When to use a Relational Database vs a NoSQL Database

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If you’re choosing a database for your organization, one of the key decisions you’ll be faced with is selecting either a relational (SQL) or NoSQL structure. While both types of databases have their own advantages, there are important differences between them that can influence your decision. In this article, we’ll take a closer look at both kinds of databases and discuss when to use a relational database vs a NoSQL database.

What is a Relational Database?

Let’s begin with a look at relational databases. These date back to the early 1970s, when the first prototypes were developed based on the ideas presented in a landmark paper written by an IBM research. The first commercially available relational database was Oracle, released in 1979.

A relational database is one that is made up of tables, with data organized into columns and rows within those tables. Each row has a unique key to identify it, and columns can be thought of as attributes. In a relational database, an individual table usually consists of just one type of “entity”– for example, students. Each row in the table represents an instance of that entity (such as students), and each column represents values that are attributes of that particular instance. For our student example, columns might represent name, address, phone number, grade level, and more. Data stored in a relational database is managed using a query language called SQL.

Today, some of the most widely-used relational databases include MySQL, PostgresSQL and Microsoft SQL Server. While these products have many similarities, there are differences in their architecture, and they’re not all supported by the same operating systems. Ultimately, the choice of relational database is usually driven by system and programmer requirements.

What is a NoSQL Database

While relational databases can be traced back several decades, the concept of the NoSQL database is fairly new in comparison. The NoSQL movement came about as a response to the rise of both big data and unstructured data. Relational database management systems performed less efficiently when faced with massive volumes of data, and their table-based structure with defined columns wasn’t an easy fit for less-structured data. The need for a new kind of database was pressing: According to a recent survey, 63 percent of enterprises indicated that their storage capacities reached 50 petabytes or more, and more than half of the data they stored was considered unstructured.

A NoSQL database differs from a relational database in several ways. There’s no defined schema, which means it’s easy for your database to adapt as your data and requirements change. NoSQL databases were created to handle unstructured data, so you can store data such as texts, video and social media content with ease. It’s also easier to scale a NoSQL database because they scale horizontally. Instead of having to upgrade to an expensive, beefed-up server, you can add capacity with a cluster of less-expensive servers.

There a number of NoSQL databases available, with MongoDB being the most popular and widely-known option. Other products include Redis, HBase and Cassandra.

Advantages and Disadvantages

If you’re trying to figure out whether a relational database vs a NoSQL database is the best choice for your organization, it can be helpful to compare the advantages and disadvantages of each product. Let’s start with relational databases. For the purpose of our comparison, we’ll look at MySQL and MongoDB:


  • Maturity: MySQL is a well-established technology, so you can count on stability and a strong user community.
  • Cost: MySQL is free and open source.
  • Compatibility: MySQL is compatible with all major platforms.

Disadvantages: Scalability: Because you have to scale up rather than scale out, vertical scaling often involves expensive hardware and increased downtime. Defined Schema: Tables need to be defined with their columns before data can be added to them; this makes it more difficult to deal with less-structured data as well as varying data types.

NoSQL databases such as MongoDB, in comparison, have a completely different set of strengths and weaknesses:


  • Flexibility: MongoDB’s flexible data model aligns naturally with the way data tends to be represented in applications.
  • Scalability: Horizontal scaling means that as your organization’s needs evolve, your database can grow along with it.
  • Dynamic Schema: With MongoDB, you can change your schema at any point without needing to modify any existing data.
  • Speed: For relatively simple queries, you can’t beat the speed of a NoSQL database like MongoDB.


  • Relative youth: NoSQL databases such as MongoDB simply haven’t been around as long as relational databases, so they may not have as extensive of a user community.
  • Can’t “pop in” to legacy apps: It takes a bit of work to modernize legacy apps to work with MongoDB, although most developers find the performance gains make the effort worthwhile.
  • Handling of complex queries: If your application requires particularly complex queries, SQL is better suited to handle them.

Use Cases

Looking at the advantages and disadvantages of a relational database vs a NoSQL database makes it easier to see where each one might best be used. The ability to handle large, unstructured volumes of data with ease makes NoSQL databases an attractive choice for many organizations; its scalability also draws organizations that are poised for growth. However, NoSQL databases can’t act as a “snap in” replacement for legacy apps that are designed to work with a relational database. In those cases, as well as those where especially complex queries are used, it may make more sense to stick with a SQL-based database such as MySQL.


Relational databases have been around for a long time now, and they’re proven to work well. For the management of structured, fairly predictable data, you can’t beat the widespread support of a SQL-based database. However, there’s a good reason NoSQL databases have managed to disrupt the datab ase market in recent years– their more scalable and flexible architecture makes them well-suited for many of today’s data needs. The right choice for your organization will depend entirely on your application’s requirements and the structure of your data.

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