Is MongoDB for Big Data?

MongoDB is a document database that provides high performance, high availability, and easy scalability. Because of its features, MongoDB is The database for Big Data processing.

What is the difference between MongoDB and Hadoop?

MongoDB is a NoSQL database, whereas Hadoop is a framework for storing & processing Big Data in a distributed environment. MongoDB is a document oriented NoSQL database. MongoDB stores data in flexible JSON like document format. You can easily map the documents to your applications.

Is MongoDB like Hadoop?

A primary difference between MongoDB and Hadoop is that MongoDB is actually a database, while Hadoop is a collection of different software components that create a data processing framework. Both of them are having some advantages which make them unique but at the same time, both have some disadvantages.

Why MongoDB is better for big data?

Platform Strengths for Big Data Use Cases When compared to Hadoop, MongoDB’s greatest strength is that it is a more robust solution, capable of far more flexibility than Hadoop, including potential replacement of existing RDBMS. Additionally, MongoDB also is inherently better at handling real-time data analytics.

Why MongoDB is more preferable for big data?

As we have seen earlier, MongoDB has a document-based structure, which is a more natural way to store unstructured data. Its flexible schema accepts data in any form and volume—so you don’t have to worry about storage as the amount of data increases.

Should I learn Hadoop or MongoDB?

Hadoop is best for Large-Scale processing application whereas MongoDB is best for Real-Time Mining of data and Processing. MongoDB belongs to the NoSQL family whereas Hadoop use of SQL for processing of data. Hadoop is a Framework that can have much software for processing whereas MongoDB is a Database type.