Database Selection Architecture Wizard (SQL vs NoSQL Decision Tree)
Answer 5 architectural questions to determine the optimal database engine for your workload. Interactive decision tree comparing Relational (Postgres, MySQL), Key-Value (Redis, DynamoDB), Document (MongoDB), Wide-Column (Cassandra), and Graph (Neo4j) databases.
SQL vs NoSQL Architecture Decision Wizard
Answer 4 architectural questions to discover the optimal database stack for your system.
PostgreSQL / AWS Aurora
Your system requires strict ACID transactions, foreign key referential integrity, and flexible relational SQL joins.
- Use Primary-Replica replication for read scale
- Add PgBouncer for connection pooling
- Use B-Tree and Covering indexes
How It Works Under the Hood
Database selection is one of the most critical decisions in system design. Candidates often default to buzzwords like "just use MongoDB" without evaluating data access patterns, transactional requirements, consistency models, and scale characteristics. Choosing the right database requires evaluating ACID requirements, read vs write ratios, query complexity (joins vs key lookups), schema mutability, and horizontal partitionability.
Core Architectural Principles
- Relational (RDBMS): ACID transactions, complex joins, normalized schemas, vertical scaling first.
- Key-Value: Microsecond latency, simple key lookups, extreme horizontal partitionability.
- Wide-Column: High-throughput write workloads, time-series metrics, append-only logs.
- Document: Semi-structured polymorphic payloads with hierarchical embedding.
Never choose a database based on hype. State your decision framework clearly: "Given that our access pattern requires strict transactional integrity for ledger transfers, we will use a relational database with serializable isolation, rather than an eventually consistent NoSQL store."
Relational consistency and join flexibility vs NoSQL horizontal partitionability and write throughput.