Using MongoDB for Big Data Analytics – 5 Reasons Why

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Written By Berry Mathew

The essential nature of Big Data analytics to today’s corporate environment is needless to mention. When working with Big Data, the factors that usually come on top of the list are the variety of data types involved, the size of the big data involved, and the speed of access. One needs access to a range of software and tools when working with Big Data. One needs to be a technological master to become an expert on Big Data analytics. And it is in its very nature that makes it ideal for the use of MongoDB and getting a MongoDB developer certification, and below are reasons discussing why:

Ample Accessibility

MongoDB can now replicate data over numerous hosts and servers, unlike relational DBMS that can only replicate data on a single server. This is useful since data is widely available in various areas, and consumers can be serviced with much more efficiency by the server closest to them. Additionally, using MongoDB’s journaling functionality, which establishes checkpoints from which the restoration process can be referenced, the process of restoration or breakdown becomes simple.

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Great Workload Capability 

The WiredTiger storage engine, which provides an executive capacity for handling different workloads, has been set as the default storage engine in the latest MongoDB releases. This storage engine comes with a slew of benefits that make it ideal for Big Data. The engine includes features like compression and backups, as well as document-concurrency, which encourages numerous write operations. Big data implies a large number of users, and the document-level concurrency functionality will allow several users to change the database at the same time without affecting performance. MongoDB was also created in C++, which makes it ideal for memory management.

Ample External Help

The ecosystem that MongoDB offers is vast and completely matured. This vast ecosystem is filled with creators and developers, including people from all around the world. On your way to getting better at your Big Data operations, you can always avail external help at any time, whether it is to resolve your issues or learn ways to upgrade. 

Data Remains Secure and Consistent

The JSON validator tool is another MongoDB function that ensures data consistency and integrity. It is used to ensure that no invalid data enters the database. If there is a field called the phone number, for example, it will always anticipate an Integer value. For this field, the JSON validator will always ensure that a string or any other data type is not submitted for database storage. Furthermore, MongoDB has backup and restoration capabilities, allowing users to return to a chosen state in the event of a failure.

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Use Anywhere and Everywhere

MongoDB is flexible for use. You can find it on almost any cloud platform, especially the major ones like Google Cloud, Azure, and so on. When working with Big data Analytics, you will be required to work with many other tools and platforms. This is where MongoDB shines with its ample deployment options. 

MongoDB has several advantages that make it ideal to use for a diverse range of operations, including Big Data Analytics. In fact enterprises like eBay, Shutterfly, EA make use of MongoDB in their operations. And especially if you are employed in the software industry, a MongoDB developer certification might just be what you need to scale up your operations.

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