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MongoDB: The Database for Big Data Processing

Last updated on Mar 20,2024 18.4K Views

MongoDB is a document database that provides high performance, high availability, and easy scalability. It is a cross-platform document-oriented database system classified as a NoSQL database, that bridges the gap between key-value and traditional RDBMS systems. MongoDB is a relatively new contender in the data storage circle compared to giant like Oracle and IBM DB2, but it has drawn huge attention with its distributed key value store, MapReduce calculation capability and document oriented NoSQL features. Because of its features, MongoDB is The database for Big Data processing. Get a further understanding from the Big Data Certification.

MongoDB, the open-source NoSQL database, was recently named “Database Management System of the Year” by DB-Engines with a good reason, as NoSQL databases are generally better-suited for processing Big Data than RDBMS.

The main idea behind MongoDB is to preserve as much functionality as possible while allowing horizontal scale and at the same time, to make developer’s life a little more elegant and easier. MongoDB is well suited in situations like scaling out, Caching and areas where high volume traffic plays an important role.

Why MongoDB is so popular?

MongoDB is a NoSQL product and is getting enormously popular in the developer community. This is because MongoDB blends seamlessly with programming languages like JavaScript, Ruby and Python; this seamless blending conveys high coding velocity. This feature along with its simplicity, has made MongoDB very popular in a short span of time.

How is MongoDB better than traditional RDBMS?

MongoDB addresses the “Variety” aspect of Big Data. It deals with the ways to represent different data types efficiently with colossal read/write scalability and huge availability of transactional systems in real time. The prevailing RDBMS are inadequate to address this need with their schema inflexibility and non-existent scale-out solutions at a low cost. Therefore, Hadoop and NoSQL are complementary in nature and do not compete at all.

  • Flexibility – MongoDB stores data in ‘Json’ documents, where it provides a rich data model that flawlessly maps to native programming language types. And the dynamic schema makes it easier to evolve the data model than with a system with enforced schemas such as a RDBMS.
  • Power – MongoDB has lots of features like secondary indexes, dynamic queries, sorting, rich updates, upserts and easy aggregation that are available in a traditional RDBMS. This gives you functionalities similar to RDBMS and also provides added advantage of flexibility and scaling capability.
  • Speed – By keeping related data together in documents, queries can be much faster than in a relational database, where related data is separated into multiple tables and then needs to be joined later.

The above feature makes MongoDB a better option than traditional RDBMS and the preferred database for processing Big Data. You can get a better understanding with the Azure Data Engineering Course in Australia.

  • Schema-less – This is perfect for flexible data model altering. In MongoDB, It is easy to declare, extend and alter extra fields to the data model, and optional nulled fields.
 Using RDBMS databases one must run scripts primarily in order to update the model. In this case it can be done through coding and no scripting is needed.
  • Clear structure of a single object – The structure of the model is in ‘Json’ and the structure is clear instead of deriving it from a table structure.
  • No SQL or hibernate queries – The good thing about MongoDB is that the operations are not complex to use (without SQL) and are key / value based.
 Easy expression language operators like ‘$gt’, ‘$lt’  can be used and practise of indexes & cursors is possible.
  • Tuning – The level of consistency can be chosen depending on the value of the data.
  • Effortlessness scale-out – Scale reads using replica sets and writes using sharding (auto balancing). Just start up another machine and you are good to go. Here, adding more machines distributes your work.
  • Scaling – MongoDB also makes it easy to scale out your database. Auto-sharding lets you to scale your cluster linearly by adding more machines, making it possible to increase capacity without any downtime.
  • Conversion or mapping of application objects to database objects are not needed.
  • Fast Access – Utilizes internal memory for storing working set, allowing faster access to data.
  • Ease of use – MongoDB concentrates on being easy to install, configure, maintain and use. For this, MongoDB provides a couple of configuration options and automatically tries to do the correct thing. This allows the user to work right away instead of spending a lot of time in fine tuning obscure database configurations. You can get a better understanding with the Azure Data Engineer Certification Course.

How popular is MongoDB?

  • Back in October, MongoDB, provider of the MongoDB NoSQL database and formerly known as 10gen, landed $150 million in new financing, the largest round ever for a database company. The deal values the New York-based start-up at $1.2 billion and is revealing of how seriously the investment community views MongoDB.
  • “The ability to store and query both unstructured and structured data with performance at scale makes MongoDB an important addition to our strategic investment portfolio,” said Robert Ames, Vice President of Information and Communication Technologies at IQT. He also added, “MongoDB is built to leverage Big Data and holds great promise for the development of new storage and processing capabilities.”
  • MongoDB has amassed more than 900 customers, including 27 Fortune 100 companies. Significant customers include CARFAX, Cisco, eBay, eHarmony, MetLife, and Telefonica
  • More than 6.5 million downloads of MongoDB

Got a question for us? Mention them in the comments section and we will get back to you or join MongoDB certification Course  or SQL Course today. 

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MongoDB: The Database for Big Data Processing