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).
Indexing Strategy and Selection
Covers index design principles and practical selection of indexes to accelerate queries while managing storage and write cost. Topics include index types such as B tree hash and bitmap indexes and full text and functional indexes; single column composite and covering indexes; clustered versus nonclustered index architectures and partial or filtered indexes. Candidates should reason about index selectivity and cardinality and how statistics and histograms influence optimizer choices. Also assess index maintenance overhead fragmentation and rebuild strategies and the trade off between faster reads and slower inserts updates and deletes. Practical skills include reading execution plans to identify missing or inefficient indexes proposing index consolidation or covering index designs testing and benchmarking index changes and understanding interactions between indexing partitioning and denormalization.
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.
Polyglot Persistence and Integration
Designing systems that combine multiple database technologies to satisfy diverse data and workload requirements and integrating them reliably. Topics include evaluation criteria for mixing relational databases, document stores, key value stores, time series databases, graph databases, and search engines; change data capture and event driven integration patterns for keeping systems synchronized; patterns for reliable cross system updates such as transactional outbox and idempotent processing; approaches to distributed transactions including two phase commit and saga orchestrations and when to prefer each; data pipeline and stream processing considerations for synchronization and eventual consistency; backup and recovery across heterogeneous systems; monitoring, testing, and schema evolution strategies; and techniques to minimize vendor lock in and operational complexity. Candidates should demonstrate how to reason about consistency boundaries, data ownership, and integration failure modes.
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.
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.
Managed Databases and Data Services
Covers choosing and operating managed database offerings and complementary cloud data services. Candidates should understand managed relational database services such as Amazon Relational Database Service for MySQL PostgreSQL MariaDB Microsoft SQL Server and Oracle, and NoSQL document and key value stores such as Amazon DynamoDB Azure Cosmos Database Google Cloud Firestore and Datastore. Expect to explain when to choose relational versus NoSQL based on data shape query complexity transactional guarantees including atomicity consistency isolation and durability read and write patterns latency and scalability requirements. Understand scaling techniques including vertical scaling read replicas for read scaling horizontal scaling via partitioning or sharding and multi region replication and failover strategies. Be familiar with backup and restore approaches including snapshots point in time recovery cross region replication and disaster recovery planning. Know consistency models and trade offs such as strong eventual and causal consistency, and understand provisioned capacity versus serverless autoscaling models and their cost and operational implications. Candidates should also be able to discuss performance tuning topics such as indexing query optimization caching connection pooling storage and input output optimization monitoring and alerting, as well as security and compliance considerations including encryption access control and network isolation. Finally be prepared to recommend a database solution given workload characteristics such as data size read to write ratio latency targets and operational constraints.
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.