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.
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, downsampling, and cardinality management. Covers storage-engine tuning such as compression, chunking, and indexing for range queries, and 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.
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.
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.
Indexing Strategy and Design
Choosing and designing indexes: B-tree, hash, composite, covering, partial, and full-text/inverted indexes, and the trade-offs between read acceleration and write/storage overhead. Covers selecting index columns from query patterns, cardinality and selectivity reasoning, diagnosing why an index is or is not used, and index maintenance: rebuilding or reorganizing a fragmented index, finding and dropping redundant or unused indexes, and rolling out a new index to production safely. Also covers indexing in analytical (bitmap, columnar), partitioned, and distributed/NoSQL systems. Central to database performance interviews.