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 Model Design and Access Patterns
Discuss how you'd design data models based on access patterns. Understand relational vs. NoSQL trade-offs. Know when to denormalize, how to handle distributed transactions, and strategies for scaling databases (sharding, partitioning). Discuss read vs. write optimization.
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.
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.
Consistency Models and Transactions
Comprehensive knowledge of data consistency models and transactional guarantees in databases and distributed systems. This includes understanding transaction properties such as Atomicity, Consistency, Isolation, and Durability (ACID) and alternative design philosophies such as Basically Available, Soft state, Eventually consistent (BASE). Candidates should be able to choose appropriate isolation levels including read uncommitted, read committed, repeatable read, serializable, and snapshot isolation and explain performance versus correctness tradeoffs and common anomalies such as dirty reads, non repeatable reads, phantom reads, lost updates, and write skew. Understand consistency models including strong consistency, strict serializability, serializability, snapshot isolation, causal consistency, eventual consistency, monotonic reads, and read your writes, and when each model is acceptable based on latency, availability, and business correctness requirements. Discuss replication strategies and their impact on guarantees, including synchronous versus asynchronous replication, multi region replication, replication lag, and replica divergence. Evaluate distributed transaction and coordination approaches such as two phase commit and consensus based protocols and weigh their performance and failure modes. Propose conflict detection and resolution strategies such as last write wins, version vectors and vector clocks, conflict free replicated data types, application level reconciliation, idempotent operations, retries, and saga or compensation patterns for long running workflows. Consider practical engineering concerns including consistency service level objectives, monitoring and alerting for staleness and replication lag, testing strategies for consistency, implications for caching and sharding, and the tradeoffs between developer complexity and user facing correctness.
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.