Suggestions for Choosing Between SQL and NoSQL Databases

When your data refuses to fit in neat rows and columns
4 min read

Choosing between a SQL and a NOSQL DB can be confusing without a framework to help guide the decision. Often engineers are more familiar with one or the other. Each one can seem daunting and strange compared to the other.

The choice of DB shapes how you will store, query, and scale your data. Pick wisely, and your system can support your system as it grows. Pick poorly, and you can find yourself wrestling with migrations, performance bottlenecks, and overly complex queries.

My goal here is to help you understand the trade-offs so you can make a good decision based on my experience.

Terms you should know

  • ORM: a tool or framework that lets developers interact with a SQL database using an object-oriented programming language, automatically translating between database tables and code objects.

  • Relational: a database that stores data in structured tables with rows and columns, where relationships between tables are defined using keys.

  • Schema: a structured blueprint that defines how data is organized, including its fields, types, and relationships.

Key Questions to Ask Before Choosing

Before deciding on SQL vs. NoSQL, it helps to step back and ask a few guiding questions about the data:

  1. Do you know the structure (schema) of your data?

  2. How often is the structure likely to change?

  3. What kinds of queries will you need to run?

  4. Are there multiple related data entities?

  5. What are the scalability and performance requirements?

  6. What skills does your team already have?

  7. How important is tooling and ecosystem support (e.g., reporting, integrations)?

Its ok if you don’t know the answer to every question.

When SQL Makes Sense

SQL databases (PostgreSQL, MySQL, SQL Server, etc.) are relational and work best when:

  • The schema is well-defined and not expected to change often.

  • There are multiple related entities that need joins.

  • Complex queries and aggregations are part of the requirements.

  • Strong transactional guarantees (ACID) are necessary.

  • You want access to a mature ecosystem of tools, ORMs, and reporting solutions.

Example: The Insurance Company
Imagine a small insurance company that has operated for years using spreadsheets. They know exactly what data they collect, what relationships exist, and how they query that information. The schema is stable and well understood.

This is a natural fit for SQL since the data is structured, consistency is critical, and relationships matter.

When NoSQL Makes Sense

NoSQL databases (MongoDB, DynamoDB, Couchbase, etc.) are non-relational and are usually a better fit when:

  • The schema is unknown, evolving, or inconsistent.

  • You expect the data structure to change frequently.

  • You mainly query by a few known keys or properties.

  • You need to scale horizontally with semi-structured or unstructured data.

  • Eventual consistency or weaker transactional guarantees are acceptable.

Example: The Trucking Company
Now imagine a trucking company with GPS devices, driver-entered forms, and inconsistent vendor data. The schema is unpredictable and has changed multiple times in the past.

This messy, evolving structure makes SQL harder to manage. A NoSQL database would be a better fit here because it handles irregular data with less upfront schema work, letting the system evolve more easily.

Gray Areas & Trade-offs

The choice isn’t always black and white.

  • Hybrid approaches are feasible:

    • PostgreSQL supports JSONB for semi-structured data.

    • MongoDB supports schema validation.

  • Misconceptions to avoid:

    • “NoSQL is always faster.” It depends on the workload.

    • “SQL doesn’t scale.” Many large companies scale SQL successfully.

  • Practical factors matter too: cost, vendor lock-in, hosting options, and previous experiences all influence the decision.

A Practical Decision Framework

Here’s a simple way to think about it:

  • If your data is structured, stable, and relational → SQL

  • If your data is messy, evolving, and key-driven → NoSQL

You could visualize it as a small checklist or flowchart:

  • Do you have a well-known schema? → SQL

  • Do you expect frequent schema changes? → NoSQL

  • Do you need strong relationships and joins? → SQL

  • Do you just need to fetch by keys or properties? → NoSQL

Remember: the “best” database may change as your system matures.

SQL and NoSQL aren’t opposites, they are tools designed to solve different kinds of problems. Each has strengths, weaknesses, and trade-offs.

  • SQL shines when you know your schema, need strong relationships, and value consistency.

  • NoSQL shines when your data is flexible, evolving, or doesn’t fit neatly into rows and columns.

At the end of the day, the best choice is the one that fits your problem today while leaving room for tomorrow.