NoSQL vs SQL: What's the difference and how to choose one?

By Indeed Editorial Team

Published 4 June 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

More businesses than ever are harnessing the power of data collection to improve decision-making and streamline their day-to-day operations. One of the biggest challenges for these organisations is choosing the right platforms for data storage, delivery and application. Traditionally, most companies chose SQL databases due to their strength in data protection and integrity, but as cloud-based technologies and data have grown in popularity, many organisations are opting for NoSQL databases due to their strength in handling unstructured data. In this article, we examine NoSQL vs. SQL databases, their differences and how to choose between them.

NoSQL vs SQL: definitions

To make an informed decision about choosing NoSQL vs. SQL databases, it's essential that you know what they are. Below is a summary of these two database tools:

What is SQL?

SQL, also known as structured query language, is a specialised programming language used to access and manipulate data sets. It's a type of query language that allows users to retrieve data sets from databases and interact with relational databases. It's not a general-purpose programming language, but rather it has a specific purpose which focuses on relational database manipulation and management.

What is NoSQL?

NoSQL, also known as ‘not only SQL', is a type of non-relational, distributed database. These are typically non-tabular databases that store data in a different way than relational tables. There are many different types of NoSQL databases depending on the data model used. Common examples include document, key-value and graph types. A NoSQL database has a flexible schema that scales easily when using large data sets or high user loads.

Related: How to use data consolidation in Excel: a complete guide

What is a relational database?

A relational database is a specific type of database that allows for the recognition and access of data concerning other data sets within the same database. It stores similar data over multiple tables in a database, allowing users to query data from multiple tables at once. Relational databases use the relational model of data to function and require a relational database management system to work. SQL is most often used to manage and interact with relational databases as it permits communication with data sets in a relational database management system.

Related: What is a database query? (Plus methods and types)

NoSQL vs SQL: the significant differences

SQL and NoSQL have several major differences that are worth knowing about. SQL is a specialised programming language that allows you to interact with relational databases. NoSQL is a type of database management that's non-relational and, more often than not, doesn't use the SQL programming language. There are five main areas where the differences between SQL and NoSQL are most prominent, which include:

1. Language

SQL is a well-used language that's been with us for over 40 years at this point. As a result, it's well-documented and clearly recognisable. This makes it a safe choice and a flexible language to use, especially when making complex queries in databases. It's restrictive, though, as it forces users to work from the predefined schema and structures data in a specific way before use. NoSQL has a more versatile schema that allows for alternative structures, promoting improved flexibility. Planning is less important due to the varied syntax available.

NoSQL is limited by missing the standard interface that SQL has, which can make complex queries harder to complete. SQL features a number of different dialects, but they all use the same grammar and syntax. This improves fluency across other languages used in relational databases. NoSQL, though, doesn't offer that level of fluency or consistency because it focuses on working across unrelated technologies. A defining feature for many NoSQL databases is the unique language used due to constraints from custom structures or capabilities.

Related: What is data mapping, and why is it important to business?

2. Scalability

Generally speaking, SQL databases have the potential for vertical scalability through an increase in processing power from hardware. NoSQL databases, though, work through a master-slave framework that benefits from horizontal scalability using servers. You can scale SQL databases horizontally, but it's generally not well-supported. Similarly, NoSQL has the potential for vertical scalability, but the most common approach is horizontal. It's also worth noting that the savings gained from efficient data structures may overtake the differences in scalability, so the final decision usually depends on use cases.

3. Structure

SQL database schema represents relational data that uses rules that focus on consistency and data integrity. This is usually structured with tables that feature columns and rows, with keys to add constraints to data relationships. NoSQL doesn't rely on this type of structure but instead tends to align with one of the following categories:

  • Column-oriented databases that transpose row-oriented relational database management systems, which prove efficient storage for high-dimensional data.

  • Key-value stores, which are dictionaries with access to various objects that have individual keys.

  • Document stores that have semi-structured data and objects that hold related information. These document stores can be completely unrelated to each other.

  • Graph databases that bring in the idea of relationships between objects and documents. This makes it easy to manipulate and manage large, connected data sets.

4. Properties

SQL and NoSQL operate using separate rules when it comes to resolving transactions. Relational database management systems have four properties, known collectively as ACID:

  • Atomicity: All transactions succeed or fail. There's no partial completion, even if the system fails.

  • Consistency: Each database step uses invariants, which are rules that stop corruption and validate data.

  • Isolation: This stops transactions from impacting each other as they act as if they're running sequentially, even when running in parallel.

  • Durability: Ensures that transactions are final, even if a system failure occurs.

NoSQL doesn't use the ACID protocol. Instead, it relies on the CAP theorem that ensures that only two of the following properties occur simultaneously:

  • Consistency: All requests receive the most recent result available or display an error.

  • Availability: All requests produce a non-error result, even if the result isn't recent.

  • Partition tolerance: If there's a loss between nodes, system operations aren't interrupted.

Related: What is business intelligence? (Definition, tools and FAQs)

5. Support

SQL has been around for decades, and as a result, a thriving community has grown around it. This has helped generate stable codebases and standard practices for use. There are many available examples and competent experts willing to support new users of SQL and relational database programming. SQL is also available for most platforms, including operating systems and other programming languages.

NoSQL isn't nearly as compatible, which means dependencies are more difficult to source. NoSQL is a technology that's being adopted quickly, but it doesn't have the decades of work behind it that SQL does. As a result, communities around NoSQL are less populated and cohesive. It does benefit from the use of open systems, though, and there's a collective effort to help with onboarding new users.

How to decide between SQL and NoSQL databases

Making the right choice between SQL and NoSQL databases depends on the work you're doing, the amount of data used and the overall structure of the data. There are also other aspects to consider, such as the database products available and their cost, maturity and stability. To help you make the right choice, follow the guidelines below to find the right option for your circumstances:

When you may choose SQL

SQL is most effective when working on smaller datasets that are tabular in nature. It's also most appropriate when systems rely on consistency. As a result, it's most often used in areas such as accounting systems or transactional systems for e-commerce. You may consider SQL in the following circumstances:

  • when you're using highly structured data sets with no change in data frequency

  • when you're supporting a transaction-oriented system that has financial applications

  • when data integrity and security are a top priority

  • when you require complex queries such as ad hoc requests

  • when you don't need scale-out capabilities

Related: What is agile project management? (Everything you need to know)

When you may choose NoSQL

NoSQL is generally preferred when working on datasets that use graphs or hierarchical data. It's also useful when large data sets are prone to change. Due to this, businesses that grow quickly without data schemata benefit greatly from NoSQL. It's often used for online content management and mobile applications. You might want to consider NoSQL for the following situations:

  • when working with lots of unstructured or semi-structured data sets that don't work in relational models

  • when you require flexible, dynamic schemata or want a choice of data models

  • when you require horizontal scalability, such as across different regions

  • when streamlined development is important

  • when data integrity isn't essential for your databases

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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