Relational vs Non Relational Databases: What’s the Deal and How to Choose the Right One?

Databases have long been the backbone of digital systems, evolving dramatically from simple file storage to sophisticated platforms that underpin the most cutting-edge applications. As technology has advanced, the decision on which type of database to deploy has become increasingly critical. This choice can profoundly influence both the performance and scalability of applications, making it essential for Chief Technology Officers (CTO) and Chief Product Officers (CPO) to be well-informed.

In this article, crafted by ProCoders—a company with over a decade of expertise in software development—we aim to demystify the differences between relational databases vs non-relational databases. By discussing their respective advantages and disadvantages, and exploring their ideal use cases, we aspire to equip you with the knowledge necessary to select the type of database that best aligns with your project’s needs. 

Should you have any further queries after reading, our team is ready to provide additional insights during a personalized consultation!

What are Relational Databases?

Definition and Background

Relational databases are structured storage systems that are based on the relational model, a concept introduced by E.F. Codd. This model organizes data into tables which consist of rows and columns, with each table representing a different type of entity.

Relational databases became the norm in the 1980s and 1990s due to their robustness and the ease with which users could create and maintain them. This period saw the rise of SQL as a powerful tool for managing databases, further cementing the dominance of relational models in data management systems.

Key Features

• Schema-Based Structure: Relational databases require a predefined schema that defines the structure of data, including the tables and the types of data that can be stored in each column. This schema must be defined before data can be stored.
• ACID Properties: One of the most significant advantages of relational databases is their ACID properties—Atomicity, Consistency, Isolation, Durability. These properties ensure that all transactions are processed reliably and help maintain the integrity of data even in complex transactional systems.
• SQL: The use of Structured Query Language (SQL) is pivotal in relational databases. SQL allows for effective querying, manipulating, and managing data, and is supported by nearly all relational database systems.
• Normalization: This process involves organizing data to minimize redundancy and improve data integrity. Normalization typically involves dividing large tables into smaller, less redundant tables and defining relationships between them.

Relational Database Examples

Some of the most popular Relational Database Management Systems (RDBMS) include MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. These systems are widely used across various industries due to their reliability and powerful features.

Use Cases

Relational databases are particularly well-suited for scenarios that require complex queries and where data integrity is crucial. Typical use cases include financial systems, inventory management, and enterprise resource planning (ERP) systems, where the relationships between different types of data are complex and deeply interconnected.

What are Non-Relational Databases?

Definition and Background

Non-relational databases, commonly referred to as NoSQL databases, represent a broad class of database management systems that diverge from the traditional relational database model. Unlike relational databases that store data in tables with predefined schemas, non-relational databases are designed to store data in more flexible formats, accommodating a variety of data models.

The rise of non-relational databases is closely tied to the challenges posed by big data, cloud computing, and modern web applications. These technologies demanded faster processing, greater scalability, and more flexibility than traditional relational databases could offer, paving the way for the adoption of NoSQL technologies.

Types of Non-Relational Databases

Document Stores: MongoDB uses JSON or BSON formats to store data. This model allows the embedding of varied data structures within documents, making it a versatile choice for content management systems and e-commerce applications.

Key-Value Stores: Take Redis as an example; it stores data as pairs of keys and values, making it incredibly efficient for scenarios requiring rapid read and write operations, such as caching.

Column-Family Stores: Apache Cassandra organizes data into columns instead of rows, optimizing it for quick data retrieval and append operations, suitable for handling large-scale distributed data.

Graph Databases: Neo4j stores data in nodes and relationships, facilitating efficient querying of complex interconnections. This makes it ideal for social network analysis or recommendation systems.

Key Features

Schema Flexibility: One of the defining characteristics of non-relational databases is their schema-less design, which allows on-the-fly modification of the data structure without downtime.

Scalability: Non-relational databases typically excel in horizontal scaling, distributing data across many servers to handle increased loads effectively.

BASE Properties: They adhere to BASE (Basically Available, Soft state, Eventual consistency) principles as opposed to the strict ACID (Atomicity, Consistency, Isolation, Durability) properties of relational databases, offering a more flexible approach to data consistency in distributed environments.

Non Relational Database Examples

Popular NoSQL databases include MongoDB, Apache Cassandra, Redis, and Neo4j. Each has been tailored to meet specific data management needs, from high-speed caching and scalable content management to efficient processing of connected data.

Use Cases

Non-relational databases shine in scenarios where relational databases might struggle, such as in handling very large sets of distributed data, supporting rapid iteration in application development, or managing data with a flexible schema. Ideal use cases include real-time analytics, content management, and social networking platforms, where the ability to scale dynamically and manage vast amounts of unstructured data is crucial.

Relational vs Non-Relational Databases Comparison

Relational vs Non-Relational Databases Data Model and Structure

Relational Databases utilize a structured data model with fixed schemas. This includes organized tables where data is stored in rows and columns, and relationships are strictly defined, often through foreign keys and normalized forms. This structured approach ensures data integrity and reduces redundancy, making it ideal for scenarios where relationships between data points are complex.

Non-Relational Databases, on the other hand, offer a more flexible data model. These databases can store data in various formats such as documents, key-value pairs, wide columns, or even graphs. This variety allows developers to tailor the data architecture to the specific needs of the application, which can be particularly beneficial for handling unstructured data or rapidly changing requirements.

Relational vs Non-Relational Databases Scalability

Relational Databases traditionally rely on vertical scaling to handle increased loads, which involves adding more power to the existing server (such as more CPUs, RAM, or faster storage). While effective up to a point, this approach can become costly and has physical limitations.

Non-Relational Databases are designed to scale horizontally, meaning you can add more servers to a database cluster to distribute the load. This type of scaling is generally more flexible and cost-effective, particularly when dealing with massive volumes of data and high user loads, as it allows for growth without significant disruptions or downtime.

Relational vs Non-Relational Databases Performance and Speed

Relational Databases are highly efficient for handling complex queries that involve multiple tables and complex joins. They also excel in situations where transactional integrity, such as banking systems or any application requiring high precision and reliability in data handling, is paramount.

Non-Relational Databases are optimized for high-speed read and write operations, making them suitable for scenarios where quick data access is crucial, such as in real-time analytics and large-scale web applications. They can handle large volumes of data with minimal latency, which is beneficial for applications that require rapid, on-the-fly data processing.

Consistency and Availability

Relational Databases adhere to ACID (Atomicity, Consistency, Isolation, Durability) principles, which ensure that transactions are processed reliably and errors or physical failures do not cause data inconsistency.

Non-Relational Databases often use eventual consistency models to offer high availability and partition tolerance, prioritizing accessibility over immediate consistency. This means that after a write operation, it might take some time before the data is consistent across all nodes in the cluster.

Flexibility

Relational Databases require a rigid schema setup before data entry, which necessitates a thorough understanding of the data and its interrelations at the design stage. Changes to the schema can be complex and disruptive.

Non-Relational Databases provide greater flexibility with schema-less data models, where the schema can be modified on the fly without significant downtime or performance impact. This makes non-relational databases highly adaptable to changes and evolving needs of business applications.

Relational vs Non-Relational Databases Overview Table

Aspect	Relational Databases	Non-Relational Databases
Data Model and Structure	Structured, fixed schemas with tables, rows, and columns; uses foreign keys and normalization	Flexible data models; supports documents, key-value pairs, wide columns, and graphs
Scalability	Vertical scaling (adding more power to a single server)	Horizontal scaling (adding more servers to a cluster)
Performance and Speed	Efficient for complex queries and transactional integrity	Optimized for high-speed read/write operations; good for real-time analytics
Consistency and Availability	Adheres to ACID principles for data reliability	Uses eventual consistency models for high availability
Flexibility	Requires a rigid schema setup, complex to change	Schema-less, allowing for on-the-fly modifications

Relational vs Non-Relational Databases Pros and Cons

Relational Databases

Pros:

• Strong Data Integrity: Relational databases ensure high data accuracy and consistency through strict schema and relationship enforcement.
• Complex Querying Capabilities: They excel in handling complex queries involving multiple tables and relationships, thanks to sophisticated SQL engines.
• Well-Understood Technology: Having been in use for decades, relational databases are stable, with well-documented best practices and a wide range of expertise available.

Cons:

• Less Flexible: The requirement for predefined schemas makes relational databases less adaptable to changes in data structure.
• Horizontal Scaling Challenges: While vertical scaling is straightforward, horizontal scaling (adding more servers) is more complex and often not as efficient.
• Schema Changes Require Downtime: Modifying the database schema usually requires downtime, which can disrupt service and complicate updates.

Non-Relational Databases

Pros:

• High Scalability: Designed for horizontal scaling, non-relational databases can easily expand across multiple servers and handle large volumes of data.
• Flexible Schemas: They allow for on-the-fly modifications to the data structure without significant downtime, making them ideal for rapidly evolving application requirements.
• Optimized for Distributed Environments: Non-relational databases are built to operate effectively across distributed systems, enhancing their resilience and availability.

Cons:

• Weaker Consistency Guarantees: Most non-relational systems use eventual consistency, which can lead to temporary discrepancies in data after updates.
• Less Mature Querying Capabilities: While improvements are being made, the querying capabilities of non-relational databases are generally not as advanced or powerful as SQL used in relational databases.
• Complex Data Management: The flexibility and variety of data models can lead to more complex data management and integration scenarios, particularly when dealing with multiple types of non-relational databases.

When selecting a database for your project, you’ll have to assess factors like data volume, complexity, and future growth to align with project demands.

Choosing the Right Type of database: Relational vs Non-relational

Project Requirements

• Data Volume and Complexity: Relational databases are ideal for applications with complex relationships and fixed schemas, while non-relational databases suit those handling large volumes of semi-structured or unstructured data.
• Consistency Needs: Relational databases with ACID compliance are better for systems requiring strong consistency, such as financial services. Non-relational databases, which often use eventual consistency, are more suitable for applications where availability is critical.
• Scalability Needs: Consider whether your project requires vertical scaling, which is common for relational databases, or horizontal scaling, which non-relational databases handle well.

Development and Maintenance

• Team Expertise: Choose based on your team’s familiarity with SQL and traditional data modeling for relational databases, or modern practices for non-relational databases.
• Operational Complexity: Relational databases may need more maintenance as transactions scale, while non-relational databases can be easier to manage but may present consistency challenges.

Future Scalability

• Growth Projections: Assess future data and user growth to determine if a database that scales out, like non-relational databases, is necessary.
• Community and Support: A strong community and good support services are essential for efficient issue resolution and smooth database operations.

By carefully evaluating these factors, CTOs and CPOs can choose a database that meets current needs and supports long-term growth and maintenance, ensuring alignment with business objectives.

Relational vs Non-Relational Databases: Conclusion

Choosing the right database hinges on a deep understanding of your specific project needs, anticipated scalability, and long-term goals. Each type of database offers unique benefits that may be more or less suitable depending on the application in question. As such, it is crucial for decision-makers, particularly CTOs and CPOs, to align their database selection with the strategic objectives of their projects.

We encourage you to experiment with both non relational vs relational databases to truly appreciate their capabilities and limitations. This hands-on approach will provide valuable insights and help determine the best fit for your various applications. Should you require further guidance or wish to delve deeper into how these databases can be tailored to your specific needs, the team at ProCoders is ready to assist you. We invite you to reach out for a personalized consultation, ensuring that your next database decision is as informed as it is strategic.