Difference Between Relational and Non Relational Database
Database management tools play a vital role in helping organizations to work with data. Without these tools, it would be difficult to store, retrieve, and update data in a way that is both efficient and effective. Database management tools help to automate many of the processes involved in working with data, making it possible for organizations to manage large volumes of data without incurring a lot of overhead costs. In addition, database management tools can help to improve the accuracy of data by providing a central repository for storing and maintaining data.
The significance of databases has become much more pronounced now than ever before, as data is now considered the new oil that is driving powerful innovations. Organizations are aggressively using data in smart ways never imagined before. From developing new products to recommendations and fraud detection, the true value of data is just starting to take shape. But in order to make meaningful use of data, organizations must get their data management right and this is where database management systems play a critical role. It’s no wonder then that the global Database Management Systems (DBMS) market is expected to hit 142.7 trillion US dollars by 2027.
There are two broad categories of databases: relational and non- relational databases. The differences between these two are quite significant depending on what you want to achieve, and it’s critical to get it right. Let’s look at the differences and why it matters:
What is a non-relational database?
A non-relational database is a database that doesn't rely on the relationships between tables to store and retrieve data. Non-relational databases are often called NoSQL databases because they don't use Structured Query Language (SQL) to interact with data. Instead, they use a variety of methods to store and access data, such as graph theory, document-oriented storage, and key-value pairs.
Non-relational databases are faster because queries don’t need to scan multiple tables to deliver results. This makes them best suited for applications that handle different data types and store data with frequent updates or changes. Therefore, they support applications that need dynamic databases that can handle massive, complex, and unstructured data.
Examples of non-Relational databases
MongoDB: This is a popular non-relational database, especially for startups. It is a document-oriented database that uses dynamic schemas with JSON-like documents instead of tables. Furthermore, it’s open-source with great customer service.
NebulaGraph: NebulaGraph is a highly performant open source graph database that is perfectly built for the sheer volume of enterprise-grade data processing. It's horizontally scalable, compatible with openCypher, delivers high availability even in the midst of failures, and is widely used in production environments by leading internet companies. Whether you are a scientist, architect or DevOps & DBA, NebulaGraph will suit your needs.
Apache’s Cassandra DB: This NoSQL database originated from Facebook. It is an excellent option for storing and organizing huge amounts of unstructured data. Apache’s Cassandra DB is ideal for rapidly growing applications because it’s efficient at scaling up.
HBase: HBase is an Apache product and is an open-source NoSQL database. This database uses Java and offers table-like capabilities.
What is a relational database?
A relational database is simply a type of database in which data is organized into tables. The tables are then linked together by relationships, which are defined as either one-to-one, one-to-many, or many-to-many. Tables are similar to folders in a file system, where each table stores a collection of information.
Relational databases use Structured Query Language (SQL) to store structured data. So a typical relational database has tables consisting of rows and columns. New tables are added to store new data. Alternatively, you can add more rows and columns to the existing table and create relations between two or more tables. A relational database is suitable when data accuracy is vital, and the data set doesn't change often.
Examples of relational databases
Oracle: This is an object-relational database that uses the C++ programming language. It is one of the best full-service options that offers reliability and excellent customer support.
Amazon RDS: This is an SQL database solution that makes it easy to deploy, manage, and scale SQL servers in the cloud.
MYSQL: MySQL is a powerful open source relational database management system that is built on a transaction-safe, updatable view model, making it a great choice for powering web applications. MySQL is used by some of the biggest websites in the world and is indeed one of the most popular RDBMSs in the world.
IBM Db2: IBM Db2 is a relational database management system (RDBMS) developed by IBM. Some of its features include data compression, columnar storage, and row-level security. Db2 can be used to create data warehouses, process analytics workloads, and power business intelligence applications.
Pros and Cons of Relational Databases
Both relational and non relational databases definitely have their respective strengths and weaknesses. Here are the pros and cons for each:
Pros of relational databases
- Simplicity: Relational databases have the simplest model. Neither the structure nor querying process is complex and that is why simple SQL queries are sufficient. Furthermore, most developers and administrators are already familiar with the basics of working with data in a spreadsheet, so using a relational database is usually not much of a learning curve.
- Accuracy: There is a high level of accuracy in relational databases as the tables can be linked to each other via primary key, which eliminates repetitiveness and the likelihood of data duplication.
- Convenient data access: Data access in a relational database is not dependent on any pathway. This means anyone with access can easily query any table, combine tables, and modify the resulting data based on any column values.
- Security: Since data is arranged in tables, it's possible to accomplish segregation and give different user privileges for different tables. This means that some users may not be able to perform certain functions, thus enhancing the security of the database.
- Large community: Since relational databases have enjoyed widespread use for such a long period of time, it means that users can easily find ready help. So many people understand relational databases.
Cons of relational databases
- Maintenance issues: As the amount of data in the database increases, the task of keeping it organized and up-to-date becomes more and more time-consuming. In some cases, developers have to spend hours every day just on maintenance.
- Costs: The initial cost of the software and hiring professional technicians can be quite pricey, especially for smaller businesses.
- Scalability: The more servers you add, the more complicated your database gets and the less scalable it becomes. This can lead to data shortages and long load times. As your database grows, you'll experience latency and availability problems that will affect its overall performance.
- Speed: If your relational database has a lot of tables and data, it can become slower due to its complexity. This can lead to slow response times over queries or even complete failure depending on how many people are logged into the server at a given time.
- Memory: A relational database takes up a lot of physical memory because each operation depends on separate storage. The requirements for physical memory may go up over time if the amount of data increases.
- Vulnerability: Relational databases can be vulnerable to SQL injection attacks because they use a static structure that is not very good at validating user input. This means that any input that is not properly sanitized can be executed as SQL code.
- Difficult to represent complex relationships: It can be hard to represent complex relationships between objects in relational databases. This can be a problem, especially since many applications these days need more than one table to store all the data they require.
Pros of non-relational databases
- Flexibility: Non-relational databases are much more flexible as they can easily manage both structured and unstructured data.They can support a wide range of popular data formats including JSON files and Big Data formats This makes them ideal for applications that need to deal with a lot of different types of data.
- Scalability: The scale-out architecture of non-relational databases provides a feasible path to scalability whenever there is an increase in data volume or traffic. When a database needs to scale up, additional nodes can be added to the cluster and this allows databases to start small and grow as needed. So if your organization wants to store twice as much data, you can simply add more nodes to the NoSQL database without having to redesign the entire system.
- Superior performance: Non-relational databases are designed to provide high performance in terms of both throughput and latency. Throughput is a measure of overall performance, while latency is the delay between request and actual response.
- Open source: Some of the best non-relational databases are often open-source. The greatest advantage of an open source graph database is that it may not require costly licensing fees. In addition, they can typically run on affordable hardware, making them inexpensive to deploy.
- Less code: Non-relational databases require only a few lines of code. For example, data can be stored in native formats, meaning developers need not write code to adapt the data to the store. The storage is quite straightforward and this makes it very easy to understand. Developers can easily change the data structure, add new types and so on.
- Less maintenance needs: Non-relational databases can easily replicate and partition data across nodes. This reduces the need for demanding administration which is often costly.
Cons of non-relational databases
- No clear standards: There is no clear standard that defines rules and roles for non-relational databases. The design and query languages, for example, vary widely between different options, which can make it difficult to choose the right database for a project.
- Backup challenges: Most of the backup tools for many non-relational databases are not properly effective to guarantee efficient backup.
- Not good for general use cases: Non-relational databases are largely suitable for highly specialized and complex use cases and not necessarily for general applications. This means that you may not be able to comfortably use non-relational databases for a wide range of everyday needs. So if you have many different types of uses for your data, you'll need different databases and model types.
- Querying difficulties: Non-relational databases can be more difficult to query than relational databases. This is because the data is not organized in a rigid, predefined structure. As a result, it can be more difficult to write queries that extract the desired information.
- Maturity: Non-relational databases started to become popular just recently and this means they are yet to reach peak maturity in the market. This means sometimes developers can face challenges finding sufficient help including documentation, online support, and tutorials.
What are the key differences between relational and non-relational databases?
The fundamental difference between relational and non-relational databases has something to do with the way data is organized. In a relational database, the data is organized into tables while in a non-relational database, the data is not organized into tables.
Here are the key differences in detail.
1. Data structures and models
Relational databases are table-based, while non relational databases are not. A relational database stores and organizes structured data and uses a relational model to run analytics. On the other hand, a non-relational database stores unstructured data and thus cannot use a predefined model to analyze the data.
Relational databases work better with structured data because it is easier to analyze and search. This data is stored in columns and rows in a table. Non-relational databases analyze less structured data sets.
There are two ways of scaling a database: horizontally, by adding servers, or vertically by expanding the existing server’s storage capacity. However, the database you use will determine how you scale it.
You can scale a relational database vertically, which means you will enhance the storage capacity of the existing server. Unlike relational databases, you can only scale a non-relational database horizontally. This involves adding more servers to increase storage capacity. A non-relational database is more distributed because you can increase the number of servers. Sharding is a popular way of scaling databases by splitting a database into several components that run on multiple servers.
Databases query data differently between non relational and relational databases. Relational databases can process more advanced queries than non-relational databases. However, how you run queries and your data set will depend on the database type.
Relational databases are ideal for complex queries because they use a standard interface and a fixed syntax for SQL queries. This makes it straightforward to run queries on a relational database. However, a non-relational database doesn’t have a standard query interface, and the syntax for unstructured query language varies.
4. Data storage
Relational databases store information in rows and columns, similar to tables, which is more straightforward. However, non-relational databases provide more complex methods of storing data.
While a relational database uses a table model to store data, a non-relational database uses multiple storage models, including document databases, graph database, key-value models, and column storage.
If a relational database has performance issues, you will need more RAM, CPU power, and storage to enhance your server. However, a larger server can be costly and may not be efficient in managing data.
On the other hand, you only need to add more servers to enhance the performance of your non-relational database. You can do this easily without affecting the performance of the existing server. This makes NoSQL databases faster than SQL.
Relational databases use SQL, the most documented, recognizable, and widely-used language. But even though it is safe, versatile, and suitable for complex queries, it restricts the user to a predefined tabular schema.
Non-relational databases on the other hand use dynamic schemas to represent data in various structures. This enhances flexibility when adding new fields or attributes and a wide range of syntax across databases. However, NoSQL languages don’t have a standard interface, making executing complex queries challenging.
The table below summarizes these key differences between relational databases and non relational databases.
|Handles structured data
|Handles unstructured data
|Ideal for complex queries
|Not ideal for complex queries
|Can be slower
Use cases for relational vs. non-relational DBs
This is perhaps the most important part of this article? Depending on the type of data you are working with and what you want to achieve, you’ll have to go for either relational or non-relational databases for different use cases.
Here are the most common use cases for relational vs non-relational databases.
Use cases for relational databases
1. Data management and administration
Relational databases use SQL as the standard language and thus are widely used by companies for data management. Almost every business must record employees, clients, suppliers, and other confidential company information. Database administrators find it convenient to store, update, and organize this data with relational databases.
2. Data analysis
Data scientists, analysts, and engineers use relational databases for data analytics and software development. This again is attributed to the use of the standard SQL language that is easy to learn and run complex queries. Furthermore, it allows users to access massive data without downloading it.
As relational databases store data in tables using columns and rows, accountants find them easier to explore, manage, query, and update data. They can quickly retrieve data that is vital for making business-related decisions.
Use cases for non-relational databases
1. Content management
Content management involves collecting, storing, retrieving, and publishing information in multiple formats, including video, text, audio, and images. Non-relational databases provide the perfect option for content management functions.
2. Internet of Things
IoT devices use sensors to collect and share data over the internet and other communication networks. With the number of connected IoT devices rising, IoT services need highly scalable databases with a more flexible schema to store and analyze this data. Non-relational databases provide the perfect option.
3. Real-time big data analytics
Relational databases cannot handle big data because it is obviously too large. NoSQL databases can best handle such because they use stream processing to store and retrieve data.
To sum it up, relational databases are well-suited for storing data that can be easily organized into rows and columns, such as customer addresses or product inventories. Non-relational databases, on the other hand, are better suited for storing data that does not fit neatly into rows and columns, such as unstructured text or images. In other words if you need to be able to store lots of info without having to change any code or set up any new infrastructure, a non-relational database might be a good fit for you. But if queries are a key requirement in your application, you'll likely get better results from a relational database.
Because of their different strengths and weaknesses as we’ve just seen, organizations must always be sure that they are making the right decision of when to use relational and non relational databases. Start by internalizing the key differences here and relate them to your needs, then analyze the data you’re working with to ultimately determine the most appropriate option.
What is the key difference between relational and non-relational databases?**
The key difference between relational and non-relational databases is the way in which data is organized. Relational databases use a tabular format, with each row representing a record and each column representing a field. In contrast, non-relational databases store data in a document-based format, with each document representing a record.
When to use a relational and non-relational database?
If you need to store data in a structured way or if you need to be able to run complex queries and reports, then a relational database is the right choice. If you just need to store data in a simple way or if you don't need to run complex queries and reports, then a non-relational database will suffice. In other words relational databases are better suited for storing data that can be easily divided into distinct fields, such as customer information or inventory data. Non-relational databases are better suited for storing data that is more free-form, such as text documents or images. As a result, the best type of database for a given application depends on the nature of the data being stored
Which is better: relational or non-relational database?
It’s certainly difficult to pick one that is better than the other between relational and non relational databases. In fact, the best way to put it is that the best type of database for your use case will depend on the specific goals you want to accomplish. Relational databases are generally easy to use, due to their intuitive structure and the fact that they conform to the standard SQL language. However, if you need a database that can handle large amounts of data and scale quickly, then a non-relational database is the better option.
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