Data Architecture: Scenario-Based Questions

Choosing between SQL and NoSQL is a fundamental architecture decision. It depends on data structure, consistency needs, query complexity, and scale patterns.

🗃️ SQL (Relational)

• Structured schema with foreign keys, constraints
• Strong ACID guarantees
• Joins, aggregates, and relational querying
• Best for transactional apps (e.g., banking, CRM)

📦 NoSQL (Non-Relational)

• Flexible schema — document, key-value, column, graph
• Eventually consistent or tunable consistency
• High scalability and horizontal sharding
• Ideal for unstructured or large-scale data (e.g., IoT, analytics)

🔍 Use Case Considerations

• Use SQL when: You need strong consistency, complex joins, normalized schemas.
• Use NoSQL when: You prioritize scale, write throughput, or flexible data models.

📈 Popular Tech Choices

• SQL: PostgreSQL, MySQL, MSSQL, Oracle
• NoSQL: MongoDB, DynamoDB, Cassandra, Redis

✅ Best Practices

• Design based on access patterns, not just data shape
• Evaluate consistency models and trade-offs (CAP theorem)
• Use polyglot persistence if multiple data needs exist

🚫 Common Pitfalls

• Using NoSQL to avoid schema design — leads to complexity later
• Ignoring transaction needs in distributed NoSQL systems
• Over-indexing in SQL → slow writes and bloated DBs

📌 Final Insight

There's no one-size-fits-all. Let the use case guide your data model — not hype. Mature systems often use both SQL and NoSQL where they fit best.