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.
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 Forensics and Data Recovery
Investigative and recovery work on databases: examining database and application artifacts, transaction logs, and residual data to reconstruct activity, plus recovering corrupted or deleted data. Covers evidence integrity and forensic method applied to database systems. A specialized surface for security and forensics roles.
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.
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.