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 Architecture and Caching
Explain design trade offs between relational and NoSQL stores and how to choose between managed relational databases and key value or document stores. Topics include data modeling, consistency and durability models, partitioning and sharding, indexing strategies, read scaling using read replicas, backup and recovery, multi availability zone failover, caching strategies with in memory stores, cache invalidation patterns, and operational considerations for scaling, monitoring, and cost management. Be prepared to discuss concrete architecture decisions and trade offs for specific workloads.
Storage Services and Data Management
Know primary storage options: Object Storage (S3, Azure Blob, GCS) - for unstructured data at scale, highly available, cost-effective. Block Storage (EBS, Azure Managed Disks) - for VM storage, IOPS/throughput optimized. Databases - Relational (RDS, Azure SQL, Cloud SQL) for structured data with relationships; NoSQL (DynamoDB, Cosmos DB, Firestore) for flexible schemas and scale. Understand access patterns, durability, and consistency models. Know when to use each storage type based on data characteristics and access patterns.
Database Design and Architecture
Designing and architecting databases for production and cloud environments with attention to data modeling, schema design, and access pattern optimization. Topics include normalization and denormalization trade offs, schema versus query driven modeling, entity and relationship design for transactional and analytical workloads, indexing and query optimization techniques, partitioning and sharding design decisions, schema evolution and migration strategies, materialized views and caching strategies, selection of storage layers for different data shapes, and practical operational runbooks for provisioning, monitoring, alerting, backups, disaster recovery, and capacity planning. Candidates should justify schema and architecture choices with respect to latency, throughput, development and operational complexity, maintainability, and cost.
Database Troubleshooting and Diagnostics
Systematic approaches and technical techniques for diagnosing database issues and restoring healthy operation. Topics include identifying symptoms, gathering diagnostic data from error logs and system views, analyzing slow queries with explain plans and profiling, diagnosing connection and authentication failures, detecting and resolving deadlocks and blocking, capacity and storage issues, replication and consistency problems, backup and restore verification, and corruption investigation. Candidates should be familiar with database specific diagnostic tools, monitoring and alerting metrics, indexing and query optimization strategies, and effective communication of findings to application and infrastructure teams.