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 Aggregation and Filtering
Focuses on using query operations to filter and aggregate datasets efficiently and correctly. Candidates should demonstrate filtering rows by conditions, applying time based filters, grouping by one or more dimensions, and using aggregate functions such as count, sum, average, minimum, and maximum. It includes correct use of pre aggregation filters and post aggregation filters, the difference between filtering rows before aggregation and filtering aggregated results, combining multiple aggregation levels, calculating distinct counts and percentiles, and composing queries that combine conditional logic and aggregation in a single statement. Performance and readability of queries, and choosing appropriate aggregation granularity for business questions, are also relevant.
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.
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.