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).
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.
Advanced SQL Window Functions
Mastery of Structured Query Language window functions and advanced aggregation techniques for analytical queries. Core function families include ranking functions such as ROW_NUMBER, RANK, DENSE_RANK, and NTILE; offset functions such as LAG and LEAD; value functions such as FIRST_VALUE, LAST_VALUE, and NTH_VALUE; and aggregate window expressions such as SUM OVER and AVG OVER. Candidates should understand the OVER clause with PARTITION BY and ORDER BY, frame specifications using ROWS BETWEEN and RANGE BETWEEN, tie handling, null behavior, and how frame definitions affect results. Common application patterns include top N per group, deduplication using row numbering, running totals and cumulative aggregates, moving averages, percent rank and distribution calculations, event sequencing and period over period comparisons, gap and island analysis, cohort and retention analysis, and trend and growth calculations. The topic also covers structuring complex queries with Common Table Expressions including recursive Common Table Expressions to break multi step analytical pipelines and to handle hierarchical or iterative problems, and choosing between window functions, GROUP BY, joins, and subqueries for correctness and readability. Performance and correctness considerations are essential, including join and sort costs, index usage, memory and sort spill behavior, execution planning and query optimization techniques, and trade offs across different database dialects and large data volumes. Interview assessments typically ask candidates to write and explain queries that use these functions, reason about frame semantics for edge cases such as ties, nulls, and partition boundaries, and to rewrite or optimize expensive queries.
Data Warehouse and Dimensional Modeling
Design and model scalable analytical data systems using dimensional modeling principles and data warehouse architecture patterns. Core concepts include fact and dimension tables, defining and enforcing grain, surrogate keys, degenerate and role playing dimensions, conformed dimensions, and handling slowly changing dimensions including Type One, Type Two, and Type Three. Understand schema choices and trade offs such as star schema versus snowflake schema, normalization versus denormalization, and fact table types including transactional, periodic snapshot, and accumulating snapshot. Apply design decisions to meet query patterns and performance goals by considering partitioning, indexing, compression, columnar storage, and aggregation strategies. Be able to design schemas for different business domains, reason about data integration and consistency, and optimize for common analytical workloads and reporting requirements.
Relational Databases and SQL
Focuses on relational database fundamentals and practical SQL skills. Candidates should be able to write and reason about SELECT queries, JOINs, aggregations, grouping, filtering, common table expressions, and window functions. They should understand schema design trade offs including normalization and denormalization, indexing strategies and index types, query performance considerations and basic optimization techniques, how to read an execution plan, and transaction semantics including isolation levels and ACID guarantees. Interviewers may test writing efficient queries, designing normalized schemas for given requirements, suggesting appropriate indexes, and explaining how to diagnose and improve slow queries.
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.
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.
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.
Join Operations and Multi Table Queries
Comprehensive mastery of joining data across two or more tables in Structured Query Language. Candidates should understand and be able to use inner join, left join, right join, and full outer join semantics, including how each type affects row inclusion and null propagation. Be familiar with self joins, cross joins and anti join and semi join patterns for filtering. Know how to write correct multi table join conditions to avoid inadvertent Cartesian products, how to deduplicate and validate results by checking row counts and key uniqueness, and how to handle nulls and duplicate column names. Understand when to prefer joins versus subqueries or common table expressions for clarity or performance. Be able to read and interpret execution plans and explain how join order, join algorithms such as nested loop join, hash join, and merge join, and appropriate indexing affect performance. Recognize differences in join syntax and behavior across Structured Query Language dialects, including use of USING versus ON clauses and older comma separated join styles. Practice building queries that combine filtering, aggregation, grouping, and joins across three or more tables to express realistic business logic while keeping correctness and performance in mind.
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.