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).
CTEs & Subqueries
Common Table Expressions (CTEs) and subqueries in SQL, including syntax, recursive CTEs, usage patterns, performance implications, and techniques for writing clear, efficient queries. Covers when to use CTEs versus subqueries, refactoring patterns, and potential pitfalls.
Common Table Expressions and Subqueries
Covers writing and structuring complex SQL queries using Common Table Expressions and subqueries, including when to prefer one approach over another for readability, maintainability, and performance. Candidates should be able to author WITH clauses to break multi step logic into clear stages, implement recursive CTEs for hierarchical data, and use subqueries in SELECT, FROM, and WHERE clauses. This topic also includes understanding correlated versus non correlated subqueries, how subqueries interact with joins and window functions, and practical guidance on choosing CTEs, subqueries, or joins based on clarity and execution characteristics. Interviewers may probe syntax, typical pitfalls, refactoring nested queries into CTEs, testing and validating each step of a CTE pipeline, and trade offs that affect execution plans and index usage.
Aggregation and Grouping
Covers SQL grouping and aggregation concepts used to summarize data across rows. Key skills include using GROUP BY with aggregate functions such as COUNT, SUM, AVG, MIN, and MAX, counting distinct values, and filtering grouped results with HAVING while understanding the difference between WHERE and HAVING. Candidates should demonstrate correct handling of NULL values in aggregates, grouping by expressions and multiple columns, and writing multi level aggregations using ROLLUP, CUBE, and GROUPING SETS. Also important is knowing when to use subqueries or common table expressions for intermediate aggregation, the difference between aggregate functions and window functions, and how grouping interacts with joins and data types. Interview questions may test correctness of queries, edge cases, performance considerations such as appropriate indexes and query plans, and the ability to transform business questions like who are the top customers or which categories have declining sales into correct aggregated SQL statements.
Complex Data Integration and Joins
Handling intricate join scenarios: multi-condition joins, conditional joins with complex logic, joining on date ranges or overlapping time periods, complex left joins with multiple filtering conditions, self-joins for hierarchical or relationship data, handling non-standard relationships between tables. Understanding implications of different join types on row counts, NULL values, and duplicate handling. Designing queries that correctly integrate data from multiple sources while maintaining data integrity and avoiding duplicate counting or missing data.
Data Joining and Merging Strategies
Focuses on combining datasets correctly and efficiently. Includes different join types such as inner, left, right, full outer, and cross joins; implications of each join type for result cardinality and missing data; strategies for resolving many to many relationships and duplicate records; methods for identifying and cleaning and aligning join keys including normalization and fuzzy matching; handling mismatched or missing keys and null semantics; performance and memory considerations when joining large tables or distributed datasets; and testing and validation to ensure joins preserve referential integrity and do not introduce inadvertent data leakage.
Database Selection and Trade Offs
How to evaluate and choose data storage systems and architectures based on workload characteristics and business constraints. Coverage includes differences between relational and nonrelational families such as document stores, key value stores, wide column stores, graph databases, time series databases, and search engines; mapping query patterns and latency requirements to storage options; trade offs between strong consistency and eventual consistency and their impact on availability and complexity; partition key design, replication strategies, and high availability considerations; operational concerns including backups, monitoring, vendor and cost trade offs, migration or hybrid strategies, and when to adopt polyglot persistence. Senior level discussion includes selecting specific managed services and reasoning about expected load patterns, failure modes, and operational burden.
SQL Fundamentals and Query Writing
Comprehensive query writing skills from basic to intermediate level. Topics include SELECT and WHERE, joining tables with inner and outer joins, grouping with GROUP BY and filtering groups with HAVING, common aggregation functions such as COUNT SUM AVG MIN and MAX, ORDER BY and DISTINCT, subqueries and common table expressions, basic window functions such as ROW_NUMBER and RANK, union operations, and principles of readable and maintainable query composition. Also covers basic query execution awareness and common performance pitfalls and how to write correct, efficient queries for combining and summarizing relational data.
Query Optimization and Execution Plans
Focuses on diagnosing slow queries and reducing execution cost through analysis of query execution plans and systematic query rewrites. Candidates should be able to read and interpret explain output and execution plans including identifying expensive operators such as sequential table scans index scans sorts nested loop join hash join and merge join and explaining why those operators appear. Core skills include cost and cardinality estimation understanding join order and predicate placement predicate pushdown and selectivity reasoning comparing exists versus in versus join patterns and identifying common anti patterns such as N plus one queries. The topic covers profiling and benchmarking approaches using explain analyze and runtime statistics comparing estimated and actual row counts proposing and validating query rewrites and configuration or schema changes and reasoning about trade offs when using materialized views caching denormalization or partitioning to improve performance. Candidates should present step by step approaches to diagnose problems measure improvements and assess impact on other workloads.
Database Design and Query Optimization
Principles of database schema design and performance optimization including relational and non relational trade offs, normalization and denormalization, indexing strategies and index types, clustered and non clustered indexes, query execution plans, common table expressions for readable complex queries, detecting missing or redundant indexes, sharding and partitioning strategies, and consistency and availability trade offs. Candidates should demonstrate knowledge of optimizing reads and writes, diagnosing slow queries, and selecting the appropriate database model for scale and consistency requirements.