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 Scalability and High Availability
Architectural approaches and operational practices for scaling and maintaining database availability. Topics include vertical versus horizontal scaling trade offs; replication topologies, leader and follower roles, read replicas and replica lag; read write splitting and connection pooling; sharding and partitioning strategies including range based, hash based, and consistent hashing approaches; handling hot partitions and data skew; federation and multi database federation patterns; cache layers and cache invalidation; rebalancing and resharding strategies; distributed concurrency control and transactional guarantees across shards; multi region deployment strategies, cross region failover and disaster recovery; monitoring, capacity planning, automation for failover and backups, and cost optimization at scale. Candidates should be able to pick scaling approaches based on read and write patterns and explain operational complexity and trade offs introduced by distributed data.
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.
Data Storage and Query Optimization
Focuses on selecting appropriate storage technologies and optimizing data access patterns. Topics include comparing relational and non relational databases, data modeling for common read and write patterns, indexing strategies and index costs, query execution plans and optimization techniques, caching and cache invalidation approaches, denormalization and materialized views, sharding and partitioning strategies, replication and failover considerations, and practical steps to diagnose and speed up slow queries. Candidates should justify choices based on constraints and demonstrate approaches to measure and improve performance.
Database Fundamentals and Data Modeling
Encompasses foundational database concepts and techniques for designing and querying data stores. Topics include relational database concepts, structured query language fundamentals for selecting and modifying data, schema design with tables and relationships, normalization principles, entity relationship modeling, indexing and basic performance considerations, transactions and data integrity, constraints and data types, and common operational tasks such as migrations, backups, and simple optimization strategies. Also discusses trade offs with nonrelational stores and when to choose different persistence models.
Search and Indexing at Scale
Designing search systems using technologies like Elasticsearch or similar. Understanding indexing strategies, query optimization, and ranking algorithms. Designing relevance scoring and filtering mechanisms.
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.