Database Architect interview questions

Best practices for designing complex database models include normalization and denormalization as appropriate, establishing clear relationships with primary and foreign keys, using surrogate keys where natural keys are not reliable, incorporating data integrity constraints, and adhering to naming conventions. A database architect also considers performance, scalability, and consistency when creating the models.

Scalability and high availability can be ensured by using data sharding, partitioning, replication strategies, and by designing stateless application components. Redundancy, failover mechanisms, and distributed database systems further enhance high availability. Additionally, leveraging cloud-native features or clustering solutions is a common practice.

Performance optimization strategies include proper indexing, query rewriting, denormalization for read-heavy workloads, caching frequent queries, archiving old data, and using database-specific performance features like partitioning or materialized views. Regular monitoring and analyzing query execution plans are critical parts of ongoing tuning.

For OLTP systems, normalized data models are used to optimize storage and transactional consistency. In contrast, OLAP systems utilize denormalized or star/snowflake schemas to facilitate fast aggregations and complex analytical queries. Consideration for data volume and access patterns guides the choice of modeling approach.

Common methods include monitoring resource utilization (CPU, memory, IOPS), analyzing slow query logs, inspecting locking and contention statistics, and utilizing profiling tools. After identification, restructuring queries, adding indexes, improving schema design, or scaling hardware/software resources are used to resolve bottlenecks.

Effective architectural patterns include isolated databases per tenant, shared database with separate schemas, or shared tables with a tenant identifier. The choice depends on required data isolation, scalability, security, and cost efficiency. The architect must balance resource usage with ease of maintenance and tenant performance.

Data governance and security requirements influence the inclusion of auditing tables, access control mechanisms, encryption strategies, data masking, and compliance with regulations like GDPR. Proper modeling ensures sensitive data is stored securely, with mechanisms to trace, audit, and protect data as needed.

Techniques such as using flexible schemas (e.g., JSON fields in relational tables), implementing versioned schemas, and leveraging database migrations and backward compatibility practices are used to accommodate evolving data requirements. This enables easy updates to the data model without service disruption.

Indexing strategies are based on query patterns, workload analysis, and table update frequencies. Selective indexing on frequently queried columns boosts read performance, while minimizing unnecessary indexes reduces write overhead. Composite and partial indexes are used where appropriate, and regular index maintenance is performed.

Automation plays a critical role by continuously monitoring health metrics, alerting on performance anomalies, automating index recommendations, and performing routine maintenance tasks such as statistics updates and backups. Automated tuning solutions in modern databases can dynamically adjust resources or tune queries for optimal performance.