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 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 Patching and Upgrades
Covers the end to end planning, testing, deployment, validation, and recovery activities required to apply vendor patches and perform database engine upgrades with minimal risk and downtime. Topics include inventory and prioritization of instances and security fixes, staging and testing in non production environments, taking and validating backups, schema and engine compatibility checks, analysis of client driver and application dependencies, and assessment of breaking changes and deprecation notices. Candidates should be able to design safe upgrade paths such as rolling upgrades, staged rollouts, and replica promotion strategies to minimize service interruption, as well as define rollback and restore procedures and runbooks. The scope also includes coordinating changes with application deployments and stakeholders, scheduling and communication, post patch monitoring and validation of data integrity and query performance, understanding vendor version support lifecycles, and the use of automation and orchestration tools and vendor specific practices for both relational and non relational databases. Finally, candidates should explain how they would recover from failed patch or upgrade attempts and how they would measure and mitigate operational risk throughout the lifecycle.
Database Fundamentals and Storage Engines
Core principles and components of data storage and persistence systems. This includes storage engine architectures and how they affect query processing and performance; transactions and isolation including atomicity, consistency, isolation, and durability; concurrency control and isolation levels; indexing strategies and how indexes affect read and write amplification; physical versus logical storage and object, block, and file storage characteristics; caching layers and cache invalidation patterns; replication basics and how replication affects durability and read performance; backup and recovery techniques including snapshots and point in time recovery; trade offs captured by consistency, availability, and partition tolerance reasoning; compression, cost versus performance trade offs, data retention, archival, and compliance concerns. Candidates should be able to reason about durability, persistence guarantees, operational recovery, and storage choices that affect latency, throughput, and cost.