When it comes to differentiating between relational vs. non-relational databases, not all people will catch the nuances that set them apart. It is extremely difficult to transition your data from one type of database to another one development starts, so not understanding the nuances between the two can pose a huge challenge later on. Understanding the critical differences between a relational and non-relational database will help you select the correct type of database for your project.
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In this blog post, we'll go through what relational and non-relational databases are, their respective advantages, disadvantages, and ideal use cases, followed by a few key differences that you should be mindful of when choosing which one to use.
With that said, let's dive right in!
What is a Database
A database is a set of data held in a computer that can be accessible in various manners. There are two main types of databases, relational & non-relational. While relational & non-relational are the two main types, there are an additional two types namely, an in-memory database and a cloud-based database. An in-memory database stores data in a computers main memory, the RAM. While a cloud-based database stores data on a hosted cloud service provider.
With that said, you can store your relational or non-relational data on either an in-memory database or a cloud-based database. Think of relational and non-relational as the form your data takes and in-memory or cloud-based as the manner in which you store that data.
A relational database is structured, meaning the data is organized in tables. Many times, the data within these tables have relationships with one another or dependencies. A non-relational database is document-oriented, meaning all information gets stored in more of a laundry list order. You will have all of your data listed within a single construct or document.
What is a Relational Database (SQL Database)
Relational databases organize data into tables with rows and columns, where each row represents a record, and each column represents a field. This structure allows for easy querying and data manipulation using Structured Query Language (SQL).
Since relational databases are the more traditional way to store data in a tabular form, most people are familiar with their use cases, making them the most popular option amongst developers.
The table itself would be made up of one variable or object we would be looking through. The column represents the data point that needs to be stored, and the row records the data points per column.
For example, if you are looking to sort data regarding what the weather is at a specific time of the day during a specific day, it would be structured as the following:
- Table: Weather
- Columns: Days of the Week
- Rows: Time of Day
- Data Points: Degrees Fahrenheit
In this structure, all queries would be related to this table, and the table's structure would allow for easy sorting, filtering, computations, etc.
If we ever need to establish a connection between tables, say, you want to know what the weather was at a certain time and relate that to a baseball game's predicted score. What we do is create what is called a key. This key allows for connections between two or more tables to solidify associations between the two.
Some popular SQL database systems include:
- Oracle
- Microsoft SQL Server
- PostgreSQL
- MySQL
- MariaDB
What is a Non-Relational Database (NoSQL database)
A non-relational database, also known as a NoSQL database, is a type of database that does not use the traditional table-based relational structure. Instead, non-relational databases use various data models, such as key-value, document, column-family, and graph.
This allows for greater flexibility in storing and managing data, especially for large-scale, distributed, and unstructured data. To break it down, let's go through the types of non-relational data models one by one.
- Key-value: This data model comprises two parts: a key and a value. The key is like an index, used to look up and access the value containing the data stored in the database.
- Document: Documents are self-contained, meaning all the information related to a single record is stored within one document. This makes it easier to add or modify data as needed.
- Column family: Column family databases store data in columns
- Graph: Graph databases use nodes and edges to represent relationships between different data sets.
Some popular NoSQL databases include:
- MongoDB
- Google Cloud Firestore
- Cassandra
- Redis
- Apache HBase
- Amazon DynamoDB
In contrast to a relational database, a NoSQL database is one that is less structured/confined in format and, thus, allows for more flexibility and adaptability. If you are going to be dealing with a dataset that isn't clearly defined, meaning not organized or structured, you likely won't have the luxury of establishing defined tables and relationships amongst the dataset.
For example, Facebook Messenger uses a NoSQL database because the information that is being gathered isn't structured enough to be segmented into tables and define relationships between each other. With tons of unstructured information, it needs to be held in a non-relational database.
Think of the information as being stored on one large Word document. Everything is there. As more information gets entered, the document gets longer. If you want to find and pull data, you have to, in essence, 'control/command + F' and search for the data itself.
Key Differences Between Relational and Non-Relational Databases
Having a high-level understanding of what each type of database is, what their use cases are, and how they stack up against each other in a pros and cons list is important. But to really understand the key differences between relational and non-relational databases, it's important to look at how they differ in terms of structure, performance, scalability, schema, and development:
Data Structure
A data structure is the way data is organized and stored in a database. Relational databases use a table-based structure with rows and columns, while non-relational databases use various data models, such as key-value, document, column-family, and graph.
Performance
Relational databases can provide strong data consistency and integrity but may be slower in performance for certain use cases. Non-relational databases can offer faster performance for specific use cases, such as big data and real-time processing.
Scalability
Relational databases have limited scalability, making them less suitable for large datasets and high read/write loads. Non-relational databases are highly scalable and can handle large-scale, distributed data more efficiently.
Query Language
Relational databases use SQL for querying and manipulating data, while non-relational databases typically use their own query languages or APIs, which can vary between different databases.
Schema
Relational databases have a predefined schema which makes them better suited for structured data. Non-relational databases, however, are more flexible and can accommodate various types of data.
Development
Relational databases require more development effort when it comes to creating complex queries or changing the database structure. On the other hand, non-relational databases are easier to develop and require fewer resources.
Pros and Cons of Relational and Non-Relational Databases
Relational and non-relational databases both have their pros and cons, depending on what type of application you're building and which one would be best suited for the task. Before you decide on which kind of database to use, it's essential to ask yourself a few questions. Now we answer the question you're really looking for.
Which type of database should you use? Well, there are some questions you should ask yourself that are outlined below. If you answer yes to the relational questions, then use a SQL.
database. If you answer yes to the non-relational questions, then use a NoSQL database:
Relational database questions:
- Does my data have relationships?
- Do I want to use JOINs to query the data, and is it necessary?
- Does my data need ACID transactions?
- Will I require complex queries on large datasets?
Non-relational database questions:
- Does my data have a hierarchical structure?
- Do I need to update parts of the data without overwriting other parts?
- Will I need to access large amounts of data quickly and efficiently?
- Am I dealing with unstructured or semi-structured data?
No matter which type of database you use, it's important to do your research and weigh the pros and cons before making a decision. Thankfully, most databases are open source or have free tiers that you can test out. So take some time to explore different database options and see which one works best for your application.
Pros of a Relational Database
- Data is easily structured into categories.
- Your data is consistent in input and meaning and easy to navigate.
- Relationships can be easily defined between data points.
- Uses Structured Query Language (SQL), making it easy to query and update data.
Cons of a Relational Database
- Indexes can take up a lot of space.
- Scaling is more difficult as the data size grows.
- Updates to the schema are time-consuming and complicated.
Pros of a Non-Relational Database
- Data is not confined to a structured group.
- You can perform functions that allow for greater flexibility.
- Your data and analysis can be more dynamic and allow for more variant inputs.
Cons of a Non-Relational Database
- It can be difficult to query the data as there is no "universal" language.
- Data may not be consistent, meaning it could have different values for one key.
- Data integrity can suffer if not managed correctly.
The primary difference between a relational and non-relational database is the structure in which data is organized. A relational database structures data in rows and columns, making it easier to mine data from existing tables.
Non-relational databases organize data in documents, collections, and graphs, allowing for a more flexible data model with the ability to store complex structures. While both types of databases can be used to store and manage similar types of data, the structure and use case is different.
Use Cases For Relational Databases
Relational databases are a great choice for applications that require complex queries, transactions, and the ability to store large amounts of data. Examples include banking systems, inventory management systems, and customer relationship management (CRM) solutions.
Use Cases For Non-Relational Databases
Non-relational databases are great for applications that require flexibility, scalability, and the ability to store complex data structures. Examples include gaming applications, social media platforms, and web-scale systems such as search engines.
How To Start Building A Database
Once you've decided on the type of database you need, you can start building it. Depending on the complexity of your application, you may consider working with a software development outsourcing consultant to help you plan, strategize, and develop the database for your use case. For smaller projects, however, there are plenty of open-source options available for free. So, let's get to the steps you should take to get your project started:
Step 1: Choose a Database Platform
The first step is to choose the database platform that will best fit your needs. You can use relational databases like MySQL or PostgreSQL or non-relational databases such as MongoDB, Redis, Cassandra, and Couchbase.
Step 2: Design Your Database Schema
Once you've chosen a database, it's time to design the schema. This is where you define the structure of your data and how it will be stored in the database. Make sure to plan thoroughly and think about how your application will interact with the data before committing to a particular schema design.
Step 3: Develop Your Database Logic
Once you have a schema, you can begin developing the logic that will be used to interact with the database. This includes writing queries and stored procedures and creating views, triggers, and other database objects.
Step 4: Test Your Database
Once you've developed your logic, it's important to test it thoroughly before deploying it in production. Make sure to test all scenarios and use cases to ensure that your database is working as expected.
Step 5: Deploy and Monitor Your Database
Once you've tested your logic, you can deploy it to production. Depending on the size of your application, this could involve setting up a server or cluster of servers for the database to run on.
Once your database is up and running, it's important to monitor its performance and make sure that it's performing as expected. This can help you identify any potential issues or bottlenecks before they become a problem.
Following these steps should get you well on your way to building a successful database for your application. With the right planning and development, you can create a robust and reliable database that will serve your needs.
Key Takeaway
Relational and non-relational databases each have their own unique advantages and disadvantages that make them better or worse suited for certain use cases. Understanding the key differences between relational and non-relational databases is essential when it comes to choosing the right type of database for your application.
When in doubt, consider consulting with a database specialist to help you make the right decision. To learn more about which type of database is best suited for the software development project you have in mind, reach out to [email protected] for more information. Our team of Account Executives and software developers will be happy to help you get started.
