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).
Database Scalability and High Availability
Architectural approaches and operational practices for scaling and maintaining database availability. Topics include vertical versus horizontal scaling trade offs; replication topologies, leader and follower roles, read replicas and replica lag; read write splitting and connection pooling; sharding and partitioning strategies including range based, hash based, and consistent hashing approaches; handling hot partitions and data skew; federation and multi database federation patterns; cache layers and cache invalidation; rebalancing and resharding strategies; distributed concurrency control and transactional guarantees across shards; multi region deployment strategies, cross region failover and disaster recovery; monitoring, capacity planning, automation for failover and backups, and cost optimization at scale. Candidates should be able to pick scaling approaches based on read and write patterns and explain operational complexity and trade offs introduced by distributed data.
Data Management and Storage
Knowledge of data storage and management strategies for large scale systems. Includes choosing between relational and non relational stores, understanding consistency models and transactional guarantees, replication and partitioning strategies, indexing and query patterns, caching approaches, data retention and backup policies, and the operational trade offs between latency throughput durability and cost. Candidates should explain how data choices constrain application design and influence program decisions.
Data Partitioning and Sharding
Techniques and operational practices for horizontally partitioning data across multiple database instances or storage nodes to achieve scale, improve performance, and manage growth. Includes selection and design of partition and shard keys to evenly distribute load and avoid hotspots, with range based, hash based, and directory based approaches and consistent hashing mechanisms. Covers handling uneven distribution and data skew, hotspot detection and mitigation, and the impact of partitioning on query patterns such as joins and cross shard queries. Explains implications for transactions and consistency, including transactional boundaries that span partitions and approaches to distributed transactions and compensation. Describes resharding and online data migration strategies, rolling rebalances, and methods to minimize downtime and data movement. Emphasizes operational concerns including shard management, automation, monitoring and alerting, failure recovery, and performance tuning. Discusses trade offs between simplicity, latency, throughput, and operational complexity and highlights considerations for both transactional and analytical workloads, including routing, caching, and coordination patterns.
Database Selection and Trade Offs
How to evaluate and choose data storage systems and architectures based on workload characteristics and business constraints. Coverage includes differences between relational and nonrelational families such as document stores, key value stores, wide column stores, graph databases, time series databases, and search engines; mapping query patterns and latency requirements to storage options; trade offs between strong consistency and eventual consistency and their impact on availability and complexity; partition key design, replication strategies, and high availability considerations; operational concerns including backups, monitoring, vendor and cost trade offs, migration or hybrid strategies, and when to adopt polyglot persistence. Senior level discussion includes selecting specific managed services and reasoning about expected load patterns, failure modes, and operational burden.
Storage Systems and Data Architecture
Designing and operating storage layers and data architectures that meet durability, availability, performance, and cost requirements at enterprise scale. Covers storage technologies including block storage, file storage, and object storage; trade offs among replication, erasure coding, and snapshots; backup, recovery, retention, and archival strategies; capacity planning and tiering; caching and data locality; performance characteristics such as throughput, input output operations per second, and latency; disaster recovery and cross region replication approaches; metadata and namespace design; interactions between storage and databases or data warehouses; data lifecycle and retention policies for compliance; encryption at rest and access control for stored data; and operational concerns such as monitoring storage health, maintenance, migrations, and cost optimization.
Database Fundamentals and Storage Engines
Core principles and components of data storage and persistence systems. This includes storage engine architectures and how they affect query processing and performance; transactions and isolation including atomicity, consistency, isolation, and durability; concurrency control and isolation levels; indexing strategies and how indexes affect read and write amplification; physical versus logical storage and object, block, and file storage characteristics; caching layers and cache invalidation patterns; replication basics and how replication affects durability and read performance; backup and recovery techniques including snapshots and point in time recovery; trade offs captured by consistency, availability, and partition tolerance reasoning; compression, cost versus performance trade offs, data retention, archival, and compliance concerns. Candidates should be able to reason about durability, persistence guarantees, operational recovery, and storage choices that affect latency, throughput, and cost.
Deep Technical Expertise in Your Strongest Area
Deep dive into your most significant database project or challenge. Be prepared for very detailed follow-up questions about your technical decisions, trade-offs you considered, alternative approaches you rejected and why, performance optimizations you made, and lessons learned. Show mastery of the topic.[2][4][8]
Infrastructure and Database Systems
Fundamental infrastructure and database engineering concepts relevant to analytics platforms and general backend systems. Topics include relational and non relational database architecture indexing strategies query optimization replication and consistency trade offs sharding and partitioning approaches caching systems design message queues and event streaming systems and how these components integrate to meet performance reliability and cost objectives. Candidates should be able to reason about capacity planning high availability disaster recovery backup strategies and operational concerns such as monitoring alerting and graceful degradation under load.
Search and Indexing at Scale
Designing search systems using technologies like Elasticsearch or similar. Understanding indexing strategies, query optimization, and ranking algorithms. Designing relevance scoring and filtering mechanisms.