Relevant when candidates move between database ecosystems.
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.
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.
SQL Query Fundamentals
Core SQL for reading and shaping data: SELECT, filtering with WHERE, sorting, DISTINCT, and single-table aggregation with GROUP BY, HAVING, and aggregate functions. Covers reasoning about NULL handling, grouping semantics, and writing correct queries against a given schema. The baseline query-writing surface most data and engineering interviews open with.
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.