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 Types and Constraints
Covers selection and implications of database data types and the use of constraints to enforce data integrity and performance considerations. Topics include numeric types and precision, integer and floating types, boolean, date and time types, fixed length and variable length string types such as CHAR and VARCHAR, long text and binary types, and semi structured types such as JSON and XML. Also includes constraint types and usage such as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK, default values, referential integrity and cascade actions. Candidates should understand storage implications, indexing trade offs, collation and encoding considerations, normalization and denormalization effects, and how type and constraint choices affect query performance, storage, and data quality.
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.
Cloud Data Warehouse Design and Optimization
Covers design and optimization of analytical systems and data warehouses on cloud platforms. Topics include schema design patterns for analytics such as star schema and snowflake schema, purposeful denormalization for query performance, column oriented storage characteristics, distribution and sort key selection, partitioning and clustering strategies, incremental loading patterns, handling slowly changing dimensions, time series data modeling, cost and performance trade offs in cloud managed warehouses, and platform specific features that affect query performance and storage layout. Candidates should be able to discuss end to end design considerations for large scale analytic workloads and trade offs between latency, cost, and maintainability.
Data Modeling for Query Performance
Focuses on schema and data modeling choices that enable efficient querying at scale. Topics include normalization and denormalization trade offs, analytical schemas such as star schema and snowflake schema, the roles of fact tables and dimension tables, modeling for common query patterns and aggregations, and how model choices impact indexing, join costs, and storage. Candidates should be able to justify schema decisions based on query workload, discuss partitioning and sharding implications for model design, and propose modeling adjustments that improve query latency and maintainability.
Handling Large Scale Data and Time Series Data
Design for efficient storage and querying of massive datasets. Understand time-series data patterns (metrics, logs), specialized solutions like InfluxDB or TimescaleDB, and archiving strategies for historical data.
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.
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.
Redshift & PostgreSQL Syntax
SQL dialect differences and syntax specifics between Amazon Redshift and PostgreSQL, including data types, functions, operators, window functions, Common Table Expressions (CTEs), and best practices for writing portable queries and optimizing performance within the database layer.