Introduction to MongoDB Use Cases

Basically, MongoDB is used for big data or in another word we can say that is the most popular use case for big data storage. Basically, MongoDB use cases are used to handle a large amount of data. That means if we need fast access to the data, sometimes it is complex to process the traditional data. At that time, we can use MongoDB use cases as per our requirements. On the other hand, big data is helping to create new applications as per business requirements. It also helps to improve customer satisfaction.

Different MongoDB Use Cases

Given below are the different use cases of MongoDB:

1. Big Data

One of MongoDB’s most conspicuous conceivable use cases is, as referenced above, Big Data. The actual term alludes to mass volumes of excessively enormous, quick, and computationally complex information to be prepared by customary, order-based information handling programming.

Large Data has become an expanding marvel over the previous decade, or somewhere in the vicinity as distributed computing, applications, and online administrations have gotten more universal, close by expanding handling force and capacity. The entirety of this aggregated information has gigantic investigation potential in a wide scope of fields, including finance, meteorology, avionics, online retail, hereditary examination, segment studies, and that’s only the tip of the iceberg, which is the place where MongoDB comes in.

Basically, MongoDB is NoSQL, and non-relational structures are used for the following four important points as follows:

Volume: Alludes to the amount of the created and put away information. By and large, volume is the main metric that determines a dataset’s quality and regardless of whether that dataset can be characterized as Big Data in any case.
Variety: Assortment alludes to the kind structure of the dataset, which can incorporate content, sound, video, pictures, search inquiries, everyday travel courses, and the sky’s the limit from there. Information can be gathered across mediums by a theme to give different experiences and to finish data holes that would show up with just a couple of information types. This is known as “information combination.”
Velocity: Speed alludes to the speed at which information is produced and examined, which in Big Data’s case is frequently constant and progressively. For instance, even a 10 second slack in following the way of a torrent across interlocking streams and foundation can be deadly if the wave is going at 80kmph. Gigantic volumes of information should be changed over into on-the-ground clearing orders progressively, not 60 seconds prior.
Veracity: Veracity alludes to the nature of the information, for example, where it was produced, its degree of detail or vagueness, how well it very well may be reproduced, etc. Singular information quality can differ uncontrollably with Big Data because of the sheer number of sources, which is the reason volume, as referenced above, is regularly viewed as the fundamental pointer of value to decide the most exact outcomes.

2. Customer Analytics

One of the most important strengths of MongoDB is Big Data analytics. We can scale the analytics as per our requirements. The IoT allows us to increase the business as well as how we can reach customer satisfaction. MongoDB helps us to compile and analyze the customer interest, and according to customer interest, we provide different choices to the customers.

3. Product Data Management

This is another use case of MongoDB, and basically, it is used for the online store and e-commerce solutions. Using the MongoDB component, we can easily manage the product catalog; we can also manage the customer orders; and the store’s inventory and shopping carts.

4. Content Management

This is another use case of MongoDB; by using the MongoDB feature, we can store the content that we are writing for building a website, particularly considering the images, text, video, etc. MongoDB also allows us to store the user comments.

5. Mobile Development and Scaling

Basically, the mobile application is of a dynamic nature, which means we can scale as per the market situation.

6. Real-Time Data Integration

Sometimes we need to integrate real-time data with a system of software. So MongoDB provides such a platform to the user, which means that MongoDB provides high efficiency to the complex system.

7. Objective and Data Requirement

Sometimes unwanted output comes so that we can use MongoDB for that particular scenario. So as per requirement, we can build the framework same as mobile development.

Examples of MongoDB Use Cases

Different examples are mentioned below:

Example #1

MongoDB use case for a web application.

By using the MongoDB use case, we can scale the web application. That means sometimes we need to store a large amount of the data so that we can easily scale the database as per requirement. MongoDB provides the consistency of data over scalability.

Example #2

It is used to store the Log Events.

Basically, when we run the web-based application at that time, HTTP requests may be generated, which means the web server generates the log message, and that message contains the different types of information such as time, when it is generated, etc. But other side log files are stored in the same location of the application, and it is difficult to handle them when we have a distributed network. So that’s why we can use MongoDB to store these log messages, and it handles like operational intelligence.

It is used for e-commerce applications.
Sometimes we need to work with machine-generated data. At that time, MongoDB provided a high volume to data feeds.
Similarly, we can use it for the stock market and social media.
For example, eBay, ADHAR, MetLife, etc., these companies used MongoDB.

Conclusion

From the above article, we have seen the basic syntax of the MongoDB use case, and we also saw different examples of MongoDB use cases. From this article, we saw how and when we use the MongoDB use case.

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