Relational Database vs. Non-Relational Database

Relational database or non-relational database: which should you use for your projects? It’s a common question. When choosing the database type that’s right for your requirements, it’s important to understand the differences between the two.

Both database types are practical in different situations and use cases and have commonalities. Both are also widely implemented, with a number of different provider options available for businesses and developers that need to store, access, or analyze data. Below, you’ll find the necessary information you need to make an informed decision about choosing the right database for your data management needs.

What is a relational database?

All databases need the following features:

•          Capability to store multiple types – ideally all types – of data
•          Easy, fast access to the data stored within the database
•          Usefulness: does the data stored provide strategic business advantages or insights?

In other words, a database that only stores partial, inaccessible, or useless data is pointless. Business databases must be able to store and provide access to both operational and analytical data to maximize their usefulness.

Operational data means data that helps run the business’s daily operations, such as sales, stock levels, or HR information. Analytical data is usually data related to customer or client engagement with the business’s products or services. This could include information on blog traffic, product trends, or predictions based on customer buying behavior. Data is stored in its raw form in data warehouses or data lakes and becomes accessible and actionable when transferred into databases.

A relational database management system, or RDBMS, is one method for storing and providing access to this wealth of digital data. RDBMSs store data in tables. These tables often have similar information, causing relationships to form between the tables — hence the name relational database. Each table has both rows and columns, as you might expect. The data is stored in rows, and the columns define what this data is. One column has unique defining information and is called the primary key. When that key is used in another table, it’s called the foreign key, and a relationship forms between the tables.

Relational database developers and managers typically use Structured Query Language (SQL) to perform, create, read, update or delete (CRUD) operations. 

Relational databases are far more suited to operational data, as some analytical data may arrive in an unstructured format unsuitable for storage in tables.

Relational database advantages

Relational databases help protect against duplicate information. The use of primary and foreign keys builds relationships that ensure data accuracy.

Reducing duplication or replication of data reduces storage costs and should reduce the resources required to run the database.

RDBMS databases are well-established, meaning there’s plenty of support available for anyone wanting to design or use a relational database.

Relational databases are ACID compliant. ACID stands for Atomicity, Consistency, Isolation, and Durability. This is a standard by which the reliability of database transactions is measured. For example, a bad query or change request should not corrupt other data within the database; the data should be stable and unaffected by failed transactions.

Relational database disadvantages

Relational databases are not particularly scalable. As the data your business ingests grows, you may struggle to grow your database alongside the larger volumes of data you have to handle. Considering that Statista predicts the world will produce 181 zettabytes of data by 2025, a lack of scalability could become a genuine limitation for businesses that want to remain agile as they grow.

Relational databases also lack flexibility. By definition, relational databases follow a rigid scheme based solely on columns and tables. This provides both advantages and disadvantages. Ultimately, it means that once the database has been created around your desired design, there’s no way to make changes later without taking the database offline and adjusting all the data to match the new criteria.

As a relational database grows, its performance slows. This means highly complex databases with numerous tables can take a long time to perform queries, slowing down the rate of useful business insights.

What is a non-relational database?

Non-relational databases are any database type that doesn’t use a relational database’s structured, relationship-focused data management style. Non-relational databases are not limited to tables, columns, and rows. This means they can handle unstructured data that doesn’t follow any particular schema. Unstructured data may include replies to automated email campaigns or text messages. There are no set parameters for this data, and often businesses will need to use business intelligence (BI) tools to sift through this unstructured data, seeking out patterns that can lead to business-critical insights and forecasts.

A table with set definitions about how data should appear and be presented is of no use for unstructured data. A non-relational database provides an alternative that supports data that follows no fixed schema.

There are multiple types of non-relational databases, but here are the pros and cons of the overall concept.

Non-relational database advantages

Non-relational databases are better suited to the cloud environment. This type of database can deal with many types of data, including data from devices across the Internet of Things (IoT) and a multitude of SaaS and apps. This allows developers to manage vastly disparate systems or applications with ease.

Scalability becomes much simpler with a non-relational database. This method of storing data is ideally suited for larger volumes of data and not limited by data type.

Because non-relational databases can handle larger and more complex forms of data, they perform better, faster, and provide more real-time insights for businesses when combined with appropriate BI tools or expert data managers.

Non-relational database disadvantages

Reliability is not as guaranteed with a non-relational database. There may be instances when adjusting data causes problems with other entries. To prevent this, developers may want to custom-code their own contingencies, making non-relational database creation slightly more complex.

An essential point concerning non-relational databases is that they’re not ACID compliant.

Finally, there is less support available for non-relational databases simply because they haven’t been around as long. The developer community is still growing, so it may seem like a tougher job to create, run, and maintain this type of database.

What are the biggest differences?

Scalability: While you can always add more rows of data to a relational database — making it vertically scalable — the more columns or tables you add, the worse it performs. Non-relational databases can be far more complex with a much lower impact on performance.

Reliability: Relational databases comply with industry standards of reliability (ACID). Non-relational databases have no such guarantees, prompting programmers to develop their own code to provide reliability.

The biggest difference between relational and non-relational databases is the way data is structured. Data in relational databases must always match the predefined structure of the column in the table. For example, you couldn’t put someone’s name in a telephone number column. The table wouldn’t accept it.

Conversely, non-relational databases fetch and present data in a multitude of ways. Let’s explore that more in the next section.

Architecture for relational databases and non-relational databases

Relational databases contain data, metadata (data about the data), plus a compiler to convert SQL queries so the database can understand the query and provide the required information. Data is always structured in tables built from columns and rows.

In a typical RDBMS architecture, queries may come from the database administrator, a data analyst, or an application programmer.

Queries may travel through a query compiler or an application program compiler. The RDBSM will have query optimizers that convert the query and run it through the RDBMS runtime system. This part of the database executes the queries or commands from other apps and fetches data accordingly.

There will also be a log that records what queries have taken place and any issues such as transaction failures or system shutdowns. This allows data managers to understand how the database is being used and address any reliability issues.

Finally, a typical RDBSM will have a recovery manager built in to ensure reliability after a failure.

Non-relational database architecture varies as there are several types. This is why they’re also called NoSQL databases, where NoSQL means Not Only SQL — or not only fixed schema and criteria.

The most basic NoSQL database is the key-value database. Data keys are paired with data values — the entries within the database. Each data value can only be accessed with a specific key that relates to that data point. This allows fast access to data, but limits the complexity of data that can be stored.

Wide-column databases are essentially a more flexible version of a relational database. They also follow the standard table with columns and rows format. Unlike relational database structure, however, each column can hold a different type of data. They can store all kinds of data, but it can slow them down when it’s time to fetch it.

Document databases are possibly the most flexible database architecture. Data is stored as JSON-like documents that can handle multiple types of data. Strings, numbers, arrays, and nested documents can all live in a document database. A single document in this type of database could hold all of a customer’s data, making it simple and fast to retrieve that information. Query APIs can fetch this data, detailing what criteria the data should be filtered by and what fields the data analyst needs to see once the data is retrieved.

Data in a document database is highly organized, easy to view, and available. There’s no reason you can’t view the same data across multiple servers, which helps break down or even prevent data silos within organizations, and makes app development far more agile.

Which type of database is MongoDB/NoSQL?

MongoDB is a cloud-based database-as-a-service designed to connect to other cloud services like AWS, Google Cloud, Azure, and other services used by businesses. There’s a high focus on data security, object-based development, and workload isolation. But is MongoDB relational or non-relational?

MongoDB is a non-relational database that’s highly scalable. It’s designed for enterprises that need to store huge volumes of data, which is easier with non-relational database architecture. MongoDB is a NoSQL database, because data is not solely stored or fetched in tables. Specifically, MongoDB is a document database that enables enterprises to store virtually unlimited forms of data.

Relational database vs. non-relational database: What type of database should I use?

Making an informed decision about the type of database to use means understanding the key differences.

In brief, both types of databases are suitable for cloud-native apps, yet both have advantages and disadvantages. Relational databases are more widely implemented and meet ACID compliance standards. However, non-relational databases are more suitable for large volumes of unstructured data, which are becoming more commonplace as the amount of data ingested by businesses grows exponentially.

Set out your goals for your database, consider your business requirements for the relevant data, and choose the type of database to use based on those needs.Whatever database you use, talk to LogicMonitor about the best ways to achieve comprehensive database monitoring and maximize the effectiveness and security of your data alongside your existing IT infrastructure.