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).
Replication, Partitioning, and Sharding
Scaling and distributing data across nodes: primary-replica and multi-primary replication, read-replica scaling, horizontal partitioning, and sharding strategies with their key-selection and rebalancing challenges. Covers replication lag, routing, and the operational cost of a partitioned topology. Key to designing databases that scale horizontally.
Storage Systems and Infrastructure
The physical and infrastructure layer beneath databases: disk and volume management, storage redundancy and RAID levels, storage services, and how storage architecture affects durability and performance. Covers matching storage configuration to reliability and throughput requirements. Serves infrastructure and systems roles that own the hardware substrate.
Database Performance Tuning and Scaling
System-level performance work beyond a single query: configuration and resource tuning, capacity planning, handling large data volumes, and scaling read and write throughput. Covers identifying bottlenecks, growth management, and the vertical-versus-horizontal scaling decision. Tests whether a candidate can keep a database healthy as load grows.
Geospatial and Time-Series Data
Specialized data shapes and their stores: geospatial data types, spatial indexing, and location queries; and time-series data with high-ingest, retention, and downsampling patterns. Covers when purpose-built extensions or databases beat general-purpose stores for these workloads. A niche but recurring topic for location- and telemetry-heavy systems.
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.
Database Monitoring, Troubleshooting, and Diagnostics
Observing and fixing databases in production: health checks, metrics and alerting, and diagnosing common failures like slow queries, lock contention, replication lag, and resource exhaustion. Covers a systematic troubleshooting method under incident pressure. Tests operational instincts distinct from design knowledge.
Caching and Connection Management
The layer between application and database: cache strategies (cache-aside, write-through, invalidation) for query results, connection pooling and lifecycle, and efficient application-to-database interaction. Covers reducing database load, avoiding pool exhaustion, and the consistency trade-offs caching introduces. Bridges application performance and database health.
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.