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.
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.
Database Administration and Operations
Day-to-day operation of database systems: configuration and tuning, patching and upgrades, automation and scripting, standards and best practices, and capacity/growth management. Covers the responsibilities of keeping databases reliable, secure, and maintainable in production. The operational-ownership surface for DBA and platform roles.
Cloud and Managed Database Services
Running databases on cloud platforms: managed relational and NoSQL offerings, cloud storage and database options (e.g. Azure, AWS), and the trade-offs of managed services versus self-managed instances. Covers provisioning, scaling, cost, and integration with the surrounding cloud ecosystem. Relevant to cloud-focused engineering and architecture roles.