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).
Technology Selection & Deep Technical Knowledge
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 Fundamentals and Storage Engines
Core principles and components of data storage and persistence systems. This includes storage engine architectures and how they affect query processing and performance; transactions and isolation including atomicity, consistency, isolation, and durability; concurrency control and isolation levels; indexing strategies and how indexes affect read and write amplification; physical versus logical storage and object, block, and file storage characteristics; caching layers and cache invalidation patterns; replication basics and how replication affects durability and read performance; backup and recovery techniques including snapshots and point in time recovery; trade offs captured by consistency, availability, and partition tolerance reasoning; compression, cost versus performance trade offs, data retention, archival, and compliance concerns. Candidates should be able to reason about durability, persistence guarantees, operational recovery, and storage choices that affect latency, throughput, and cost.
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.
Database Architecture and Partitioning
Design database architecture and partitioning strategies appropriate to workload and access patterns. Evaluate database types including relational and various NoSQL models, schema design and indexing strategies, and when to use a monolithic database versus sharding. Cover sharding approaches such as range based, hash based, consistent hashing, and directory based sharding, as well as replica topologies, read replicas, replication lag, and handling cross shard queries. Address operational concerns at scale: resharding, mitigating hot partitions, balancing data distribution, transactional and consistency guarantees, and the trade offs between availability, consistency, and partition tolerance. Include monitoring, migration strategies, and impact on application logic and joins.
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.
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.
Infrastructure and Database Systems
Fundamental infrastructure and database engineering concepts relevant to analytics platforms and general backend systems. Topics include relational and non relational database architecture indexing strategies query optimization replication and consistency trade offs sharding and partitioning approaches caching systems design message queues and event streaming systems and how these components integrate to meet performance reliability and cost objectives. Candidates should be able to reason about capacity planning high availability disaster recovery backup strategies and operational concerns such as monitoring alerting and graceful degradation under load.
Data Consistency and Recovery
Covers the spectrum of data consistency models used in distributed systems and the operational practices for detecting and recovering from inconsistency. Topics include strong consistency guarantees provided by atomicity, consistency, isolation, and durability style transactions and synchronous replication, and weaker models such as eventual consistency and causal consistency along with their read guarantees like read your writes and monotonic reads. Explain the trade offs between consistency, availability, and latency and how those trade offs influence architecture decisions, user experience, and cost. Discuss replication strategies including synchronous replication, asynchronous replication, and read replicas, and how replication modes affect staleness and failure behavior. Include coordination and consensus mechanisms for achieving stronger guarantees, for example leader based replication and consensus protocols, and distributed transaction approaches such as two phase commit. Cover operational concerns: how consistency choices change testing, deployment, monitoring, and incident response. Describe detection and recovery techniques for inconsistency such as validation checks, reconciliation and anti entropy processes, tombstones and conflict resolution strategies, use of vector clocks or conflict free replicated data types to resolve concurrent updates, point in time recovery and backups, and procedures for partial repairs, rollbacks, and replays. At senior levels also address how consistency decisions shape runbooks, alerting, and post incident analysis.
Database and Data Platform Selection
Evaluation and selection of database and data platform technologies to meet analytical and operational needs. Covers assessment of relational, non relational, columnar, and specialized systems such as time series and search engines; data warehouse platforms and cloud analytics platforms; query patterns and workload characteristics; consistency and transactional guarantees; partitioning and clustering strategies; storage formats and compression; performance and scalability trade offs; operational complexity and administration overhead; data ingestion and incremental loading patterns; pricing and cloud platform considerations; and how to choose the right solution based on data volume, concurrency, latency requirements, and total cost of ownership.