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 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.
Relational Database Fundamentals and Design
Core concepts of relational databases and schema design including tables, relationships such as one to one one to many and many to many, primary keys and foreign keys, data integrity constraints, and the properties of atomicity consistency isolation and durability and why they matter. Understand differences between relational systems using structured query language and nonrelational databases, indexing strategies, normalization and denormalization trade offs, simple query optimization techniques, and when to choose a normalized relational design versus a document or key value store. Candidates should be able to perform basic entity identification, produce simple schema diagrams, explain persistence and durability considerations, and reason about basic performance and scaling trade offs.
Storage and Database Infrastructure
Storage concepts: SSDs vs. HDDs, RAID configurations, storage protocols. Database troubleshooting basics, replication concepts, backup and recovery strategies, understanding query performance and index behavior, and storage at scale.
Data Infrastructure Technology Selection
Deep understanding of specific technologies relevant to complex system design. Master databases (PostgreSQL, Cassandra, DynamoDB, Elasticsearch), message queues (Kafka, RabbitMQ), caching systems (Redis), search engines, and frameworks. Understand their strengths, weaknesses, trade-offs, operational characteristics, scaling patterns, and common pitfalls. Be able to justify technology choices based on specific system requirements.
Application Database Interaction and Performance
Understanding how applications interact with databases, connection pooling, prepared statements vs. dynamic SQL, batch processing, N+1 query problems, and coordination with development teams on performance.
Database Backup and Recovery
Focuses on database specific backup and recovery techniques and trade offs. Topics include logical and physical backup methods, full backups, incremental and differential backups, transaction log or write ahead log backups, point in time recovery, backup consistency and atomicity, transaction log management and truncation, database native and third party backup tools and commands, snapshot based backups and replication, strategies to meet Recovery Time Objective and Recovery Point Objective for database workloads, verification queries and restore testing for completeness and consistency, handling large data sets and partial restores, backup retention and archiving, encryption and secure storage of backups, and automated restore procedures and scripts for routine testing.
Data Organization and Tracking
Designing, structuring, and maintaining data models and lightweight tracking systems that support operational work such as records, cases, vendors, projects, budgets, and compliance obligations. Candidates should be able to define the right fields and metadata, unique identifiers, relationships between entities, lifecycle statuses, milestone and deadline tracking, recurrence or renewal triggers, and reporting requirements. Discussion should include choices between normalized and pragmatic schemas, tagging and taxonomy, searchability and indexing, dashboards and metrics for stakeholders, integration considerations with adjacent line-of-business systems, data governance, ownership and stewardship, access controls and privacy, retention and audit trail policies, and practical implementation approaches from spreadsheets to databases and commercial platforms.
Azure Storage and Database Options
Be able to compare Azure storage services and managed database offerings and explain when each is appropriate. Cover object storage for unstructured data, file shares for lift and shift legacy workloads, queue storage for messaging patterns, and table storage for simple NoSQL key value needs. For databases describe managed relational options such as Azure SQL Database and Azure Database for PostgreSQL or MySQL, and NoSQL options such as Cosmos DB, including differences in consistency, global distribution, latency, and operational trade offs. Discuss redundancy and durability options such as locally redundant, geo redundant, and read access geo redundant storage, and touch on performance tuning, backup and restore, lifecycle management, and security considerations that influence selection.
Database Architecture and Optimization
Designing and tuning data storage systems to meet requirements for availability, latency, throughput, and cost. Topics include choosing between managed relational services and NoSQL key value or document stores, data modelling and schema design, partitioning and sharding strategies, replication and read replica patterns, indexing and query optimization, transaction and consistency trade offs, connection pooling and resource management, caching and cache invalidation strategies, backup and retention policies, capacity planning and monitoring, and approaches for migrating or scaling databases in production. Candidates should be able to discuss concrete techniques for improving performance, diagnosing slow queries, and balancing operational complexity against performance and cost.