Relational vs NoSQL in Real Projects, How I Choose the Right Database for .NET and Cloud Systems

Source: Dev.to

Shape of the Data, and How It Changes

Relational shines when:

• Relationships are complex and matter (e.g., orders, customers, invoices).
• Queries need joins, groupings, or constraints.
• You need ACID guarantees (banking, inventory, booking).

NoSQL fits when:

• Data is document‑like, deeply nested, or varies per record (e.g., user profiles, logs, chat messages).
• The schema is a moving target, or you want to avoid migrations for every tweak.
• You’re optimizing for single‑entity lookups by key.

Query Patterns: Not Just Reads, But Writes and Updates

Ask yourself how the data will be accessed and updated, not just stored.

• If you need to update tiny bits of data atomically, an RDBMS is your friend.
• If you’re mostly reading or writing whole documents (JSON blobs), NoSQL is smoother.
• For reporting, analytics, or any ad‑hoc queries, SQL’s maturity is hard to beat.

Consistency, Transactions, and the Myth of “NoSQL Magic”

• Need multi‑entity transactions? SQL is built for this. Some NoSQL databases now offer transactions, but they’re rarely as robust or as well‑understood by teams.
• NoSQL’s eventual‑consistency model can introduce subtle bugs, especially in distributed systems.

Scaling: Vertical, Horizontal, and Operational Reality

• SQL scales vertically (bigger box) and, with cloud services like Azure SQL Hyperscale, can handle far more than most apps need.
• NoSQL excels at horizontal scaling and write‑heavy workloads, but it comes with more operational complexity and trickier consistency guarantees.

Developer Experience: Don’t Underestimate the Human Factor

• What does your team know today? Productivity counts.
• EF Core and Dapper make SQL pleasant in .NET land.
• MongoDB drivers are great, but error handling and validation are on you.

Cost, Cloud, and Vendor Lock‑In

• Managed SQL (Azure SQL, AWS RDS) is straightforward, but scaling up can become pricey.
• NoSQL (Cosmos DB, DynamoDB) is cheap at low usage, yet costs can spike with unoptimized queries or “hot” partitions.
• Beware features that tie you to a specific cloud (e.g., Cosmos DB’s unique partitioning quirks).

API and System Design: Evolution Without Regret

• SQL schemas force you to think about constraints, but they can slow you down when evolving fast.
• NoSQL lets you iterate quickly, but can lead to “schema drift” and technical debt if you’re not disciplined.
• For versioning APIs, consider storing raw requests and responses in NoSQL for audit/troubleshooting while keeping core business data in SQL.

Concrete Takeaways

• Start with SQL unless you have a clear reason not to. The boring choice is often the right one.
• Use NoSQL for parts of your system that truly need flexibility, massive horizontal scale, or unstructured data.
• Avoid mixing models in a single service unless you’re ready for the added complexity; if you do, draw strict boundaries (e.g., CQRS or microservices).
• Model your queries and evolution over time before locking in a storage option.
• Budget for data validation and migration, whichever database you choose.

Your Turn

Drop your war stories, questions, or alternative solutions in the comments. If you want a practical code template for EF Core or MongoDB data models, let me know and I’ll share!

Tags: SoftwareEngineering, DotNet, SystemDesign, Cloud, APIDesign, RealWorldEngineering