Difference Between Relational and Non Relational Database
Developers and data teams within organizations or software development outsourcing companies typically have two major options when it comes to database management systems: relational and non relational databases. Though seemingly minor, this distinction has a hugely influential impact. Both types of databases have their unique benefits, depending on the desired end product. This makes it critical to understand the differences so you can always make the correct choice.
Let’s identify the most important differences between relational and non-relational databases, and help you settle on the best for your needs from project to project.
Also Read: Latest trends in software development outsourcing
What are relational databases?
A relational database, also called sql database, is a type of database that stores and organizes data in tables. The data in these tables are related to each other, and can be linked together using keys. This provides a flexible way to store data, and allows for easy retrieval of information. Relational databases are used in a variety of operational applications, such as customer relationship management (CRM) systems and online shopping carts. They are also the basis for many popular web applications, such as content management systems (CMS) and forums.
Relational databases work by storing data in tables. For example, you might have a table with the following columns: first_name, last_name, age. To add data to the table, you would specify the values for each column. For example, if you wanted to add John Smith to the table, you would specify «John» for the first_name column, «Smith» for the last_name column, and «22» for the age column.
To query the data in the table, you would use special SQL statements that allow you to select certain rows or columns from the table. For example, to add data to a table, you use the INSERT statement. To retrieve data from a table, you use the SELECT statement. To modify data in a table, you use the UPDATE statement. To delete data from a table, you use the DELETE statement.
Tables are similar to folders in a file system, where each table stores a collection of records. Each record, or row, in a table contains data for a single entity, such as a customer or an order. Tables are related to each other by keys. For example, a customer table might be related to an orders table by the customer's ID number (key). This relationship enables the database to retrieve information from multiple tables with a single query.
Popular relational databases
There are several relational databases in the market, and these below are some of the most common ones:
SQL Server
SQL Server is developed by Microsoft and is used to store and manage data in a central location, making it easy to access and query.
Businesses of all sizes use SQL Server thanks to the many features that make it an attractive choice, including high availability, scalability, and security. In addition, SQL Server offers a variety of tools that make it easy to administer and query data.
MySQL
MySQL is an open source relational database that runs on a variety of platforms including Windows, Mac, and Linux. It’s used by millions of websites including popular platforms such as Facebook and Google. It has a stable codebase and a large user community, making it a good choice for both small and large applications.
MySQL also offers good performance and scalability, making it suitable for both single-server and clustered deployments. Additionally, the fact that MySQL is free and open source makes it an attractive option for budget-conscious businesses.
PostgreSQL
PostgreSQL was originally developed for UNIX-like platforms, but now also runs on Windows and macOS. It also has a number of features that make it popular for web development, such as support for JSON and XML data types, powerful data search capabilities, and an extensible security framework. PostgreSQL is released under the PostgreSQL License, which is compatible with the GNU GPL.
Oracle Database
Oracle Database is produced and marketed by Oracle Corporation. It is a powerful, high-performance database that runs on enterprise servers and is used by some of the largest organizations in the world. Oracle Database can be deployed on-premises, in the cloud, or in a hybrid environment. It also offers a wide range of features and options, making it one of the most versatile databases available.
What is a non-relational database?
A non-relational database (also referred to as NoSQL database) is a type of database that does not rely on the traditional table-based relational model. Non-relational databases are often used when the data is unstructured or semi-structured, meaning that it does not fit into the rows and columns of a traditional table.
This makes them more flexible than relational databases, which are designed to work with data that is organized into tables. Non-relational databases are often used for big data applications that require real-time data access or that need to scale quickly. Because they are not bound by the same restrictions as relational databases, they can be easier to design and develop.
Types of non-relational databases
The main types of NoSQL databases include graph stores, document stores, key value stores and column stores. Like the case for relational databases, these databases can also be proprietary or open source.
Graph stores
As the name suggests, a graph database uses graphs to represent the relationships between data elements. They are designed for data that can be represented as a network of interconnected nodes, such as social networking data. The data in a graph database is represented as a set of nodes and edges, with each node representing an entity and each edge representing a relationship between two nodes. This approach makes it easy to query and update data, as well as to find new connections between data elements. As a result, graph databases are well suited for applications that require complex queries or that need to make frequent changes to data relationships.
Key value stores
In key value databases, each piece of data is stored in a record that contains a unique key and one or more values. This type of database is well suited for data that does not need to be relational, such as session information or user preferences.
Document data stores
Document stores are similar to key-value stores, but they allow each value to be a complex document. This type of database is often used for content management systems or catalogs.
Documents are similar to JSON (JavaScript Object Notation) objects, and each document can contain any number of key-value pairs. Document data stores are typically used for storing semi-structured data, such as blog posts, product catalogs, and user profiles. They are also well suited for handling data that is constantly changing, such as real-time ticker data or social media feeds.
One of the main advantages of a document data store is that it can be easily scaled to meet the needs of a growing application. Additionally, document data stores are generally more tolerant of schema changes than other types of databases. As a result, they can be a good choice for applications that are still in development and likely to experience significant changes.
Column stores
Column stores are designed for data that can be logically organized into columns, such as financial data. This type of data is typically accessed by scanning all or part of a column, rather than by searching for specific values in multiple columns. In addition, column stores are often able to compress data more effectively, making them well-suited for use in data warehouses and other storage-intensive applications.
Popular non-relational databases
These are some of the most popular non-relational databases in the market today:
Nebula Graph
Nebula Graph is a super-scalable, open source graph database. It offers lightning-fast response times and is licensed under Apache 2.0, so you can copy, modify, and redistribute it for commercial purposes without needing permission.
Plus, it boasts impressive integration capabilities and can easily be integrated with popular open-source big data frameworks like Spark GraphX and Flink.
MongoDB
MongoDB is a document-oriented database system. It is easy to use and can be deployed on-premise or in the cloud. MongoDB is also cross-platform, allowing you to run it on Windows, Linux, and MacOS. Additionally, MongoDB supports a wide variety of programming languages, including Java, Python, Node.js, and PHP.
Redis
Redis is an open source non relational database that can be used as a cache, message broker, and database. It supports data structures such as strings, hashes, lists, and sorted sets. Redis has been designed to be fast and scalable and is released under the terms of the BSD 3-Clause license.
Google Cloud Firestore
Google Cloud Firestore is a document database that simplifies storing, syncing, and querying data for your mobile and web apps at global scale. It offers seamless integration with other Google Cloud products and services, such as Cloud Functions and BigQuery. With its real-time capabilities, Firestore enables you to build responsive applications that give users a rich experience. You can also combine it with the power of Machine Learning to gain valuable insights from data.
Amazon DynamoDB
Amazon DynamoDB is a key-value store type of NoSQL database. Each record in the database is identified by a unique key, and records can be accessed by retrieving their associated key.
DynamoDB lets you offload the administrative burdens of operating and scaling a distributed database, so that you don't have to worry about hardware provisioning, setup and configuration, replication, software patching, or cluster scaling. DynamoDB automatically scales your database throughput up or down in response to actual traffic patterns, without requiring any manual tuning.
The main differences between relational and non relational database
The main difference between a relational and a non relational database lies in the way that data is stored. A relational database stores data in structured tables with rows and columns. Each row represents a single record, and each column represents a field in that record. Non relational databases, on the other hand, typically use a key-value store, document store, column store, or graph store. Please refer above under types of NoSQL databases.
Summary of the differences between relational and non relational databases
| Item | Relational | Non-relational |
|---|---|---|
Data storage | Data is stored in tables, with each table containing multiple rows and columns. | Data is stored in a more flexible format, often as key-value pairs, documents, columns or graphs. |
Relationships management | Relationships are defined using foreign keys, which link one table to another | Relationships are denoted using nodes and edges. |
Query language | Relational databases use structured query language (SQL) | Non relational databases can use a variety of different query languages, depending on the particular database system being used. |
Use cases | Suited for for operational, transaction processing and data warehousing applications | Suited for real-time applications and big data analytics. |
Set up | Tend to be complex to set up and maintain | Simpler to set up and use |
Scaling | Typically scale vertically | Can scale either vertically or horizontally |
When to use relational databases vs non relational databases?
The greatest determinant of which database to use between relational and non relational is what you want to achieve with the data, also known as the use case. Non relational databases are best suited for projects that require deep analysis to reveal patterns that can power intelligence, such as social networks and fraud detection. Relational databases, on the other hand, are more effective for projects that require high-level data arrangement for operational functions, such as enterprise resource planning, cart management, accounting, etc.
Use the criteria below to assist your decision making;
| Criteria | Need | Best Choice | |
|---|---|---|---|
| Relational database | Non relational database | ||
Data | Working with data that needs to be highly structured and related? | ✓ | ✗ |
Speed | Working with data that requires fast querying? | ✗ | ✓ |
Use case | Operational applications | ✓ | ✗ |
Intelligence-driven applications | ✗ | ✓ | |
Conclusion
While the relational model has been the standard for a long time, the non-relational model has gained popularity in recent years as organizations discover that they can do more with the massive data that they have. The structure of storing data in a relational database is inflexible and can limit what you can do with the data, especially when you want to create business value out of big data.
In contrast, non-relational databases store data as a collection of objects, which can be easily queried and updated. This flexibility makes non-relational databases well suited for applications that need to store extremely large amounts of data. Additionally, non-relational databases can be easily scaled by adding more servers, making them ideal for dynamic applications with high traffic volume.
When making your choice, don't forget to factor in cost. The type of database you go for can significantly affect the app development cost. You want to make sure that you are getting good value for money. This does not necessarily mean that you blindly choose a database system that is cheaper. Instead, you want to make sure that the cost is commensurate with the value that the end product brings i.e Return on Investment.