Some developers usually prefer to research the right tools for their ideas before building. Others build their project with whatever tools are at their disposal at the time of conceiving the idea and change them as their needs change. Whichever category you fall into, one thing is sure – at some point, you will sit to evaluate if the tools you are currently using are the right ones for you.

There are no best tools, just the right tools for your project. For example, when you need to make a round hole in a wood, a keyhole saw is the right tool for the task. But when you need to cut pipes, rods, or other metalwork, you would use a hacksaw because that is the right tool for the job.

There is a never-ending debate on what database is the best. This article is not looking to add to the number. Rather, it aims to highlight the strengths of these databases to enable you to pick the right one for your project.

What Are Relational Databases?

Relational Databases (RDBMS), also known as SQL databases, are often referred to in some quarters as the organized filing cabinets of the database world. RDBMS stores data in tables with rows and columns, making it resemble a spreadsheet. In a table, each row represents a record, and each column represents an attribute of that record. The database allows you to efficiently query and connect data across multiple tables using relationships established through foreign keys. MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server are popular RDBMS.

Concepts of RDBMS:

  • Table: A table holds the data in rows and columns. Each table represents a specific entity, such as customers, products, or orders.
  • Row: A record or instance of data within a table. Each row corresponds to a unique entry or record, and it contains values for each column. A row is also called a tuple.
  • Column: A column is a field or property representing a specific aspect of the data. Columns define the characteristics of the data stored in a table. Columns may also be called attributes.
  • Key: A key is a column or set of columns that uniquely identifies each row in a table. Primary keys are used to ensure data integrity and facilitate efficient data retrieval.
  • Relationships: Connections between tables based on common keys. These relationships define how data in one table is related to data in another, enabling the establishment of links between different entities.
  • Normalization: The process of organizing data to eliminate redundancy and dependency, ensuring efficient storage and minimizing data update anomalies.
  • SQL (Structured Query Language): A standard language for managing and querying relational databases. SQL provides a set of commands for creating, modifying, and querying data in relational databases.

Areas Relational Databases Shine

Every database has its strengths. The following are some areas where RDBMS shines:

  • Data Integrity: Strict rules ensure data consistency and accuracy, crucial for financial transactions and other sensitive information. ACID properties ensure data integrity and consistency, which is critical for applications where accurate and reliable data management is paramount, such as financial systems or healthcare databases.
  • Complex Queries: Powerful SQL language allows for sophisticated queries involving multiple tables and relationships.
  • Standardization: Widely used and understood, making it easier to find and hire skilled database administrators.
  • Transactional Processing: Relational databases are designed for transactional processing, ensuring that database transactions are either completed entirely or not at all. This is crucial in applications where maintaining data consistency is critical.
  • Normalization: Relational databases support normalization, a process that minimizes data redundancy and ensures that data is stored efficiently. This is advantageous in scenarios where maintaining data integrity and avoiding anomalies is a priority.
  • Security: Many relational databases provide robust security features, including user authentication, authorization, and encryption. This makes them suitable for applications with stringent security requirements.
  • Mature Ecosystem: Relational databases have a mature and well-established ecosystem with a wide range of tools, libraries, and frameworks. This makes it easier for developers to work with relational databases and integrate them into various applications.
  • ACID Transactions: ACID properties ensure the reliability of transactions by providing guarantees around Atomicity, Consistency, Isolation, and Durability. This is crucial for applications where the accuracy of transactions is non-negotiable.
  • Ad Hoc Querying: Relational databases allow for ad hoc querying, where users can interactively query the database using SQL to retrieve specific information. This flexibility is valuable in various analytical and reporting scenarios.
  • Consistency in Data Updates: Relational databases ensure consistency during updates, deletions, and insertions, preventing scenarios where data becomes inconsistent due to failed operations.

Weaknesses of Relational Databases

  • Scalability challenges: Large data may result in horizontal scaling challenges, requiring expensive hardware upgrades. Scaling up to handle large amounts of data can be challenging.
  • Schema rigidity: RDBMS have fixed schema. This may not adapt well to applications with evolving data. If your application needs to handle unstructured or semi-structured data, relational databases are not the right fit for you.
  • Performance limitations: RDBMS can be slow when queries become complex and when data joins involve large tables.
  • Cost and overhead. Relational databases can be costly with significant overhead associated with ensuring data integrity and maintaining indexes in high transaction environments
  • Complexity: Unlike non-relational databases that may not require any existing knowledge of databases, relational databases require a substantial knowledge of SQL to handle complex tasks. This can be challenging for people new to databases.

What Are Non-Relational Databases?

Non-relational or NoSQL databases break free from the rigidity of relational databases. It is simple to set up and use, requiring little to no existing database knowledge. It also comes in different types, like document databases, key-value stores, and graph databases.

NoSQL databases are more flexible and provide a scalable approach to managing large volumes of unstructured or semi-structured data. They are cost-efficient, more scalable than RDBMS, and can process big data in real time. SinglebaseCloud is a database provider that offers unlimited NoSQL document databases that are fast and scalable.

Strengths of Non-Relational Databases

The following are areas where non-relational databases shine:

  • Scalability: Non-relational databases are often more scalable than relational databases. They can easily handle large amounts of schema-less data and scale horizontally by adding more servers to a distributed system.
  • Data flexibility: Non-relational databases are schema-less or have a dynamic schema, allowing for flexibility in the data model. This is particularly useful when dealing with data that doesn’t have a fixed structure, and the schema can evolve.
  • Variety of Data Types: If your application deals with diverse data types (e.g., documents, key-value pairs, graphs), a non-relational database may be a better fit. Each type of NoSQL database is designed to handle specific data models efficiently.
  • Rapid Development and Agile Methodologies: NoSQL databases are often chosen for projects with rapidly changing requirements where it’s essential to adapt quickly. They allow developers to add or modify fields without the need to change the entire database schema.
  • Big Data and Real-time Processing: NoSQL databases are commonly used in scenarios where real-time data processing and analysis are crucial, such as in big data applications or systems that require low-latency responses.
  • Hierarchical or Graph Data: NoSQL databases, especially graph databases, are well-suited for scenarios where relationships between data entities are complex and interconnected, such as in social networks or recommendation systems.
  • Aggregation and Analytics: NoSQL databases are often employed in scenarios where the primary use case involves aggregating and analyzing large datasets, as they can provide efficient ways to perform these operations.
  • Cost and Resource Efficiency: In certain cases, NoSQL databases can be more cost-effective in terms of both hardware and licensing fees, making them an attractive option for organizations with budget constraints.

Weaknesses of Non-Relational Database

  • Data integrity: It can be trickier to ensure data consistency due to looser structure. Compared to the strict schema of RDBMS, NoSQL’s flexible structure leaves room for inconsistencies and errors. Ensuring data consistency across distributed servers and different data types can be challenging
  • Complex queries: NoSQL languages might not be as powerful as SQL for intricate queries across multiple data types. While NoSQL languages exist for querying specific data types, they generally lack the expressiveness and versatility of SQL. Complex queries spanning different data structures or involving joins can be cumbersome and inefficient.
  • Limited standardization: Expertise in specific NoSQL variants may be harder to find than SQL skills.


What database to choose depends on your specific needs. Relational databases will be a better choice for applications that involve structured data with complex relationships and ACID requirements. Applications that deal with large unstructured data require high scalability therefore a NoSQL database is a better choice.

Ultimately, the best approach is often a hybrid, combining the strengths of both RDBMS and NoSQL based on the specific needs of your application. Singlebase is a NoSQL document database that combines the strengths of RDBMS and NoSQL.

SinglebaseCloud combines the scalability of NoSQL and the relatability & familiarity of SQL, allowing you to store and manage large data efficiently. It lets you store schema-less data, perform advanced queries and indexes like joins, and retrieve data using REST, GraphQL, or SQL.