Relational vs. Non-Relational Databases

Relational vs. Non-Relational Databases: What's the Difference?

I keep seeing "relational" and "non-relational" everywhere, so I wrote this down to get it straight in my head. I am also revising for my Google Cloud Engineer exam, so the GCP examples are front of mind.

Relational databases (SQL)

Relational databases organize data into tables with defined schemas. If you have structured data and relationships matter (customers and orders), this is usually the safest default.

Things I associate with relational DBs:

Schema-based: rows and columns with strict data types.
Relationships: primary keys and foreign keys keep tables consistent.
ACID: atomicity, consistency, isolation, durability.

Google Cloud options that fit here:

Cloud SQL: managed PostgreSQL/MySQL/SQL Server.
AlloyDB: managed PostgreSQL with more performance.
Spanner: globally distributed SQL with strong consistency.

Non-relational databases (NoSQL)

NoSQL systems are more flexible and tend to scale horizontally. They store data as documents, key-value pairs, wide columns, etc. Great for messy or fast-changing data.

Things I associate with NoSQL:

Flexible schema: you can change shape as you go.
Horizontal scaling: built to spread across many servers.
BASE-ish behavior: basically available, soft state, eventual consistency.

Google Cloud options here:

Bigtable: huge throughput and low latency, good for write-heavy workloads.
Firestore: serverless, great for web/mobile apps.
Memorystore: in-memory cache (Redis/Memcached).

How I think about choosing

My rough mental checklist:

Data shape: structured and relational? SQL. Loose and changing? NoSQL.
Consistency: need strict consistency? SQL. Can tolerate eventual consistency? NoSQL might be fine.
Scale: huge scale or global distribution? Spanner or Bigtable. Modest scale? Cloud SQL is often enough.
Cost: do not overpay for capability you do not need.

Quick note on AI and vector search

Generative AI use cases often involve vector embeddings (arrays of numbers that represent meaning). Some relational options now support vector search, and some NoSQL options do too. On GCP, this is why you see features around vector search in things like AlloyDB and Cloud SQL, plus integrations with Vertex AI.

Final thoughts

There is no one-size-fits-all. The right database depends on your data, your consistency needs, and how you expect the system to scale. I am still learning, but writing this out helped me make sense of it.