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. Show
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
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
Pros and Cons of Relational DatabasesBoth relational and non relational databases definitely have their respective strengths and weaknesses. Here are the pros and cons for each: Pros of relational databases
Cons of relational databases
Pros of non-relational databases
Cons of non-relational databases
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. 2. Scaling 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. 3. Querying 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. 5. Performance 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. 6. Language 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.
Use cases for relational vs. non-relational DBsThis 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 databases1. 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. 3. Accounting 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 databases1. 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. FAQ SectionWhat 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. You May Also Like:Explore more concepts and practical applications of graph databases in this blog: What is a graph database and what are its use cases - Definition, examples & trends |