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).
Database Design and Architecture
Designing and architecting databases for production and cloud environments with attention to data modeling, schema design, and access pattern optimization. Topics include normalization and denormalization trade offs, schema versus query driven modeling, entity and relationship design for transactional and analytical workloads, indexing and query optimization techniques, partitioning and sharding design decisions, schema evolution and migration strategies, materialized views and caching strategies, selection of storage layers for different data shapes, and practical operational runbooks for provisioning, monitoring, alerting, backups, disaster recovery, and capacity planning. Candidates should justify schema and architecture choices with respect to latency, throughput, development and operational complexity, maintainability, and cost.
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.
SQL Scenarios
Advanced SQL query design and optimization scenarios, including complex joins, subqueries, window functions, common table expressions (CTEs), set operations, indexing strategies, explain plans, and performance considerations across relational databases.
Relational Database Fundamentals and Design
Core concepts of relational databases and schema design including tables, relationships such as one to one one to many and many to many, primary keys and foreign keys, data integrity constraints, and the properties of atomicity consistency isolation and durability and why they matter. Understand differences between relational systems using structured query language and nonrelational databases, indexing strategies, normalization and denormalization trade offs, simple query optimization techniques, and when to choose a normalized relational design versus a document or key value store. Candidates should be able to perform basic entity identification, produce simple schema diagrams, explain persistence and durability considerations, and reason about basic performance and scaling trade offs.
Storage and Database Infrastructure
Storage concepts: SSDs vs. HDDs, RAID configurations, storage protocols. Database troubleshooting basics, replication concepts, backup and recovery strategies, understanding query performance and index behavior, and storage at scale.
Data Infrastructure Technology Selection
Deep understanding of specific technologies relevant to complex system design. Master databases (PostgreSQL, Cassandra, DynamoDB, Elasticsearch), message queues (Kafka, RabbitMQ), caching systems (Redis), search engines, and frameworks. Understand their strengths, weaknesses, trade-offs, operational characteristics, scaling patterns, and common pitfalls. Be able to justify technology choices based on specific system requirements.
Database Troubleshooting and Diagnostics
Systematic approaches and technical techniques for diagnosing database issues and restoring healthy operation. Topics include identifying symptoms, gathering diagnostic data from error logs and system views, analyzing slow queries with explain plans and profiling, diagnosing connection and authentication failures, detecting and resolving deadlocks and blocking, capacity and storage issues, replication and consistency problems, backup and restore verification, and corruption investigation. Candidates should be familiar with database specific diagnostic tools, monitoring and alerting metrics, indexing and query optimization strategies, and effective communication of findings to application and infrastructure teams.
Storage Services and Data Management
Know primary storage options: Object Storage (S3, Azure Blob, GCS) - for unstructured data at scale, highly available, cost-effective. Block Storage (EBS, Azure Managed Disks) - for VM storage, IOPS/throughput optimized. Databases - Relational (RDS, Azure SQL, Cloud SQL) for structured data with relationships; NoSQL (DynamoDB, Cosmos DB, Firestore) for flexible schemas and scale. Understand access patterns, durability, and consistency models. Know when to use each storage type based on data characteristics and access patterns.
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.