MongoDB (NoSQL) vs. SQL databases
Choosing the right database – SQL or NoSQL – is not just a matter of taste, but plays a decisive role in the performance and scalability of your applications. Due to a rapidly changing digital landscape, developers are faced with the challenge of choosing between traditional SQL databases and NoSQL solutions such as MongoDB. In this article, we take a detailed look at MongoDB in comparison to conventional SQL databases. It will help you to decide on the optimum database solution for your individual application requirements.
Different databases for different data types
SQL and NonSQL databases differ mainly in the processing of data types.
"SQL databases are well suited for processing structured data, NonSQL databases are better for unstructured data. "
What is structured data?
Classic tables are probably the best example of structured data. Each cell represents a specific object or entity and each column represents an attribute or property of this entity. A fixed schema defines the structure of the table, including individual data types and restrictions for each column.
Structured data can be found in customer databases, for example: each row represents a customer, while the individual columns contain attributes such as name, address, telephone number and e-mail address. Transaction databases also contain clearly structured data, as each row represents a transaction and columns describe the date, amount or purpose, for example.
Structured data is characteristic of use cases that require a clear data organization and structure. They are widely used in many industries and form the backbone of finance (transactions), retail (inventory lists), healthcare (patient data) and logistics (storage locations and stocks).
What is unstructured data?
Unstructured data is data that is not organized in a fixed schema. These include text documents, videos and images. There is also semi-structured data, which has a certain structure but does not follow a strict pattern. JSON or XML data are well-known examples of this.
In today’s digital world, unstructured and semi-structured data is widespread. Social media platforms consist largely of individual text messages, images and videos. E-commerce websites store product descriptions and reviews that only partially follow a real scheme, even if they may use a certain design template from the user’s point of view or are arranged in a certain way.
Traditional SQL databases are not ideal for processing such data, as they have to be organized in tables with fixed schemas in order to work efficiently. This means that it can be difficult to store, query or even analyze this unstructured or semi-structured data. This is where NoSQL databases such as MongoDB come into play.
Relational (SQL) databases for structured data
As relational databases, SQL databases have long formed the foundation of numerous company applications. MySQL, PostgreSQL, MariaDB, Microsoft SQL Server and Oracle Database are certainly still the best-known and most successful relational database management systems.
"What the usual SQL databases have in common is that they are well suited to processing structured data."
They follow fixed schemas and often require complex joins in order to create links between individual data records. In general, the use of classic SQL databases has a number of advantages for their users:
Structured data modeling
As the name “Structured Query Language” suggests, SQL databases are well suited for applications that require structured data with clear relationships between the individual entities. They enable the unambiguous definition of tables with fixed schemas and support complex relationships through referential integrity and joins.
Transaction security
SQL databases provide transaction support that ensures that changes to the databases are either fully executed or fully undone to ensure data consistency.
ACID principle
SQL databases implement the ACID principle (Atomicity, Consistency, Isolation, Durability), which means that they offer strict guarantees for data consistency, isolation and durability, especially in demanding transaction environments.
Sophisticated technologies
SQL databases such as MySQL, PostgreSQL and Microsoft SQL Server have been established on the market for many years and are supported by a large community as well as numerous tools and frameworks.
Nevertheless, SQL databases are reaching their limits, especially in today’s world. This is because many use cases require the processing of unstructured or semi-structured data.
How does MongoDB bring structure to chaos?
"NoSQL databases such as MongoDB offer more flexible data modeling and are better suited to handling unstructured data such as text, photos and images."
Since Web 2.0, i.e. since the time when users actively determined the content on the Internet, unstructured data has become increasingly ubiquitous. Ever since this data became of interest to companies and organizations, there has been a need to process unstructured data.
MongoDB is a NoSQL solution that meets this need:
Flexible data modeling
MongoDB enables the storage of unstructured or semi-structured data in the form of documents, which offers a high degree of flexibility in data modeling and makes it easier to add or remove individual fields without having to change the entire schema.
Scalability
MongoDB is horizontally scalable, which means that it is easier to increase the capacity of the database by adding more servers. This makes MongoDB the ideal software for applications with large data volumes and high scalability requirements.
Performance for read-heavy applications
MongoDB often offers better performance for read-intensive applications. Especially when it benefits from more flexible data modeling and the ability to save data in document formats.
Simple replication & failover
MongoDB supports simple replication and automatic failover, which improves reliability and ensures the permanent availability of your database.
Which database solution is right for me?
Conventional SQL solutions are a consideration for companies with very stable data schemas and no special requirements for storing unstructured data.
Choose MongoDB to meet all your data requirements and processing needs with ease. MongoDB offers unmatched data availability, exceptional schema design flexibility and lightning-fast scalability to drive your business forward.
Say yes to big data and the fulfillment of all your data requirements
With MongoDB and Polygran at your side, you are ready to tackle big data and demanding analytics requirements with ease.
Let our team of experts advise and support you. Our many years of experience with database solutions such as MongoDB ensure ideal conceptualization and smooth implementation in your company.
Use your competitive advantage with MongoDB as an innovative NoSQL solution in your company.
Book an appointment today, free of charge and without obligation, and let us give you expert advice.
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