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).
Structured Query Language Join Operations
Comprehensive coverage of Structured Query Language join types and multi table query patterns used to combine relational data and answer business questions. Topics include inner join, left join, right join, full outer join, cross join, self join, and anti join patterns implemented with NOT EXISTS and NOT IN. Candidates should understand equi joins versus non equi joins, joining on expressions and composite keys, and how join choice affects row counts and null semantics. Practical skills include translating business requirements into correct join logic, chaining joins across two or more tables, constructing multi table aggregations, handling one to many relationships and duplicate rows, deduplication strategies, and managing orphan records and referential integrity issues. Additional areas covered are join conditions versus WHERE clause filtering, aliasing for readability, using functions such as coalesce to manage null values, avoiding unintended Cartesian products, and basic performance considerations including join order, appropriate indexing, and interpreting query execution plans to diagnose slow joins. Interviewers may probe result correctness, edge cases such as null and composite key behavior, and the candidate ability to validate outputs against expected business logic.
Structured Query Language
Ability to write and reason about queries using structured query language to extract and aggregate data from relational databases. Key capabilities include composing select statements with appropriate where clauses, grouping and aggregation, joining multiple tables with clear join keys, basic subqueries, and simple windowing or analytic patterns. Candidates should understand relational schema design, primary and foreign keys, basic performance considerations such as filtering and indexing, and how to translate business questions into reproducible queries for analysis and dashboards.