Difference Between MongoDB And RDBMS
Comparing MongoDB and RDBMS
Difference Between MongoDB And RDBMS
MongoDB is a NoSQL database that stores data in a flexible, document-based format, while RDBMS (Relational Database Management System) stores data in tables with predefined schemas. MongoDB is schema-less, allowing for more fluid and organic data structures, while RDBMS requires a structured schema design with predefined relationships between tables. MongoDB is designed for scalability and handling large volumes of unstructured data, making it well-suited for modern applications with dynamic data requirements, whereas RDBMS is better suited for structured data and complex queries that require joins and transactions. Overall, MongoDB is a good choice for applications that require flexibility, scalability, and high-performance, while RDBMS is preferred for traditional applications with structured data needs.
To Download Our Brochure: https://www.justacademy.co/download-brochure-for-free
Message us for more information: +91 9987184296
1 - Data Model:
MongoDB: It uses a flexible document based data model, storing data in JSON like documents with dynamic schemas, allowing for more fluid and versatile data structures.
RDBMS: Relational Database Management Systems (RDBMS) use a tabular structure with predefined schemas, enforcing data integrity through relationships between tables.
2) Scalability:
MongoDB: Designed for horizontal scalability through sharding, allowing for distributed storage and improved performance on high traffic applications.
RDBMS: Typically scaled vertically by upgrading hardware, which can be more challenging and costly as the database grows.
3) Query Language:
MongoDB: Uses a query language that is based on JavaScript like syntax, making it easier for developers who are familiar with JavaScript.
RDBMS: Utilizes SQL (Structured Query Language), a standardized language for managing relational databases.
4) Data Integrity:
MongoDB: Provides flexible schemas which can potentially lead to reduced data integrity if not carefully managed.
RDBMS: Ensures data integrity through defined relationships and constraints, maintaining ACID (Atomicity, Consistency, Isolation, Durability) properties.
5) Normalization vs. Denormalization:
MongoDB: Supports denormalized data models, allowing for embedding related data within a single document for improved read performance but may lead to data redundancy.
RDBMS: Typically follows normalization principles to reduce redundancy and maintain data integrity, but can result in more complex queries due to the need for joins.
6) Complex Transactions:
MongoDB: Prioritizes speed and scalability over complex transactional capabilities, which may be a limitation for applications requiring complex transaction support.
RDBMS: Offers robust support for complex transactions with features like rollback, commit, and locking mechanisms, ensuring data consistency in multi step processes.
7) Indexing:
MongoDB: Allows for the creation of various types of indexes to improve query performance, including single field, compound, geospatial, and text indexes.
RDBMS: Provides indexing capabilities to optimize query execution plans and enforce data integrity constraints like primary keys and unique constraints.
8) Replication and High Availability:
MongoDB: Supports automatic replication through replica sets for high availability and fault tolerance, allowing for failover and data redundancy.
RDBMS: Offers similar capabilities through master slave replication setups, though it may require more manual configuration compared to MongoDB's built in replication features.
9) Schema Flexibility:
MongoDB: Allows for schema evolution and adaptation over time, making it easier to accommodate changing data requirements without disrupting existing applications.
RDBMS: Requires schema changes to be carefully planned and executed, especially in environments where data integrity is critical.
10) Development Speed:
MongoDB: Offers faster development cycles due to its flexible data model and schema less nature, allowing developers to iterate and evolve applications quickly.
RDBMS: Requires more upfront planning and design due to rigid schemas, which can slow down development cycles, especially when dealing with complex data structures.
11) Data Validation:
MongoDB: Supports flexible data validation rules that can be enforced at the document level, providing more granular control over data quality.
RDBMS: Relies on constraints and triggers for data validation, ensuring that data meets predefined rules and requirements.
12) Document Size Limits:
MongoDB: Allows for larger document sizes compared to traditional RDBMS, which can be beneficial for storing complex and nested data structures within a single document.
RDBMS: Enforces limitations on row size and datatype constraints, which can impact the storage and retrieval of large data objects.
13) Real time Analytics:
MongoDB: Well suited for real time analytics and processing of large volumes of data, thanks to its distributed architecture and support for parallel queries.
RDBMS: May struggle with handling real time analytics due to limitations in scalability and performance, particularly when dealing with massive datasets.
14) Security Features:
MongoDB: Provides robust security features such as authentication, authorization, encryption, and auditing capabilities to protect data at rest and in transit.
RDBMS: Offers similar security features but may require additional configuration and setup to achieve the same level of protection as MongoDB out of the box.
15) Community and Ecosystem:
MongoDB: Has a vibrant community and ecosystem with extensive documentation, online resources, and third party tools that support developers in building and maintaining MongoDB applications.
RDBMS: Benefits from a long established ecosystem with a wide range of tools, frameworks, and libraries tailored for working with relational databases, offering strong community support and resources for developers.
By covering these points and highlighting the key differences between MongoDB and RDBMS, the training program can provide students with a comprehensive understanding of both database technologies, their strengths, weaknesses, and best practices for designing and developing applications based on their use cases and requirements.
Browse our course links : https://www.justacademy.co/all-courses
To Join our FREE DEMO Session: Click Here
Contact Us for more info:
- Message us on Whatsapp: +91 9987184296
- Email id: info@justacademy.co
Difference Between Abstract Class And Abstract Method In Java
Core Java Interview Questions For 6 Years Experience
