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, failover and split-brain handling, cross-shard operations such as joins, distributed transactions, and global secondary indexes, and the operational cost of a partitioned topology. Key to designing databases that scale horizontally.
Storage Systems and Infrastructure
The physical storage substrate that sits beneath databases and other data-intensive systems: disk and volume management, RAID levels and disk-redundancy trade-offs (capacity vs fault tolerance vs rebuild time), and cloud block and instance-store performance characteristics (IOPS, throughput, latency), including how ephemeral instance-store volumes differ from persistent attached ones. Also covers storage-service tiering: hot, warm, cold, and archival lifecycle policies, the retrieval-cost-versus-latency trade-off, and designing the automation that migrates data between tiers and routes reads across them while meeting latency and cost targets. Covers matching a storage configuration's redundancy and performance profile to a system's durability and throughput requirements. This is distinct from how a database engine implements storage internally (write-ahead logs, page layouts, B-tree versus LSM structures). Aimed at engineers who configure and operate the underlying storage hardware.
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, the vertical-versus-horizontal scaling decision, materialized views for expensive queries, and maintenance work such as vacuuming, index rebuilds, bulk loads, and safe schema changes on large tables. Tests whether a candidate can keep a database healthy as load grows.
Cloud and Managed Database Services
Running databases as managed cloud services rather than self-hosting them: choosing and provisioning managed relational and NoSQL offerings (for example Amazon RDS/Aurora/DynamoDB, Azure SQL Database/Cosmos DB, Google Cloud SQL/Spanner), sizing instances and storage, and designing an architecture (read replicas, connection proxies and pooling for managed or serverless compute) to meet a latency or availability target. It covers comparing provisioned versus serverless/autoscaling pricing and operational models, forecasting capacity and cost, and migrating a database onto or between managed deployments: moving a self-hosted database to a managed service, switching a workload from provisioned to serverless (or back), changing a single-AZ deployment to multi-AZ, and the questions to ask before adopting a new managed vendor. The core question is the split of responsibility and cost between the cloud provider and the engineering team: what the provider takes on (patching, infrastructure-level HA, automated backups) versus what remains the team's job (configuration, capacity and cost tuning, security posture like encryption and key rotation). It does not cover backup and restore mechanics, RPO/RTO planning, replication or sharding internals, or SQL-level query diagnostics.
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.
Database Selection and Trade-offs
Choosing the right database and data platform for a workload: relational versus NoSQL versus specialized stores, managed versus self-hosted, and matching technology to consistency, scale, cost, and query or access-pattern needs. Covers OLTP versus OLAP and transactional-versus-analytical workload splits, polyglot persistence across multiple data stores, structuring an ambiguous selection prompt, naming trade-offs, and defending a recommendation to stakeholders.