Relational (SQL) vs. Non-Relational (NoSQL) Databases with ORM

Relational (SQL) vs. Non-Relational (NoSQL) Databases

Choosing a database is about deciding how you want to store your information: in a strictly organized ledger (SQL) or a flexible collection of files (NoSQL).

Think of it this way: Relational databases (SQL) are the organized library, and Non-Relational databases (NoSQL) are the flexible, chaotic filing cabinet that lets you throw anything in. Neither one is “better”—they’re just suited for different jobs.

1. Relational Databases: The Organized Library (SQL)

If you value order, consistency, and having a detailed map for everything, you’re looking at a relational database. These have been the standard for decades.

What Makes Them Tick?

• The Structure: They use tables—the classic spreadsheet-style format with rows and columns. Before you store any data, you have to define the table’s structure (the schema) down to the last column. No exceptions.
  • Example: If you have an Orders table, you need columns for OrderID, CustomerID, and OrderDate. Every single order record must have those three items. You can’t just randomly add a “DeliveryDriver’sMood” column to one order.
• The Relationships: The magic is in the connections. They use keys to link tables together. This structure ensures data integrity—if you delete a customer, the database can stop you from having “orphan” orders floating around.
• The Guarantees (ACID): SQL databases are obsessed with reliability. The ACID properties (Atomicity, Consistency, Isolation, Durability) basically guarantee that your transactions are correct, complete, and reliable—every single time.

Where They Shine (The Classic Jobs)

Relational databases are your go-to when precision and history are crucial.

Popular Tools: PostgreSQL, MySQL, Oracle, Microsoft SQL Server.

2. Non-Relational Databases: The Flexible Filing Cabinet (NoSQL)

NoSQL was born from the needs of the internet giants who needed to handle massive, rapidly changing, and often messy data loads that didn’t fit neatly into tables.

What Makes Them Tick?

• The Structure: Forget tables. NoSQL is schema-less and uses various models optimized for different data types:
  • Document Databases (like MongoDB): Store data as flexible JSON-like documents. The next document you save can have completely different fields, and the database won’t complain.
  • Key-Value Stores (like Redis): The fastest approach. Think of it like a giant, super-fast lookup dictionary for things like shopping cart contents.
  • Graph Databases (like Neo4j): Built to map complex relationships, perfect for finding second and third-degree connections (“friends of friends”).
• The Guarantees (BASE): NoSQL trades some of that strict consistency for blistering speed and availability. It follows the BASE model (Basically Available, Soft State, Eventually Consistent). The system is virtually never down, even if the data takes a second to update across all servers.
• The Scaling: NoSQL is built for horizontal scaling. When you need more capacity, you just plug in another cheap server and the system automatically spreads the data across it. This is how you handle millions of users effortlessly.

Where They Shine (The Modern Jobs)

NoSQL is your solution when speed, flexibility, and massive scale are your top concerns.

Popular Tools: MongoDB, Redis, Cassandra, Neo4j.

3. Quick Comparison: Choosing Your Foundation

The Modern Reality

The truth is, very few big companies use only one type of database. They use a mix of both—a strategy called polyglot persistence.

For example, an e-commerce site might use:

1. PostgreSQL (SQL) to manage the core Order and Customer tables (ACID guarantees).
2. MongoDB (Document NoSQL) to store the product catalog because product attributes change often and are flexible.
3. Redis (Key-Value NoSQL) to store user shopping cart contents for lightning-fast retrieval.

Ultimately, the best database for your project is the one that best supports your application’s biggest needs, whether that’s absolute truth (SQL) or massive scale and agility (NoSQL).

ORM(Object Relational Mapping)

Object Relational Mapping (ORM) is a technique used in creating a “bridge” between object-oriented programs and, in most cases, relational databases.

Put another way, you can see the ORM as the layer that connects object oriented programming (OOP) to relational databases.

When interacting with a database using OOP languages, you’ll have to perform different operations like creating, reading, updating, and deleting (CRUD) data from a database. By design, you use SQL for performing these operations in relational databases.

While using SQL for this purpose isn’t necessarily a bad idea, the ORM and ORM tools help simplify the interaction between relational databases and different OOP languages.

What is an ORM Tool?

An ORM tool is software designed to help OOP developers interact with relational databases. So instead of creating your own ORM software from scratch, you can make use of these tools.

Here’s an example of SQL code that retrieves information about a particular user from a database:

"SELECT id, name, email, country, phone_number FROM users WHERE id = 20"

The code above returns information about a user — name, email, country, and phone_number — from a table called users. Using the WHERE clause, we specified that the information should be from a user with an id of 20.

On the other hand, an ORM tool can do the same query as above with simpler methods. That is:

users.GetById(20)

So the code above does the same as the SQL query. Note that every ORM tool is built differently so the methods are never the same, but the general purpose is similar.

ORM tools can generate methods like the one in the last example.

Most OOP languages have a variety of ORM tools that you can choose from. Here are some of the most popular for JavaScript, Java, Python, PHP, and .NET development:

Popular ORM Tools for JavaScript

1. Sequelize

Sequelize is a promise-based ORM for Node.js. It supports SQL databases such as MySQL, PostgreSQL, SQLite, MariaDB, and Microsoft SQL Server. It provides features like model definitions, associations, migrations, and validations.

2. Prisma

Prisma is a modern, type-safe ORM for JavaScript and TypeScript. It supports databases like PostgreSQL, MySQL, SQLite, SQL Server, MongoDB, and CockroachDB. Prisma focuses on developer experience with auto-generated queries and strong type safety.

3. TypeORM

TypeORM is an ORM that works with both JavaScript and TypeScript. It supports SQL databases (MySQL, PostgreSQL, SQLite, SQL Server, Oracle) and some NoSQL databases. It uses decorators and follows patterns similar to Entity Framework.

4. Mongoose

Mongoose is an Object Data Modeling (ODM) library for MongoDB and Node.js. It provides schema-based modeling, validation, middleware, and query building for document-based databases.

Popular ORM Tools for Java

1. Hibernate

Hibernate enables developers to write data persistent classes following OOP concepts like inheritance, polymorphism, association, composition. Hibernate is highly performant and is also scalable.

2. Apache OpenJPA

Apache OpenJPA is also a Java persistence tool. It can be used as a stand-alone POJO (plain old Java object) persistence layer.

3. EclipseLink

EclipseLink is an open source Java persistence solution for relational, XML, and database web services.

4. jOOQ

jOOQ generates Java code from data stored in a database. You can also use this tool to build type safe SQL queries.

5. Oracle TopLink

You can use Oracle TopLink to build high-performance applications that store persistent data. The data can be transformed into either relational data or XML elements.

Popular ORM Tools for Python

1. Django

Django is a great tool for building web applications rapidly.

2. web2py

web2py is an open source full-stack Python framework for building fast, scalable, secure, and data-driven web applications.

3. SQLObject

SQLObject is an object relational manager that provides an object interface to your database.

4. SQLAlchemy

SQLAlchemy provides persistence patterns designed for efficient and high-performing database access.

Popular ORM Tools for PHP

1. Laravel

Laravel comes with an object relational manager called Eloquent which makes interaction with databases easier.

2. CakePHP

CakePHP provides two object types: repositories which give you access to a collection of data and entities which represents individual records of data.

3. Qcodo

Qcodo provides different commands that can be run in the terminal to interact with databases.

4. RedBeanPHP

RedBeanPHP is a zero config object relational mapper.

Popular ORM Tools for .NET

1. Entity Framework

Entity Framework is a multi-database object-database mapper. It supports SQL, SQLite, MySQL, PostgreSQL, and Azure Cosmos DB.

2. NHibernate

NHibernate is an open source object relational mapper with tons of plugins and tools to make development easier and faster.

3. Dapper

Dapper is a micro-ORM. It is mainly used to map queries to objects. This tool doesn’t do most of the things an ORM tool would do like SQL generation, caching results, lazy loading, and so on.

4. Base One Foundation Component Library (BFC)

BFC is a framework for creating networked database applications with Visual Studio and DBMS software from Microsoft, Oracle, IBM, Sybase, and MySQL

You can see more ORM tools here.

Now let’s discuss some of the advantages and disadvantages of using ORM tools.

Advantages of Using ORM Tools

Here are some of the advantages of using an ORM tool:

1. It speeds up development time for teams.

2. Decreases the cost of development.

3. Handles the logic required to interact with databases.

4. Improves security. ORM tools are built to eliminate the possibility of SQL injection attacks.

You write less code when using ORM tools than with SQL.

Disadvantages of Using ORM Tools

1. Learning how to use ORM tools can be time consuming.

2. They are likely not going to perform better when very complex queries are involved.

3. ORMs are generally slower than using SQL.

Summary In this article, we talked about Object Relational Mapping. This is a technique used to connect object oriented programs to relational databases.

We listed some of the popular ORM tools for different programming languages.

We concluded with some of the advantages and disadvantages of using ORM tools. languages.