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.
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.
Indexing Strategy and Design
Choosing and designing indexes: B-tree, hash, composite, covering, and partial indexes, and the trade-offs between read acceleration and write/storage overhead. Covers selecting index columns from query patterns, cardinality and selectivity reasoning, and diagnosing why an index is or is not used. Central to database performance interviews.
Backup and Disaster Recovery
Keeping data durable and recoverable when systems fail: backup design (full, incremental, differential, and snapshot strategies; point-in-time recovery), backup verification and restore testing, retention and archival policy (including compliance retention and legal holds), encryption and key management for backups, and disaster-recovery planning measured against recovery-time and recovery-point objectives (RTO/RPO). Tests whether a candidate can design a backup strategy that actually restores, choose the right retention tier for a business's downtime and data-loss tolerance, and operate backup systems safely under failure, compliance, and ransomware threats. Distinct from code-level fault-tolerance patterns (circuit breakers, retries, bulkheads) and multi-region failover architecture, which belong to high-availability-and-disaster-recovery.