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).
Data Modeling and Storage Strategy
Covers the end to end considerations for modeling, storing, and operating application data at scale. Topics include choosing between relational, document, key value, wide column, and graph stores based on access patterns; trade offs between normalization and denormalization; indexing and query optimization; partitioning and sharding strategies (range, hash, consistent hashing); replication topologies and failover patterns; consistency models and transaction semantics including strong consistency, eventual consistency, isolation levels, and distributed transactions; caching strategies and cache invalidation; event sourcing and command query responsibility segregation for auditability and scale; analytics versus operational storage patterns, data warehousing and lake options, and change data capture for near real time pipelines. Also includes operational concerns such as backup and restore, retention and archival policies, geo distribution and latency considerations, cost and capacity planning, security and compliance concerns for sensitive data, and migration strategies between storage technologies.
Database Design and Query Optimization
Principles of database schema design and performance optimization including relational and non relational trade offs, normalization and denormalization, indexing strategies and index types, clustered and non clustered indexes, query execution plans, common table expressions for readable complex queries, detecting missing or redundant indexes, sharding and partitioning strategies, and consistency and availability trade offs. Candidates should demonstrate knowledge of optimizing reads and writes, diagnosing slow queries, and selecting the appropriate database model for scale and consistency requirements.
Database Architecture and Partitioning
Design database architecture and partitioning strategies appropriate to workload and access patterns. Evaluate database types including relational and various NoSQL models, schema design and indexing strategies, and when to use a monolithic database versus sharding. Cover sharding approaches such as range based, hash based, consistent hashing, and directory based sharding, as well as replica topologies, read replicas, replication lag, and handling cross shard queries. Address operational concerns at scale: resharding, mitigating hot partitions, balancing data distribution, transactional and consistency guarantees, and the trade offs between availability, consistency, and partition tolerance. Include monitoring, migration strategies, and impact on application logic and joins.