RDBMS vs MongoDB

RDBMS vs MongoDB
Written by Nilima PaulDecember 7, 2021
13 min read
Nilima Paul

Technology Security Analyst

In this article, we will discuss RDBMS vs MongoDB how they stack up against each other.

what is RDBMS?

A relational database is a type of database that stores and provides access to data points that are related to one another. Relational databases are based on the relational model, an intuitive, straightforward way of representing data in tables. In a relational database, each row in the table is a record with a unique ID called the key. The columns of the table hold attributes of the data, and each record usually has a value for each attribute, making it easy to establish the relationships among data points.


what is MongoDB?

MongoDB is an open-source NoSQL database management program. NoSQL is used as an alternative to traditional relational databases. NoSQL databases are quite useful for working with large sets of distributed data. MongoDB is a tool that can manage document-oriented information, store or retrieve information.

MongoDB supports various forms of data. It is one of the many nonrelational database technologies that arose in the mid-2000s under the NoSQL banner -- normally, for use in big data applications and other processing jobs involving data that doesn't fit well in a rigid relational model. Instead of using tables and rows as in relational databases, the MongoDB architecture is made up of collections and documents.

Organizations can use Mongo DB for its ad-hoc queries, indexing, load balancing, aggregation, server-side JavaScript execution and, other features.


Difference Between RDBMS vs MongoDB

Essentially, RDBMS represents social data set administration framework; it is utilized to store the information dependent on the social model. Regularly with RDBMS, we can make an information base, we can refresh the data set just as erase the data set according to client necessity. In another word, we can say that we can cooperate distinctively with the various information bases according to client necessities. Then again, MongoDB is likewise used to store the information and it is an open-source report arranged data set. It is likewise called a NoSQL information base. NoSQL implies we can store information in records design without lines and segments. In this point, we will find out with regards to RDBMS versus MongoDB.

Key Differences

First, try to understand RDBMS.

The relational database management system (RDBMS)is a typical kind of data set that stores information in tables, so it very well may be utilized corresponding to other put away datasets. Most data sets utilized by organizations these days are social information bases, rather than a level record or progressive data set. Most of the current IT systems and applications depend on a social DBMS.

Social informational indexes have the muscle to manage tremendous quantities of data and complex requests. Various tables are standard use for current data bases. The data is every now and again set aside in various tables, similarly called 'relations'. These tables are apportioned into lines, similarly called records and segments (fields). There can be a large number of segments in an informational collection. Segments comprise of one unequivocal data type, like city or cost.

Presently we should consider the significant elements of RDBMS to be follows.

Starting Setup: This is the very initial stage of selecting RDBMS. The setting of DBMS, enhancing it for ideal tasks, and future-sealing it for development requires sufficient adaptability for incorporation into the momentum information foundation. Synchronization with different stages is likewise fundamental for a continuous work process.

Security of Data: Each and every type of database provides different types of security methods such as encryption; access rights as well as we can protect the stored data. Data security is an important key factor when we select any database and most of the time we can consider authorization and authentication of users.

Data Model: RDBMS provides the different types of models to the user that means sometimes we need to work on the unstructured data then we can use RDBMS that means RDBMS provides the hybrid model for that. Finally, we can say as per user requirements we can use a data model.

Data reliability and accuracy: RDBMS supports the or we can say that RDBMS works as per the ACID properties, that means RDBMS provides the data consistency, reliability of data, atomicity of data, etc.

Object Model: Basically MongoDB supports the object model and we can use nested means for multiple levels as per our requirement. Object models are present everywhere and we can use them in any structure as well as we can provide the indexing to the object at any level as per user requirement.

Optional Indexes: Indexes accelerate the inquiries fundamentally yet they likewise delay down composes. Optional files are a five-star development in MongoDB. This makes it simple to file any property of an article put away in MongoDB regardless of whether it is settled. This makes it truly simple to question from the information base dependent on these auxiliary lists.

Replication and high accessibility: MongoDB upholds a “solitary expert” model. This implies you have an expert hub and various slave hubs. On the off chance that the expert goes down, one of the slaves is chosen as an expert. This cycle happens naturally however it typically requires some investment, before the 3.2 delivery, 10-40 seconds were taken yet after the arrival of MongoDB 3.2, and later, disappointments are recognized quicker and another pioneer chose in less than 2-10 seconds.

Local Aggregation: MongoDB has an inherent Aggregation system to run an ETL (Extract, change, and burden) pipeline to change the information put away in the data set. This is incredible for little to medium positions however as your information handling needs become more muddled the conglomeration structure becomes hard to troubleshoot.

Pattern or schema-less Models: MongoDB, permits you to not uphold any diagram on your reports. While this was the default in earlier forms, in the fresher adaptation you have the choice to authorize a blueprint for your records. Each record in MongoDB can have an alternate design and it is dependent upon your application to decipher the information. While this isn’t pertinent to most applications, now and again the additional adaptability is significant. Building fewer models imply that reports in a similar assortment don’t have to have a similar arrangement of fields or design, and normal fields in an assortment’s archives might hold various kinds of information.

Comparison table

Now let’s see the comparison table between RDBMS and MongoDB as follows.


  • The RDBMS used a Table structure to store the data.
  • ln RDBMS we used rows and columns.
  • RDBMS uses the schema structure.
  • In RDBMS there is no index.
  • It provides high-level security to the database.
  • In RDBMS we can scale the database vertically.
  • In RDBMS we follow the ACID properties.
  • The performance of RDBMS is slow for large amounts of data.
  • We can use SQL language in RDBMS


  • MongoDB used doc structure to store the data.
  • In MongoDB, we used docs for NoSQL.
  • MongoDB has schema-less databases.
  • In MongoDB, each field has been indexed.
  • It also provides security.
  • In MongoDB, we can scale the database horizontally.
  • In MongoDB, we follow the CAP theorem.
  • The performance of MongoDB is fast for a large amount of data.
  • In MongoDB, we can use the BSON query language.

Conclusion – RDBMS vs MongoDB

We trust from this article you dive deeper into RDBMS versus MongoDB. From the above article, we have taken in the fundamental idea of RDBMS and MongoDB. furthermore we additionally see the diverse key elements of RDBMS and MongoDB. From this article, we figured out how and when we utilize the RDBMS and MongoDB.

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Nilima Paul
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