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The Battle of the NoSQL Databases – Comparing MongoDB & MSSQL's NoSQL Functions
It is a well-known fact that MSSQL databases have ruled the world of data technologies and have been the primary source of data storage for over four decades. Generally, the MSSQL database is used mainly for accessing relational databases. MSSQL ruled the segment, but as the Web development market paced up, there came a shift towards the usage of open source databases like MySQL, PostgreSQL, etc. But MSSQL was still the first choice. Soon enough, data started growing exponentially, and scalability became a major issue; at that time, NoSQL rolled in to save the day. NoSQL (derived from “Not only SQL”) is the name given to a type of database that can host non-relational, unstructured data. It means data in a NoSQL database does not necessarily exist in fixed-length columns and rows like it does in a relational database and can be highly unstructured. This type of database comes with built-in high-availability and fast performance features. Applications using NoSQL databases are less concerned about entity relationship, transactional consistency, or data duplication.
MongoDB is a NoSQL database that made a deep proliferation over the last decade or so, fueled by the explosive growth of the web and mobile applications running in the cloud. This new breed of internet-connected applications demands fast, fault-tolerant and scalable schema-less data storage which NoSQL databases can offer. MongoDB uses JSON to store data like documents that can vary in structure offerings, a dynamic, flexible schema. MongoDB designed for high availability and scalability with auto-sharding. MongoDB is one of the popular open-source databases that arise under the NoSQL database, which is used for high volume data storage. In MongoDB, the rows known as documents don’t require to have a schema defined beforehand. The fields will be created on the fly. The data model available within MongoDB allows hierarchical relationships representation, to store arrays, and other more complex structures more efficiently.
High-Level Differences Between MongoDB & MSSQL
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MongoDB (NoSQL Database ) |
MSSQL Database |
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MongoDB database is a non-relational or distributed database. |
MSSQL database is a relational database (RDBMS). |
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Relatively young technology. |
An old and mature technology. |
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MongoDB database based on documents, key-value pairs, graphs, or columns, and they don’t have to stick to standard schema definitions. |
MSSQL database is a table based in the form of rows & columns and must strictly adhere to standard schema definitions. They are a better option for applications that need multi-row transactions. |
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MongoDB has a dynamic schema for unstructured data. Data can be flexibly stored without having a predefined structure. |
MSSQL has a well-designed pre-defined schema for structured data. |
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MongoDB database favors denormalized schema. |
MSSQL databases favor normalized schema. |
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MongoDB is much cheaper to scale when compared to relational databases. |
MSSQL is costly to scale. |
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MongoDB database is horizontally scalable. It can be scaled by adding more servers to the infrastructure to manage a large load and lessen the heap. |
MSSQL database is vertically scalable. It can be scaled by increasing the hardware capacity (CPU, RAM, SSD, etc.) on a single server. |
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MongoDB has some limitations to fit for complex queries as there is no standard interface in MongoDB for handling queries. The queries in MongoDB are not as powerful as SQL queries. It is called UnQL, and the syntax for using the Unstructured query language will vary from syntax to syntax. |
MSSQL is suitable to fit for complex queries as SQL has a standard interface for handling queries. The syntax of SQL queries is fixed. |
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MongoDB database suits best for hierarchical data storage as it follows the key-value pair method for storing the data. |
MSSQL database does not suit well for hierarchical data storage. |
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They are classified based on the way they store data as a key-value store, document store, graph store, column store, and XML store. |
From a commercial perspective, the MSSQL database is not open-source or closed source. |
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MongoDB database is compliant with the Brewers CAP theorem (Consistency, Availability, and Partition tolerance). |
MSSQL database is compliant with ACID properties (Atomicity, Consistency, Isolation & Durability). |
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New data can be easily inserted in the MongoDB database as it does not require any prior steps. |
Adding new data in the MSSQL database requires some changes to be made, like backfilling data, altering schemas. |
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Only limited community support is available for MongoDB databases. |
MSSQL database has excellent vendor support, and community support is available. |
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You can use MongoDB for a heavy transactional purpose. To store local data transactions that need not be very durable. |
MSSQL Database best fit for high transaction-based applications. |
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MongoDB is suitable for hierarchical data storage and storing large data sets (E.g., Big Data). |
MSSQL is not suitable for hierarchical data storage. |
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MongoDB is a document-oriented database and JSON is the native data type that stores its data in JSON file objects. It creates indexes on the collection level and supports indexes on any field or subfield of the documents in a MongoDB collection. |
JSON support in MSSQL arrived in the 2016 release of the product. However, in contrast to the MongoDB database, SQL Server doesn’t include a native JSON datatype. It supports limited indexing capabilities and no native JSON indexes; just fulltext indexing. |
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In MongoDB, ‘mongoimport’ and ‘mongoexport’ command-line tools are used to import and export the documents and insert or update in a MongoDB collection. |
Some of common methods used to import and export JSON data into MSSQL database :-
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Advantages of MongoDB
Having seen the excellent features of MongoDB, now every developer should be able to understand why it is better to use a NoSQL based database for developing big data transactions applications and for implementing a scalable model. Now, it’s time to leave behind the schema definitions of MSSQL and get the advantage of using schema-less databases like MongoDB. The following are some of the vital advantages of MongoDB.
Distributed Data Platform
MongoDB assures new levels of availability and scalability, throughout geographically distributed data centers and cloud regions. With no downtime and without changing any code in an application, MongoDB scales elastically in terms of data volume and throughput. The technology gives you enough flexibility across various data centers with the right consistency.
Fast and Iterative Development
Frequent change of business requirements will not directly affect the success of any project delivery in any enterprise. A flexible data model with dynamic schema, command-line tools, and powerful GUI, helps the developers to build and evolve applications. Moreover, automated provisioning enables continuous integration and delivery for productive operations, whereas static relational schemas and complex procedures based RDBMS are now something from the past.
Flexible Data Model
MongoDB will store the data in a flexible JSON-like documents method, which allows data persistence and combining easily. The objects in the application code are mapped to the document model, due to which working with data becomes easy. The schema governance controls, complex aggregations, data access, and rich indexing functionality will not be compromised in any way. Without any downtime, one can modify the schema dynamically. This flexibility is an excellent advantage for a developer and less worry about data manipulation.
Reduced Total Cost of Ownership (TCO)
Application developers will be able to do their jobs better by using MongoDB. Costs become significantly very much lowered as MongoDB runs on commodity hardware. This technology allows an on-demand, pay-as-you-go pricing model with annual subscriptions, which comes with 24/7 global support.
Integrated Feature Set
MongoDB used in the development of a variety of real-time applications such as event-driven streaming data pipelines, analytics with data visualization, text, and geospatial search, graph processing, in-memory performance, and global replication reliably and securely. For any RDBMS to accomplish this, there require additional complex technologies, along with separate integration requirements.
Conclusion
In today’s database, MongoDB is gaining large popularity as a NoSQL database and becoming a real game-changer in the IT arena. MongoDB is an excellent choice for businesses that have rapid growth or databases with no clear schema definitions (i.e., you have a lot of unstructured data). Moreover, it has numerous benefits, including lower cost, open-source availability, and easier scalability, which makes MongoDB an appealing choice for anyone thinking about integrating with Big Data. Even though MongoDB is a young technology compared to MSSQL, however, which makes them slightly more volatile.
